A comparison of ground truth estimation methods

  • Biancardi, Alberto M
  • Jirapatnakul, Artit C
  • Reeves, Anthony P
International journal of computer assisted radiology and surgery 2010 Journal Article, cited 17 times
Website
PURPOSE: Knowledge of the exact shape of a lesion, or ground truth (GT), is necessary for the development of diagnostic tools by means of algorithm validation, measurement metric analysis, accurate size estimation. Four methods that estimate GTs from multiple readers' documentations by considering the spatial location of voxels were compared: thresholded Probability-Map at 0.50 (TPM(0.50)) and at 0.75 (TPM(0.75)), simultaneous truth and performance level estimation (STAPLE) and truth estimate from self distances (TESD). METHODS: A subset of the publicly available Lung Image Database Consortium archive was used, selecting pulmonary nodules documented by all four radiologists. The pair-wise similarities between the estimated GTs were analyzed by computing the respective Jaccard coefficients. Then, with respect to the readers' marking volumes, the estimated volumes were ranked and the sign test of the differences between them was performed. RESULTS: (a) the rank variations among the four methods and the volume differences between STAPLE and TESD are not statistically significant, (b) TPM(0.50) estimates are statistically larger (c) TPM(0.75) estimates are statistically smaller (d) there is some spatial disagreement in the estimates as the one-sided 90% confidence intervals between TPM(0.75) and TPM(0.50), TPM(0.75) and STAPLE, TPM(0.75) and TESD, TPM(0.50) and STAPLE, TPM(0.50) and TESD, STAPLE and TESD, respectively, show: [0.67, 1.00], [0.67, 1.00], [0.77, 1.00], [0.93, 1.00], [0.85, 1.00], [0.85, 1.00]. CONCLUSIONS: The method used to estimate the GT is important: the differences highlighted that STAPLE and TESD, notwithstanding a few weaknesses, appear to be equally viable as a GT estimator, while the increased availability of computing power is decreasing the appeal afforded to TPMs. Ultimately, the choice of which GT estimation method, between the two, should be preferred depends on the specific characteristics of the marked data that is used with respect to the two elements that differentiate the method approaches: relative reliabilities of the readers and the reliability of the region boundaries.

A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom

  • Gavrielides, Marios A
  • Kinnard, Lisa M
  • Myers, Kyle J
  • Peregoy, Jennifer
  • Pritchard, William F
  • Zeng, Rongping
  • Esparza, Juan
  • Karanian, John
  • Petrick, Nicholas
Optics express 2010 Journal Article, cited 50 times
Website
A number of interrelated factors can affect the precision and accuracy of lung nodule size estimation. To quantify the effect of these factors, we have been conducting phantom CT studies using an anthropomorphic thoracic phantom containing a vasculature insert to which synthetic nodules were inserted or attached. Ten repeat scans were acquired on different multi-detector scanners, using several sets of acquisition and reconstruction protocols and various nodule characteristics (size, shape, density, location). This study design enables both bias and variance analysis for the nodule size estimation task. The resulting database is in the process of becoming publicly available as a resource to facilitate the assessment of lung nodule size estimation methodologies and to enable comparisons between different methods regarding measurement error. This resource complements public databases of clinical data and will contribute towards the development of procedures that will maximize the utility of CT imaging for lung cancer screening and tumor therapy evaluation.

Accuracy of emphysema quantification performed with reduced numbers of CT sections

  • Pilgram, Thomas K
  • Quirk, James D
  • Bierhals, Andrew J
  • Yusen, Roger D
  • Lefrak, Stephen S
  • Cooper, Joel D
  • Gierada, David S
American Journal of Roentgenology 2010 Journal Article, cited 8 times
Website

An Image Processing Tool for Efficient Feature Extraction in Computer-Aided Detection Systems

  • Soysal, Omer M
  • Chen, P
  • Schneider, Helmut
2010 Conference Proceedings, cited 3 times
Website

Automatic fissure detection in CT images based on the genetic algorithm

  • Tseng, Lin-Yu
  • Huang, Li-Chin
2010 Conference Proceedings, cited 5 times
Website
Lung cancer is one of the most frequently occurring cancer and has a very low five-year survival rate. Computer-aided diagnosis (CAD) helps reducing the burden of radiologists and improving the accuracy of abnormality detection during CT image interpretations. Owing to rapid development of the scanner technology, the volume of medical imaging data is becoming huger and huger. Automated segmentations of the target organ region are always required by the CAD systems. Although the analysis of lung fissures provides important information for treatment, it is still a challenge to extract fissures automatically based on the CT values because the appearance of lung fissures is very fuzzy and indefinite. Since the oblique fissures can be visualized more easily among other fissures on the chest CT images, they are used to check the exact localization of the lesions. In this paper, we propose a fully automatic fissure detection method based on the genetic algorithm to identify the oblique fissures. The accurate rates of identifying the oblique fissures in the right lung and the left lung are 97% and 86%, respectively when the method was tested on 87 slices.

The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans

  • Armato III, Samuel G
  • McLennan, Geoffrey
  • Bidaut, Luc
  • McNitt-Gray, Michael F
  • Meyer, Charles R
  • Reeves, Anthony P
  • Zhao, Binsheng
  • Aberle, Denise R
  • Henschke, Claudia I
  • Hoffman, Eric A
  • Kazerooni, E. A.
  • MacMahon, H.
  • Van Beeke, E. J.
  • Yankelevitz, D.
  • Biancardi, A. M.
  • Bland, P. H.
  • Brown, M. S.
  • Engelmann, R. M.
  • Laderach, G. E.
  • Max, D.
  • Pais, R. C.
  • Qing, D. P.
  • Roberts, R. Y.
  • Smith, A. R.
  • Starkey, A.
  • Batrah, P.
  • Caligiuri, P.
  • Farooqi, A.
  • Gladish, G. W.
  • Jude, C. M.
  • Munden, R. F.
  • Petkovska, I.
  • Quint, L. E.
  • Schwartz, L. H.
  • Sundaram, B.
  • Dodd, L. E.
  • Fenimore, C.
  • Gur, D.
  • Petrick, N.
  • Freymann, J.
  • Kirby, J.
  • Hughes, B.
  • Casteele, A. V.
  • Gupte, S.
  • Sallamm, M.
  • Heath, M. D.
  • Kuhn, M. H.
  • Dharaiya, E.
  • Burns, R.
  • Fryd, D. S.
  • Salganicoff, M.
  • Anand, V.
  • Shreter, U.
  • Vastagh, S.
  • Croft, B. Y.
Medical physics 2011 Journal Article, cited 546 times
Website
PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Equating quantitative emphysema measurements on different CT image reconstructions

  • Bartel, Seth T
  • Bierhals, Andrew J
  • Pilgram, Thomas K
  • Hong, Cheng
  • Schechtman, Kenneth B
  • Conradi, Susan H
  • Gierada, David S
Medical physics 2011 Journal Article, cited 15 times
Website
PURPOSE: To mathematically model the relationship between CT measurements of emphysema obtained from images reconstructed using different section thicknesses and kernels and to evaluate the accuracy of the models for converting measurements to those of a reference reconstruction. METHODS: CT raw data from the lung cancer screening examinations of 138 heavy smokers were reconstructed at 15 different combinations of section thickness and kernel. An emphysema index was quantified as the percentage of the lung with attenuation below -950 HU (EI950). Linear, quadratic, and power functions were used to model the relationship between EI950 values obtained with a reference 1 mm, medium smooth kernel reconstruction and values from each of the other 14 reconstructions. Preferred models were selected using the corrected Akaike information criterion (AICc), coefficients of determination (R2), and residuals (conversion errors), and cross-validated by a jackknife approach using the leave-one-out method. RESULTS: The preferred models were power functions, with model R2 values ranging from 0.949 to 0.998. The errors in converting EI950 measurements from other reconstructions to the 1 mm, medium smooth kernel reconstruction in leave-one-out testing were less than 3.0 index percentage points for all reconstructions, and less than 1.0 index percentage point for five reconstructions. Conversion errors were related in part to image noise, emphysema distribution, and attenuation histogram parameters. Conversion inaccuracy related to increased kernel sharpness tended to be reduced by increased section thickness. CONCLUSIONS: Image reconstruction-related differences in quantitative emphysema measurements were successfully modeled using power functions.

Automated segmentation refinement of small lung nodules in CT scans by local shape analysis

  • Diciotti, Stefano
  • Lombardo, Simone
  • Falchini, Massimo
  • Picozzi, Giulia
  • Mascalchi, Mario
IEEE Trans Biomed Eng 2011 Journal Article, cited 68 times
Website
One of the most important problems in the segmentation of lung nodules in CT imaging arises from possible attachments occurring between nodules and other lung structures, such as vessels or pleura. In this report, we address the problem of vessels attachments by proposing an automated correction method applied to an initial rough segmentation of the lung nodule. The method is based on a local shape analysis of the initial segmentation making use of 3-D geodesic distance map representations. The correction method has the advantage that it locally refines the nodule segmentation along recognized vessel attachments only, without modifying the nodule boundary elsewhere. The method was tested using a simple initial rough segmentation, obtained by a fixed image thresholding. The validation of the complete segmentation algorithm was carried out on small lung nodules, identified in the ITALUNG screening trial and on small nodules of the lung image database consortium (LIDC) dataset. In fully automated mode, 217/256 (84.8%) lung nodules of ITALUNG and 139/157 (88.5%) individual marks of lung nodules of LIDC were correctly outlined and an excellent reproducibility was also observed. By using an additional interactive mode, based on a controlled manual interaction, 233/256 (91.0%) lung nodules of ITALUNG and 144/157 (91.7%) individual marks of lung nodules of LIDC were overall correctly segmented. The proposed correction method could also be usefully applied to any existent nodule segmentation algorithm for improving the segmentation quality of juxta-vascular nodules.

Quantitative CT assessment of emphysema and airways in relation to lung cancer risk

  • Gierada, David S
  • Guniganti, Preethi
  • Newman, Blake J
  • Dransfield, Mark T
  • Kvale, Paul A
  • Lynch, David A
  • Pilgram, Thomas K
Radiology 2011 Journal Article, cited 41 times
Website

Reduced lung-cancer mortality with low-dose computed tomographic screening

  • National
  • Lung
  • Screening
  • Trial
  • Research
  • Team
New England Journal of Medicine 2011 Journal Article, cited 4992 times
Website

The national lung screening trial: overview and study design

  • National
  • Lung
  • Screening
  • Trial
  • Research
  • Team
Radiology 2011 Journal Article, cited 760 times
Website

An Automated Method for Locating Phantom Nodules in Anthropomorphic Thoracic Phantom CT Studies

  • Peskin, Adele P
  • Dima, Alden A
  • Saiprasad, Ganesh
2011 Conference Paper, cited 1 times
Website

Evaluation of reader variability in the interpretation of follow-up CT scans at lung cancer screening

  • Singh, Satinder
  • Pinsky, Paul
  • Fineberg, Naomi S
  • Gierada, David S
  • Garg, Kavita
  • Sun, Yanhui
  • Nath, P Hrudaya
Radiology 2011 Journal Article, cited 47 times
Website

Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme

  • Zinn, Pascal O
  • Majadan, Bhanu
  • Sathyan, Pratheesh
  • Singh, Sanjay K
  • Majumder, Sadhan
  • Jolesz, Ferenc A
  • Colen, Rivka R
PLoS One 2011 Journal Article, cited 192 times
Website
BACKGROUND: Despite recent discoveries of new molecular targets and pathways, the search for an effective therapy for Glioblastoma Multiforme (GBM) continues. A newly emerged field, radiogenomics, links gene expression profiles with MRI phenotypes. MRI-FLAIR is a noninvasive diagnostic modality and was previously found to correlate with cellular invasion in GBM. Thus, our radiogenomic screen has the potential to reveal novel molecular determinants of invasion. Here, we present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM. METHODS: Based on The Cancer Genome Atlas (TCGA), discovery and validation sets with gene, microRNA, and quantitative MR-imaging data were created. Top concordant genes and microRNAs correlated with high FLAIR volumes from both sets were further characterized by Kaplan Meier survival statistics, microRNA-gene correlation analyses, and GBM molecular subtype-specific distribution. RESULTS: The top upregulated gene in both the discovery (4 fold) and validation (11 fold) sets was PERIOSTIN (POSTN). The top downregulated microRNA in both sets was miR-219, which is predicted to bind to POSTN. Kaplan Meier analysis demonstrated that above median expression of POSTN resulted in significantly decreased survival and shorter time to disease progression (P<0.001). High POSTN and low miR-219 expression were significantly associated with the mesenchymal GBM subtype (P<0.0001). CONCLUSION: Here, we propose a novel diagnostic method to screen for molecular cancer subtypes and genomic correlates of cellular invasion. Our findings also have potential therapeutic significance since successful molecular inhibition of invasion will improve therapy and patient survival in GBM.

Collaborative projects

  • Armato, S
  • McNitt-Gray, M
  • Meyer, C
  • Reeves, A
  • Clarke, L
Int J CARS 2012 Journal Article, cited 307 times
Website

Removing Mixture Noise from Medical Images Using Block Matching Filtering and Low-Rank Matrix Completion

  • Barzigar, Nafise
  • Roozgard, Aminmohammad
  • Verma, Pramode K
  • Cheng, Samuel
2012 Conference Proceedings, cited 2 times
Website

Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results

  • Gevaert, Olivier
  • Xu, Jiajing
  • Hoang, Chuong D
  • Leung, Ann N
  • Xu, Yue
  • Quon, Andrew
  • Rubin, Daniel L
  • Napel, Sandy
  • Plevritis, Sylvia K
Radiology 2012 Journal Article, cited 187 times
Website
PURPOSE: To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets. MATERIALS AND METHODS: A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available. RESULTS: There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance. CONCLUSION: This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

The Study on Data Hiding in Medical Images

  • Huang, Li-Chin
  • Tseng, Lin-Yu
  • Hwang, Min-Shiang
International Journal of Network Security 2012 Journal Article, cited 25 times
Website
Reversible data hiding plays an important role in medical image systems. Many hospitals have already applied the electronic medical information in healthcare systems. Reversible data hiding is one of the feasible methodologies to protect the individual privacy and confidential information. With application in several high quality medical devices, the detection rate of diseases and treating are improved at the early stage. Its demands havebeen rising for recognizing complicated anatomical structures in high quality images. However, most data hiding methods are still applied in 8-bit depth medical images with 255 intensity levels. This paper summarizes the existing reversible data hiding algorithms and introduces basic knowledge in medical image.

Correlation of perfusion parameters with genes related to angiogenesis regulation in glioblastoma: a feasibility study

  • Jain, R
  • Poisson, L
  • Narang, J
  • Scarpace, L
  • Rosenblum, ML
  • Rempel, S
  • Mikkelsen, T
American Journal of Neuroradiology 2012 Journal Article, cited 39 times
Website
BACKGROUND AND PURPOSE: Integration of imaging and genomic data is critical for a better understanding of gliomas, particularly considering the increasing focus on the use of imaging biomarkers for patient survival and treatment response. The purpose of this study was to correlate CBV and PS measured by using PCT with the genes regulating angiogenesis in GBM. MATERIALS AND METHODS: Eighteen patients with WHO grade IV gliomas underwent pretreatment PCT and measurement of CBV and PS values from enhancing tumor. Tumor specimens were analyzed by TCGA by using Human Gene Expression Microarrays and were interrogated for correlation between CBV and PS estimates across the genome. We used the GO biologic process pathways for angiogenesis regulation to select genes of interest. RESULTS: We observed expression levels for 92 angiogenesis-associated genes (332 probes), 19 of which had significant correlation with PS and 9 of which had significant correlation with CBV (P < .05). Proangiogenic genes such as TNFRSF1A (PS = 0.53, P = .024), HIF1A (PS = 0.62, P = .0065), KDR (CBV = 0.60, P = .0084; PS = 0.59, P = .0097), TIE1 (CBV = 0.54, P = .022; PS = 0.49, P = .039), and TIE2/TEK (CBV = 0.58, P = .012) showed a significant positive correlation; whereas antiangiogenic genes such as VASH2 (PS = -0.72, P = .00011) showed a significant inverse correlation. CONCLUSIONS: Our findings are provocative, with some of the proangiogenic genes showing a positive correlation and some of the antiangiogenic genes showing an inverse correlation with tumor perfusion parameters, suggesting a molecular basis for these imaging biomarkers; however, this should be confirmed in a larger patient population.

Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening CT

  • Jeon, Kyung Nyeo
  • Goo, Jin Mo
  • Lee, Chang Hyun
  • Lee, Youkyung
  • Choo, Ji Yung
  • Lee, Nyoung Keun
  • Shim, Mi-Suk
  • Lee, In Sun
  • Kim, Kwang Gi
  • Gierada, David S
Investigative radiology 2012 Journal Article, cited 51 times
Website

Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder

  • Mongkolwat, Pattanasak
  • Channin, David S
  • Kleper, Vladimir
  • Rubin, Daniel L
Radiographics 2012 Journal Article, cited 15 times
Website
In a routine clinical environment or clinical trial, a case report form or structured reporting template can be used to quickly generate uniform and consistent reports. Annotation and image markup (AIM), a project supported by the National Cancer Institute's cancer biomedical informatics grid, can be used to collect information for a case report form or structured reporting template. AIM is designed to store, in a single information source, (a) the description of pixel data with use of markups or graphical drawings placed on the image, (b) calculation results (which may or may not be directly related to the markups), and (c) supplemental information. To facilitate the creation of AIM annotations with data entry templates, an AIM template schema and an open-source template creation application were developed to assist clinicians, image researchers, and designers of clinical trials to quickly create a set of data collection items, thereby ultimately making image information more readily accessible.

Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration

  • Otake, Y
  • Schafer, S
  • Stayman, JW
  • Zbijewski, W
  • Kleinszig, G
  • Graumann, R
  • Khanna, AJ
  • Siewerdsen, JH
2012 Conference Proceedings, cited 8 times
Website

Malignant nodule detection on lung ct scan images with kernel rx-algorithm

  • Roozgard, Aminmohammad
  • Cheng, Samuel
  • Liu, Hong
2012 Conference Proceedings, cited 22 times
Website

External clinical validation of prone and supine CT colonography registration

  • Roth, Holger R
  • Boone, Darren J
  • Halligan, Steve
  • Hampshire, Thomas E
  • McClelland, Jamie R
  • Hu, Mingxing
  • Punwani, Shonit
  • Taylor, Stuart
  • Hawkes, David J
2012 Book Section, cited 2 times
Website

Reader variability in identifying pulmonary nodules on chest radiographs from the national lung screening trial

  • Singh, Satinder
  • Gierada, David S
  • Pinsky, Paul
  • Sanders, Colleen
  • Fineberg, Naomi
  • Sun, Yanhui
  • Lynch, David
  • Nath, Hrudaya
Journal of thoracic imaging 2012 Journal Article, cited 4 times
Website

Lungs image segmentation through weighted FCM

  • Sivakumar, S
  • Chandrasekar, C
2012 Conference Proceedings, cited 8 times
Website

Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images

  • Tan, Maxine
  • Deklerck, Rudi
  • Jansen, Bart
  • Cornelis, Jan
2012 Conference Proceedings, cited 9 times
Website
Systems for Computer-Aided Detection (CAD), specifically for lung nodule detection received increasing attention in recent years. This is in tandem with the observation that patients who are diagnosed with early stage lung cancer and who undergo curative resection have a much better prognosis. In this paper, we analyze the performance of a novel feature-deselective neuroevolution method called FD-NEAT to retain relevant features derived from CT images and evolve neural networks that perform well for combined feature selection and classification. Network performance is analyzed based on radiologists' ratings of various lung nodule characteristics defined in the LIDC database. The analysis shows that the FD-NEAT classifier relates well with the radiologists' perception in almost all the defined nodule characteristics, and shows that FD-NEAT evolves networks that are less complex than the fixed-topology ANN in terms of number of connections.

Medical image thresholding using WQPSO and maximum entropy

  • Venkatesan, Anusuya
  • Parthiban, Latha
2012 Conference Proceedings, cited 1 times
Website

Classificação Multirrótulo na Anotação Automática de Nódulo Pulmonar Solitário

  • Villani, Leonardo
  • Prati, Ronaldo Cristiano
2012 Conference Proceedings, cited 0 times

A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature

  • Zinn, Pascal O
  • Sathyan, Pratheesh
  • Mahajan, Bhanu
  • Bruyere, John
  • Hegi, Monika
  • Majumder, Sadhan
  • Colen, Rivka R
PLoS One 2012 Journal Article, cited 63 times
Website
BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.

Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases

  • Aggarwal, Preeti
  • Vig, Renu
  • Sardana, HK
Journal of Computers 2013 Journal Article, cited 5 times
Website
This paper presents a novel framework for combining well known shape, texture, size and resolution informatics descriptor of solitary pulmonary nodules (SPNs) detected using CT scan. The proposed methodology evaluates the performance of classifier in differentiating benign, malignant as well as metastasis SPNs with 246 chests CT scan of patients. Both patient-wise as well as nodule-wise available diagnostic report of 80 patients was used in differentiating the SPNs and the results were compared. For patient-wise data, generated a model with efficiency of 62.55% with labeled nodules and using semi-supervised approach, labels of rest of the unknown nodules were predicted and finally classification accuracy of 82.32% is achieved with all labeled nodules. For nodule-wise data, ground truth database of labeled nodules is expanded from a very small ground truth using content based image retrieval (CBIR) method and achieved a precision of 98%. Proposed methodology not only avoids unnecessary biopsies but also efficiently label unknown nodules using pre-diagnosed cases which can certainly help the physicians in diagnosis.

Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising

  • Agostinelli, Forest
  • Anderson, Michael R
  • Lee, Honglak
2013 Conference Proceedings, cited 118 times
Website
Stacked sparse denoising auto-encoders (SSDAs) have recently been shown to be successful at removing noise from corrupted images. However, like most denoising techniques, the SSDA is not robust to variation in noise types beyond what it has seen during training. We present the multi-column stacked sparse denoising autoencoder, a novel technique of combining multiple SSDAs into a multi-column SSDA (MC-SSDA) by combining the outputs of each SSDA. We eliminate the need to determine the type of noise, let alone its statistics, at test time. We show that good denoising performance can be achieved with a single system on a variety of different noise types, including ones not seen in the training set. Additionally, we experimentally demonstrate the efficacy of MC-SSDA denoising by achieving MNIST digit error rates on denoised images at close to that of the uncorrupted images.

CT Colonography: External Clinical Validation of an Algorithm for Computer-assisted Prone and Supine Registration

  • Boone, Darren J
  • Halligan, Steve
  • Roth, Holger R
  • Hampshire, Tom E
  • Helbren, Emma
  • Slabaugh, Greg G
  • McQuillan, Justine
  • McClelland, Jamie R
  • Hu, Mingxing
  • Punwani, Shonit
Radiology 2013 Journal Article, cited 5 times
Website
PURPOSE: To perform external validation of a computer-assisted registration algorithm for prone and supine computed tomographic (CT) colonography and to compare the results with those of an existing centerline method. MATERIALS AND METHODS: All contributing centers had institutional review board approval; participants provided informed consent. A validation sample of CT colonographic examinations of 51 patients with 68 polyps (6-55 mm) was selected from a publicly available, HIPAA compliant, anonymized archive. No patients were excluded because of poor preparation or inadequate distension. Corresponding prone and supine polyp coordinates were recorded, and endoluminal surfaces were registered automatically by using a computer algorithm. Two observers independently scored three-dimensional endoluminal polyp registration success. Results were compared with those obtained by using the normalized distance along the colonic centerline (NDACC) method. Pairwise Wilcoxon signed rank tests were used to compare gross registration error and McNemar tests were used to compare polyp conspicuity. RESULTS: Registration was possible in all 51 patients, and 136 paired polyp coordinates were generated (68 polyps) to test the algorithm. Overall mean three-dimensional polyp registration error (mean +/- standard deviation, 19.9 mm +/- 20.4) was significantly less than that for the NDACC method (mean, 27.4 mm +/- 15.1; P = .001). Accuracy was unaffected by colonic segment (P = .76) or luminal collapse (P = .066). During endoluminal review by two observers (272 matching tasks, 68 polyps, prone to supine and supine to prone coordinates), 223 (82%) polyp matches were visible (120 degrees field of view) compared with just 129 (47%) when the NDACC method was used (P < .001). By using multiplanar visualization, 48 (70%) polyps were visible after scrolling +/- 15 mm in any multiplanar axis compared with 16 (24%) for NDACC (P < .001). CONCLUSION: Computer-assisted registration is more accurate than the NDACC method for mapping the endoluminal surface and matching the location of polyps in corresponding prone and supine CT colonographic acquisitions.

Constructing 3D-Printable CAD Models of Prostates from MR Images

  • Breseman, Kelsey
  • Lee, Christopher
  • Bloch, B Nicholas
  • Jaffe, Carl
2013 Conference Proceedings, cited 1 times
Website
This paper describes the development of a procedure to generate patient-specific, three-dimensional (3D) solid models of prostates (and related anatomy) from magnetic resonance (MR) images. The 3D models are rendered in STL file format which can be physically printed or visualized on a holographic display system. An example is presented in which a 3D model is printed following this procedure.

Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers

  • Buckler, AndrewJ
  • Ouellette, M.
  • Danagoulian, J.
  • Wernsing, G.
  • Liu, TiffanyTing
  • Savig, Erica
  • Suzek, BarisE
  • Rubin, DanielL
  • Paik, David
Journal of Digital Imaging 2013 Journal Article, cited 17 times
Website

Automated lung field segmentation in CT images using mean shift clustering and geometrical features

  • Chama, Chanukya Krishna
  • Mukhopadhyay, Sudipta
  • Biswas, Prabir Kumar
  • Dhara, Ashis Kumar
  • Madaiah, Mahendra Kasuvinahally
  • Khandelwal, Niranjan
2013 Conference Proceedings, cited 8 times
Website

Results of initial low-dose computed tomographic screening for lung cancer

  • Church, T. R.
  • Black, W. C.
  • Aberle, D. R.
  • Berg, C. D.
  • Clingan, K. L.
  • Duan, F.
  • Fagerstrom, R. M.
  • Gareen, I. F.
  • Gierada, D. S.
  • Jones, G. C.
  • Mahon, I.
  • Marcus, P. M.
  • Sicks, J. D.
  • Jain, A.
  • Baum, S.
N Engl J MedThe New England journal of medicine 2013 Journal Article, cited 529 times
Website
BACKGROUND: Lung cancer is the largest contributor to mortality from cancer. The National Lung Screening Trial (NLST) showed that screening with low-dose helical computed tomography (CT) rather than with chest radiography reduced mortality from lung cancer. We describe the screening, diagnosis, and limited treatment results from the initial round of screening in the NLST to inform and improve lung-cancer-screening programs. METHODS: At 33 U.S. centers, from August 2002 through April 2004, we enrolled asymptomatic participants, 55 to 74 years of age, with a history of at least 30 pack-years of smoking. The participants were randomly assigned to undergo annual screening, with the use of either low-dose CT or chest radiography, for 3 years. Nodules or other suspicious findings were classified as positive results. This article reports findings from the initial screening examination. RESULTS: A total of 53,439 eligible participants were randomly assigned to a study group (26,715 to low-dose CT and 26,724 to chest radiography); 26,309 participants (98.5%) and 26,035 (97.4%), respectively, underwent screening. A total of 7191 participants (27.3%) in the low-dose CT group and 2387 (9.2%) in the radiography group had a positive screening result; in the respective groups, 6369 participants (90.4%) and 2176 (92.7%) had at least one follow-up diagnostic procedure, including imaging in 5717 (81.1%) and 2010 (85.6%) and surgery in 297 (4.2%) and 121 (5.2%). Lung cancer was diagnosed in 292 participants (1.1%) in the low-dose CT group versus 190 (0.7%) in the radiography group (stage 1 in 158 vs. 70 participants and stage IIB to IV in 120 vs. 112). Sensitivity and specificity were 93.8% and 73.4% for low-dose CT and 73.5% and 91.3% for chest radiography, respectively. CONCLUSIONS: The NLST initial screening results are consistent with the existing literature on screening by means of low-dose CT and chest radiography, suggesting that a reduction in mortality from lung cancer is achievable at U.S. screening centers that have staff experienced in chest CT. (Funded by the National Cancer Institute; NLST ClinicalTrials.gov number, NCT00047385.).

Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images

  • Dhara, Ashis Kumar
  • Mukhopadhyay, Sudipta
  • Alam, Naved
  • Khandelwal, Niranjan
2013 Conference Paper, cited 4 times
Website

3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images

  • Dhara, Ashis Kumar
  • Mukhopadhyay, Sudipta
  • Khandelwal, Niranjan
2013 Conference Proceedings, cited 7 times
Website

Supervised Machine-Learning Framework and Classifier Evaluation for Automated Three-dimensional Medical Image Segmentation based on Body MRI

  • Frischmann, Patrick
2013 Thesis, cited 0 times
Website

Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume

  • Gavrielides, Marios A
  • Zeng, Rongping
  • Myers, Kyle J
  • Sahiner, Berkman
  • Petrick, Nicholas
Academic radiology 2013 Journal Article, cited 23 times
Website
RATIONALE AND OBJECTIVES: The aim of this study was to quantify the effect of overlapping reconstruction on the precision and accuracy of lung nodule volume estimates in a phantom computed tomographic (CT) study. MATERIALS AND METHODS: An anthropomorphic phantom was used with a vasculature insert on which synthetic lung nodules were attached. Repeated scans of the phantom were acquired using a 64-slice CT scanner. Overlapping and contiguous reconstructions were performed for a range of CT imaging parameters (exposure, slice thickness, pitch, reconstruction kernel) and a range of nodule characteristics (size, density). Nodule volume was estimated with a previously developed matched-filter algorithm. RESULTS: Absolute percentage bias across all nodule sizes (n = 2880) was significantly lower when overlapping reconstruction was used, with an absolute percentage bias of 6.6% (95% confidence interval [CI], 6.4-6.9), compared to 13.2% (95% CI, 12.7-13.8) for contiguous reconstruction. Overlapping reconstruction also showed a precision benefit, with a lower standard percentage error of 7.1% (95% CI, 6.9-7.2) compared with 15.3% (95% CI, 14.9-15.7) for contiguous reconstructions across all nodules. Both effects were more pronounced for the smaller, subcentimeter nodules. CONCLUSIONS: These results support the use of overlapping reconstruction to improve the quantitative assessment of nodule size with CT imaging.

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

  • Gutman, David A
  • Cobb, Jake
  • Somanna, Dhananjaya
  • Park, Yuna
  • Wang, Fusheng
  • Kurc, Tahsin
  • Saltz, Joel H
  • Brat, Daniel J
  • Cooper, Lee A
Journal of the American Medical Informatics Association 2013 Journal Article, cited 70 times
Website
BACKGROUND: The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types. OBJECTIVE: To build an integrated web-based platform supporting whole-slide pathology image visualization and data integration. MATERIALS AND METHODS: All images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites. RESULTS: The Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20,000 whole-slide images from 22 cancer types. DISCUSSION: The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics. CONCLUSIONS: With the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints.

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

  • Gutman, David A
  • Cobb, Jake
  • Somanna, Dhananjaya
  • Park, Yuna
  • Wang, Fusheng
  • Kurc, Tahsin
  • Saltz, Joel H
  • Brat, Daniel J
  • Cooper, Lee AD
  • Kong, Jun
Journal of the American Medical Informatics Association 2013 Journal Article, cited 70 times
Website
BACKGROUND: The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types. OBJECTIVE: To build an integrated web-based platform supporting whole-slide pathology image visualization and data integration. MATERIALS AND METHODS: All images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites. RESULTS: The Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20,000 whole-slide images from 22 cancer types. DISCUSSION: The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics. CONCLUSIONS: With the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints.

MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set

  • Gutman, David A
  • Cooper, Lee A D
  • Hwang, Scott N
  • Holder, Chad A
  • Gao, Jingjing
  • Aurora, Tarun D
  • Dunn, William D Jr
  • Scarpace, Lisa
  • Mikkelsen, Tom
  • Jain, Rajan
  • Wintermark, Max
  • Jilwan, Manal
  • Raghavan, Prashant
  • Huang, Erich
  • Clifford, Robert J
  • Mongkolwat, Pattanasak
  • Kleper, Vladimir
  • Freymann, John
  • Kirby, Justin
  • Zinn, Pascal O
  • Moreno, Carlos S
  • Jaffe, Carl
  • Colen, Rivka
  • Rubin, Daniel L
  • Saltz, Joel
  • Flanders, Adam
  • Brat, Daniel J
Radiology 2013 Journal Article, cited 217 times
Website
PURPOSE: To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival. MATERIALS AND METHODS: Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff alpha statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test. RESULTS: Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01). CONCLUSION: This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.

Vector quantization-based automatic detection of pulmonary nodules in thoracic CT images

  • Han, Hao
  • Li, Lihong
  • Han, Fangfang
  • Zhang, Hao
  • Moore, William
  • Liang, Zhengrong
2013 Conference Proceedings, cited 8 times
Website

Multiparametric MRI of prostate cancer: An update on state‐of‐the‐art techniques and their performance in detecting and localizing prostate cancer

  • Hegde, John V
  • Mulkern, Robert V
  • Panych, Lawrence P
  • Fennessy, Fiona M
  • Fedorov, Andriy
  • Maier, Stephan E
  • Tempany, Clare
Journal of Magnetic Resonance Imaging 2013 Journal Article, cited 164 times
Website
Magnetic resonance (MR) examinations of men with prostate cancer are most commonly performed for detecting, characterizing, and staging the extent of disease to best determine diagnostic or treatment strategies, which range from biopsy guidance to active surveillance to radical prostatectomy. Given both the exam's importance to individual treatment plans and the time constraints present for its operation at most institutions, it is essential to perform the study effectively and efficiently. This article reviews the most commonly employed modern techniques for prostate cancer MR examinations, exploring the relevant signal characteristics from the different methods discussed and relating them to intrinsic prostate tissue properties. Also, a review of recent articles using these methods to enhance clinical interpretation and assess clinical performance is provided. J. Magn. Reson. Imaging 2013;37:1035-1054. © 2013 Wiley Periodicals, Inc.

Design of a Patient-Specific Radiotherapy Treatment Target

  • Heyns, Michael
  • Breseman, Kelsey
  • Lee, Christopher
  • Bloch, B Nicholas
  • Jaffe, Carl
  • Xiang, Hong
2013 Conference Proceedings, cited 3 times
Website

A reversible data hiding method by histogram shifting in high quality medical images

  • Huang, Li-Chin
  • Tseng, Lin-Yu
  • Hwang, Min-Shiang
Journal of Systems and Software 2013 Journal Article, cited 60 times
Website
Enormous demands for recognizing complicated anatomical structures in medical images have been demanded on high quality of medical image such as each pixel expressed by 16-bit depth. Now, most of data hiding algorithms are still applied in 8-bit depth medical images. We proposed a histogram shifting method for image reversible data hiding testing on high bit depth medical images. Among image local block pixels, we exploit the high correlation for smooth surface of anatomical structure in medical images. Thus, we apply a different value for each block of pixels to produce a difference histogram to embed secret bits. During data embedding stage, the image blocks are divided into two categories due to two corresponding embedding strategies. Via an inverse histogram shifting mechanism, the original image will be accurately recovered after the hidden data extraction. Due to requirements of medical images for data hiding, we proposed six criteria: (1) well-suited for high quality medical images, (2) without salt-and-pepper, (3) applicable to medical image with smooth surface, (4) well-suited sparse histogram of intensity levels, (5) free location map, (6) ability of adjusting data embedding capacity, PSNR and Inter-Slice PSNR. We proposed a data hiding methods satisfying above 6 criteria. © 2012 Elsevier Inc. All rights reserved

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

  • Hunter, Luke
2013 Thesis, cited 4 times
Website

Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers

  • Jain, Rajan
  • Poisson, Laila
  • Narang, Jayant
  • Gutman, David
  • Scarpace, Lisa
  • Hwang, Scott N
  • Holder, Chad
  • Wintermark, Max
  • Colen, Rivka R
  • Kirby, Justin
  • Freymann, John
  • Brat, Daniel J
  • Jaffe, Carl
  • Mikkelsen, Tom
Radiology 2013 Journal Article, cited 99 times
Website
PURPOSE: To correlate tumor blood volume, measured by using dynamic susceptibility contrast material-enhanced T2*-weighted magnetic resonance (MR) perfusion studies, with patient survival and determine its association with molecular subclasses of glioblastoma (GBM). MATERIALS AND METHODS: This HIPAA-compliant retrospective study was approved by institutional review board. Fifty patients underwent dynamic susceptibility contrast-enhanced T2*-weighted MR perfusion studies and had gene expression data available from the Cancer Genome Atlas. Relative cerebral blood volume (rCBV) (maximum rCBV [rCBV(max)] and mean rCBV [rCBV(mean)]) of the contrast-enhanced lesion as well as rCBV of the nonenhanced lesion (rCBV(NEL)) were measured. Patients were subclassified according to the Verhaak and Phillips classification schemas, which are based on similarity to defined genomic expression signature. We correlated rCBV measures with the molecular subclasses as well as with patient overall survival by using Cox regression analysis. RESULTS: No statistically significant differences were noted for rCBV(max), rCBV(mean) of contrast-enhanced lesion or rCBV(NEL) between the four Verhaak classes or the three Phillips classes. However, increased rCBV measures are associated with poor overall survival in GBM. The rCBV(max) (P = .0131) is the strongest predictor of overall survival regardless of potential confounders or molecular classification. Interestingly, including the Verhaak molecular GBM classification in the survival model clarifies the association of rCBV(mean) with patient overall survival (hazard ratio: 1.46, P = .0212) compared with rCBV(mean) alone (hazard ratio: 1.25, P = .1918). Phillips subclasses are not predictive of overall survival nor do they affect the predictive ability of rCBV measures on overall survival. CONCLUSION: The rCBV(max) measurements could be used to predict patient overall survival independent of the molecular subclasses of GBM; however, Verhaak classifiers provided additional information, suggesting that molecular markers could be used in combination with hemodynamic imaging biomarkers in the future.

Radiogenomic correlation for prognosis in patients with glioblastoma multiformae

  • Karnayana, Pallavi Machaiah
2013 Thesis, cited 0 times
Website

Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data

  • Kumar, Ashnil
  • Kim, Jinman
  • Cai, Weidong
  • Fulham, Michael
  • Feng, Dagan
Journal of Digital Imaging 2013 Journal Article, cited 109 times
Website
Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.

Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker

  • Li, Ang
  • Li, Changyang
  • Wang, Xiuying
  • Eberl, Stefan
  • Feng, David Dagan
  • Fulham, Michael
2013 Conference Proceedings, cited 9 times
Website

vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging

  • Lundström, Claes
International journal of computer assisted radiology and surgery 2013 Journal Article, cited 0 times
Website
Purpose Often, the large amounts of data generated in diagnostic imaging cause overload problems for IT systems and radiologists. This entails a need of effective use of data reduction beyond lossless levels, which, in turn, underlines the need to measure and control the image fidelity. Existing image fidelity metrics, however, fail to fully support important requirements from a modern clinical context: support for high-dimensional data, visualization awareness, and independence from the original data. Methods We propose an image fidelity metric, called the visual peak signal-to-noise ratio (vPSNR), fulfilling the three main requirements. A series of image fidelity tests on CT data sets is employed. The impact of visualization transform (grayscale window) on diagnostic quality of irreversibly compressed data sets is evaluated through an observer-based study. In addition, several tests were performed demonstrating the benefits, limitations, and characteristics of vPSNR in different data reduction scenarios. Results The visualization transform has a significant impact on diagnostic quality, and the vPSNR is capable of representing this effect. Moreover, the tests establish that the vPSNR is broadly applicable. Conclusions vPSNR fills a gap not served by existing image fidelity metrics, relevant for the clinical context. While vPSNR alone cannot fulfill all image fidelity needs, it can be a useful complement in a wide range of scenarios.

Imaging descriptors improve the predictive power of survival models for glioblastoma patients

  • Mazurowski, Maciej Andrzej
  • Desjardins, Annick
  • Malof, Jordan Milton
Neuro-oncology 2013 Journal Article, cited 62 times
Website
BACKGROUND: Because effective prediction of survival time can be highly beneficial for the treatment of glioblastoma patients, the relationship between survival time and multiple patient characteristics has been investigated. In this paper, we investigate whether the predictive power of a survival model based on clinical patient features improves when MRI features are also included in the model. METHODS: The subjects in this study were 82 glioblastoma patients for whom clinical features as well as MR imaging exams were made available by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). Twenty-six imaging features in the available MR scans were assessed by radiologists from the TCGA Glioma Phenotype Research Group. We used multivariate Cox proportional hazards regression to construct 2 survival models: one that used 3 clinical features (age, gender, and KPS) as the covariates and 1 that used both the imaging features and the clinical features as the covariates. Then, we used 2 measures to compare the predictive performance of these 2 models: area under the receiver operating characteristic curve for the 1-year survival threshold and overall concordance index. To eliminate any positive performance estimation bias, we used leave-one-out cross-validation. RESULTS: The performance of the model based on both clinical and imaging features was higher than the performance of the model based on only the clinical features, in terms of both area under the receiver operating characteristic curve (P < .01) and the overall concordance index (P < .01). CONCLUSIONS: Imaging features assessed using a controlled lexicon have additional predictive value compared with clinical features when predicting survival time in glioblastoma patients.

Phase I trial of preoperative chemoradiation plus sorafenib for high-risk extremity soft tissue sarcomas with dynamic contrast-enhanced MRI correlates

  • Meyer, Janelle M
  • Perlewitz, Kelly S
  • Hayden, James B
  • Doung, Yee-Cheen
  • Hung, Arthur Y
  • Vetto, John T
  • Pommier, Rodney F
  • Mansoor, Atiya
  • Beckett, Brooke R
  • Tudorica, Alina
Clinical Cancer Research 2013 Journal Article, cited 41 times
Website

Image segmentation on GPGPUs: a cellular automata-based approach

  • Olmedo, Irving
  • Perez, Yessika Guerra
  • Johnson, James F
  • Raut, Lakshman
  • Hoe, David HK
2013 Conference Proceedings, cited 0 times
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Compressibility variations of JPEG2000 compressed computed tomography

  • Pambrun, Jean-Francois
  • Noumeir, Rita
2013 Conference Proceedings, cited 3 times
Website

Texture classification of lung computed tomography images

  • Pheng, Hang See
  • Shamsuddin, Siti M
2013 Conference Proceedings, cited 2 times
Website

ROC curves for low-dose CT in the National Lung Screening Trial

  • Pinsky, P. F.
  • Gierada, D. S.
  • Nath, H.
  • Kazerooni, E. A.
  • Amorosa, J.
J Med ScreenJournal of medical screening 2013 Journal Article, cited 4 times
Website
The National Lung Screening Trial (NLST) reported a 20% reduction in lung cancer specific mortality using low-dose chest CT (LDCT) compared with chest radiograph (CXR) screening. The high number of false positive screens with LDCT (around 25%) raises concerns. NLST radiologists reported LDCT screens as either positive or not positive, based primarily on the presence of a 4+ mm non-calcified lung nodule (NCN). They did not explicitly record a propensity score for lung cancer. However, by using maximum NCN size, or alternatively, radiologists' recommendations for diagnostic follow-up categorized hierarchically, surrogate propensity scores (PSSZ and PSFR) were created. These scores were then used to compute ROC curves, which determine possible operating points of sensitivity versus false positive rate (1-Specificity). The area under the ROC curve (AUC) was 0.934 and 0.928 for PSFR and PSSZ, respectively; the former was significantly greater than the latter. With the NLST definition of a positive screen, sensitivity and specificity of LDCT was 93.1% and 76.5%, respectively. With cutoffs based on PSFR, a specificity of 92.4% could be achieved while only lowering sensitivity to 86.9%. Radiologists using LDCT have good predictive ability; the optimal operating point for sensitivity and specificity remains to be determined.

National lung screening trial: variability in nodule detection rates in chest CT studies

  • Pinsky, P. F.
  • Gierada, D. S.
  • Nath, P. H.
  • Kazerooni, E.
  • Amorosa, J.
Radiology 2013 Journal Article, cited 43 times
Website
PURPOSE: To characterize the variability in radiologists' interpretations of computed tomography (CT) studies in the National Lung Screening Trial (NLST) (including assessment of false-positive rates [FPRs] and sensitivity), to examine factors that contribute to variability, and to evaluate trade-offs between FPRs and sensitivity among different groups of radiologists. MATERIALS AND METHODS: The HIPAA-compliant NLST was approved by the institutional review board at each screening center; all participants provided informed consent. NLST radiologists reported overall screening results, nodule-specific findings, and recommendations for diagnostic follow-up. A noncalcified nodule of 4 mm or larger constituted a positive screening result. The FPR was defined as the rate of positive screening examinations in participants without a cancer diagnosis within 1 year. Descriptive analyses and mixed-effects models were utilized. The average odds ratio (OR) for a false-positive result across all pairs of radiologists was used as a measure of variability. RESULTS: One hundred twelve radiologists at 32 screening centers each interpreted 100 or more NLST CT studies, interpreting 72 160 of 75 126 total NLST CT studies in aggregate. The mean FPR for radiologists was 28.7% +/- 13.7 (standard deviation), with a range of 3.8%-69.0%. The model yielded an average OR of 2.49 across all pairs of radiologists and an OR of 1.83 for pairs within the same screening center. Mean FPRs were similar for academic versus nonacademic centers (27.9% and 26.7%, respectively) and for centers inside (25.0%) versus outside (28.7%) the U.S. "histoplasmosis belt." Aggregate sensitivity was 96.5% for radiologists with FPRs higher than the median (27.1%), compared with 91.9% for those with FPRs lower than the median (P = .02). CONCLUSION: There was substantial variability in radiologists' FPRs. Higher FPRs were associated with modestly higher sensitivity.

Exploring relationships between multivariate radiological phenotypes and genetic features: A case-study in Glioblastoma using the Cancer Genome Atlas

  • Rao, Arvind
2013 Conference Proceedings, cited 0 times

Magnetic resonance spectroscopy as an early indicator of response to anti-angiogenic therapy in patients with recurrent glioblastoma: RTOG 0625/ACRIN 6677

  • Ratai, E. M.
  • Zhang, Z.
  • Snyder, B. S.
  • Boxerman, J. L.
  • Safriel, Y.
  • McKinstry, R. C.
  • Bokstein, F.
  • Gilbert, M. R.
  • Sorensen, A. G.
  • Barboriak, D. P.
Neuro-oncology 2013 Journal Article, cited 0 times
Website
Background. The prognosis for patients with recurrent glioblastoma remains poor. The purpose of this study was to assess the potential role of MR spectroscopy as an early indicator of response to anti-angiogenic therapy. Methods. Thirteen patients with recurrent glioblastoma were enrolled in RTOG 0625/ACRIN 6677, a prospective multicenter trial in which bevacizumab was used in combination with either temozolomide or irinotecan. Patients were scanned prior to treatment and at specific timepoints during the treatment regimen. Postcontrast T1-weighted MRI was used to assess 6-month progression-free survival. Spectra from the enhancing tumor and peritumoral regions were defined on the postcontrast T1-weighted images. Changes in the concentration ratios of N-acetylaspartate/creatine (NAA/Cr), choline-containing compounds (Cho)/Cr, and NAA/Cho were quantified in comparison with pretreatment values. Results. NAA/Cho levels increased and Cho/Cr levels decreased within enhancing tumor at 2 weeks relative to pretreatment levels (P = .048 and P = .016, respectively), suggesting a possible antitumor effect of bevacizumab with cytotoxic chemotherapy. Nine of the 13 patients were alive and progression free at 6 months. Analysis of receiver operating characteristic curves for NAA/Cho changes in tumor at 8 weeks revealed higher levels in patients progression free at 6 months (area under the curve = 0.85), suggesting that NAA/Cho is associated with treatment response. Similar results were observed for receiver operating characteristic curve analyses against 1-year survival. In addition, decreased Cho/Cr and increased NAA/Cr and NAA/Cho in tumor periphery at 16 weeks posttreatment were associated with both 6-month progression-free survival and 1-year survival. Conclusion. Changes in NAA and Cho by MR spectroscopy may potentially be useful as imaging biomarkers in assessing response to anti-angiogenic treatment.

3D medical image denoising using 3D block matching and low-rank matrix completion

  • Roozgard, Aminmohammad
  • Barzigar, Nafise
  • Verma, Pramode
  • Cheng, Samuel
2013 Conference Proceedings, cited 0 times
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Lung nodule detection using fuzzy clustering and support vector machines

  • Sivakumar, S
  • Chandrasekar, C
International Journal of Engineering and Technology 2013 Journal Article, cited 43 times
Website
Lung cancer is the primary cause of tumor deaths for both sexes in most countries. Lung nodule, an abnormality which leads to lung cancer is detected by various medical imaging techniques like X-ray, Computerized Tomography (CT), etc. Detection of lung nodules is a challenging task since the nodules are commonly attached to the blood vessels. Many studies have shown that early diagnosis is the most efficient way to cure this disease. This paper aims to develop an efficient lung nodule detection scheme by performing nodule segmentation through fuzzy based clustering models; classification by using a machine learning technique called Support Vector Machine (SVM). This methodology uses three different types of kernels among these RBF kernel gives better class performance.

Supervised Machine Learning Approach Utilizing Artificial Neural Networks for Automated Prostate Zone Segmentation in Abdominal MR images

  • Wieser, Hans-Peter
2013 Thesis, cited 0 times
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Clinically relevant modeling of tumor growth and treatment response

  • Yankeelov, Thomas E
  • Atuegwu, Nkiruka
  • Hormuth, David
  • Weis, Jared A
  • Barnes, Stephanie L
  • Miga, Michael I
  • Rericha, Erin C
  • Quaranta, Vito
Science translational medicine 2013 Journal Article, cited 70 times
Website
Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point--for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

  • Aerts, H. J.
  • Velazquez, E. R.
  • Leijenaar, R. T.
  • Parmar, C.
  • Grossmann, P.
  • Cavalho, S.
  • Bussink, J.
  • Monshouwer, R.
  • Haibe-Kains, B.
  • Rietveld, D.
  • Hoebers, F.
  • Rietbergen, M. M.
  • Leemans, C. R.
  • Dekker, A.
  • Quackenbush, J.
  • Gillies, R. J.
  • Lambin, P.
2014 Journal Article, cited 1029 times
Website
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

Test–Retest Reproducibility Analysis of Lung CT Image Features

  • Balagurunathan, Yoganand
  • Kumar, Virendra
  • Gu, Yuhua
  • Kim, Jongphil
  • Wang, Hua
  • Liu, Ying
  • Goldgof, Dmitry B
  • Hall, Lawrence O
  • Korn, Rene
  • Zhao, Binsheng
Journal of Digital Imaging 2014 Journal Article, cited 85 times
Website
Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test–retest, the biological range and a feature independence measure. There were 66 (30.14 %) features with concordance correlation coefficient ≥ 0.90 across test–retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R2Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91 % for a size-based feature and 92 % for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test–retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation

  • Blessy, SA Praylin Selva
  • Sulochana, C Helen
Technology and Health Care 2014 Journal Article, cited 0 times
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Using Docker to support reproducible research

  • Chamberlain, Ryan
  • Invenshure, L
  • Schommer, Jennifer
2014 Report, cited 30 times
Website
Reproducible research is a growing movement among scientists, but the tools for creating sustainable software to support the computational side of research are still in their infancy and are typically only being used by scientists with expertise in computer programming and system administration. Docker is a new platform developed for the DevOps community that enables the easy creation and management of consistent computational environments. This article describes how we have applied it to computational science and suggests that it could be a powerful tool for reproducible research.

The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals

  • Clarke, Laurence P.
  • Nordstrom, Robert J.
  • Zhang, Huiming
  • Tandon, Pushpa
  • Zhang, Yantian
  • Redmond, George
  • Farahani, Keyvan
  • Kelloff, Gary
  • Henderson, Lori
  • Shankar, Lalitha
  • Deye, James
  • Capala, Jacek
  • Jacobs, Paula
Translational oncology 2014 Journal Article, cited 0 times
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Automated Medical Image Modality Recognition by Fusion of Visual and Text Information

  • Codella, Noel
  • Connell, Jonathan
  • Pankanti, Sharath
  • Merler, Michele
  • Smith, John R
2014 Book Section, cited 10 times
Website

Semantic Model Vector for ImageCLEF2013

  • Codella, Noel
  • Merler, Michele
2014 Report, cited 0 times
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NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures

  • Colen, Rivka
  • Foster, Ian
  • Gatenby, Robert
  • Giger, Mary Ellen
  • Gillies, Robert
  • Gutman, David
  • Heller, Matthew
  • Jain, Rajan
  • Madabhushi, Anant
  • Madhavan, Subha
  • Napel, Sandy
  • Rao, Arvind
  • Saltz, Joel
  • Tatum, James
  • Verhaak, Roeland
  • Whitman, Gary
Translational oncology 2014 Journal Article, cited 39 times
Website
The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project

  • Colen, Rivka R
  • Vangel, Mark
  • Wang, Jixin
  • Gutman, David A
  • Hwang, Scott N
  • Wintermark, Max
  • Jain, Rajan
  • Jilwan-Nicolas, Manal
  • Chen, James Y
  • Raghavan, Prashant
  • Holder, C. A.
  • Rubin, D.
  • Huang, E.
  • Kirby, J.
  • Freymann, J.
  • Jaffe, C. C.
  • Flanders, A.
  • TCGA Glioma Phenotype Research Group
  • Zinn, P. O.
BMC Medical Genomics 2014 Journal Article, cited 47 times
Website
BACKGROUND: Invasion of tumor cells into adjacent brain parenchyma is a major cause of treatment failure in glioblastoma. Furthermore, invasive tumors are shown to have a different genomic composition and metabolic abnormalities that allow for a more aggressive GBM phenotype and resistance to therapy. We thus seek to identify those genomic abnormalities associated with a highly aggressive and invasive GBM imaging-phenotype. METHODS: We retrospectively identified 104 treatment-naive glioblastoma patients from The Cancer Genome Atlas (TCGA) whom had gene expression profiles and corresponding MR imaging available in The Cancer Imaging Archive (TCIA). The standardized VASARI feature-set criteria were used for the qualitative visual assessments of invasion. Patients were assigned to classes based on the presence (Class A) or absence (Class B) of statistically significant invasion parameters to create an invasive imaging signature; imaging genomic analysis was subsequently performed using GenePattern Comparative Marker Selection module (Broad Institute). RESULTS: Our results show that patients with a combination of deep white matter tracts and ependymal invasion (Class A) on imaging had a significant decrease in overall survival as compared to patients with absence of such invasive imaging features (Class B) (8.7 versus 18.6 months, p < 0.001). Mitochondrial dysfunction was the top canonical pathway associated with Class A gene expression signature. The MYC oncogene was predicted to be the top activation regulator in Class A. CONCLUSION: We demonstrate that MRI biomarker signatures can identify distinct GBM phenotypes associated with highly significant survival differences and specific molecular pathways. This study identifies mitochondrial dysfunction as the top canonical pathway in a very aggressive GBM phenotype. Thus, imaging-genomic analyses may prove invaluable in detecting novel targetable genomic pathways.

Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death

  • Colen, Rivka R
  • Wang, Jixin
  • Singh, Sanjay K
  • Gutman, David A
  • Zinn, Pascal O
Radiology 2014 Journal Article, cited 36 times
Website
PURPOSE: To identify the molecular profiles of cell death as defined by necrosis volumes at magnetic resonance (MR) imaging and uncover sex-specific molecular signatures potentially driving oncogenesis and cell death in glioblastoma (GBM). MATERIALS AND METHODS: This retrospective study was HIPAA compliant and had institutional review board approval, with waiver of the need to obtain informed consent. The molecular profiles for 99 patients (30 female patients, 69 male patients) were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Volumes of necrosis at MR imaging were extracted. Differential gene expression profiles were obtained in those patients (including male and female patients separately) with high versus low MR imaging volumes of tumor necrosis. Ingenuity Pathway Analysis was used for messenger RNA-microRNA interaction analysis. A histopathologic data set (n = 368; 144 female patients, 224 male patients) was used to validate the MR imaging findings by assessing the amount of cell death. A connectivity map was used to identify therapeutic agents potentially targeting sex-specific cell death in GBM. RESULTS: Female patients showed significantly lower volumes of necrosis at MR imaging than male patients (6821 vs 11 050 mm(3), P = .03). Female patients, unlike male patients, with high volumes of necrosis at imaging had significantly shorter survival (6.5 vs 14.5 months, P = .01). Transcription factor analysis suggested that cell death in female patients with GBM is associated with MYC, while that in male patients is associated with TP53 activity. Additionally, a group of therapeutic agents that can potentially be tested to target cell death in a sex-specific manner was identified. CONCLUSION: The results of this study suggest that cell death in GBM may be driven by sex-specific molecular pathways.

A Novel Hybrid Perceptron Neural Network Algorithm for Classifying Breast MRI Tumors

  • ElNawasany, Amal M
  • Ali, Ahmed Fouad
  • Waheed, Mohamed E
2014 Book Section, cited 3 times
Website
Breast cancer today is the leading cause of death amongstcancer patients inflicting women around the world. Breast cancer is themost common cancer in women worldwide. It is also the principle cause ofdeath from cancer among women globally. Early detection of this diseasecan greatly enhance the chances of long-term survival of breast cancervictims. Classification of cancer data helps widely in detection of the dis-ease and it can be achieved using many techniques such as Perceptronwhich is an Artificial Neural Network (ANN) classification technique.In this paper, we proposed a new hybrid algorithm by combining theperceptron algorithm and the feature extraction algorithm after apply-ing the Scale Invariant Feature Transform (SIFT) algorithm in orderto classify magnetic resonance imaging (MRI) breast cancer images. Theproposed algorithm is called breast MRI cancer classifier (BMRICC) andit has been tested tested on 281 MRI breast images (138 abnormal and143 normal). The numerical results of the general performance of theBMRICC algorithm and the comparasion results between it and other 5benchmark classifiers show that, the BMRICC algorithm is a promisingalgorithm and its performance is better than the other algorithms.

Multisite Image Data Collection and Management Using the RSNA Image Sharing Network

  • Erickson, Bradley J
  • Fajnwaks, Patricio
  • Langer, Steve G
  • Perry, John
Translational oncology 2014 Journal Article, cited 3 times
Website
The execution of a multisite trial frequently includes image collection. The Clinical Trials Processor (CTP) makes removal of protected health information highly reliable. It also provides reliable transfer of images to a central review site. Trials using central review of imaging should consider using CTP for handling image data when a multisite trial is being designed.

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation

  • Fedorov, Andriy
  • Fluckiger, Jacob
  • Ayers, Gregory D
  • Li, Xia
  • Gupta, Sandeep N
  • Tempany, Clare
  • Mulkern, Robert
  • Yankeelov, Thomas E
  • Fennessy, Fiona M
Magnetic Resonance Imaging 2014 Journal Article, cited 30 times
Website
Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (K(trans)) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of K(trans). Comparing cAIF, different estimates for both ve, and K(trans) were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while K(trans) values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. K(trans) estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.

Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

  • Gevaert, Olivier
  • Mitchell, Lex A
  • Achrol, Achal S
  • Xu, Jiajing
  • Echegaray, Sebastian
  • Steinberg, Gary K
  • Cheshier, Samuel H
  • Napel, Sandy
  • Zaharchuk, Greg
  • Plevritis, Sylvia K
Radiology 2014 Journal Article, cited 151 times
Website
Purpose: To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data. Materials and Methods: Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways. Results: Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes. Conclusion: Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively.

Projected outcomes using different nodule sizes to define a positive CT lung cancer screening examination

  • Gierada, David S
  • Pinsky, Paul
  • Nath, Hrudaya
  • Chiles, Caroline
  • Duan, Fenghai
  • Aberle, Denise R
Journal of the National Cancer Institute 2014 Journal Article, cited 74 times
Website
Background Computed tomography (CT) screening for lung cancer has been associated with a high frequency of false positive results because of the high prevalence of indeterminate but usually benign small pulmonary nodules. The acceptability of reducing false-positive rates and diagnostic evaluations by increasing the nodule size threshold for a positive screen depends on the projected balance between benefits and risks. Methods We examined data from the National Lung Screening Trial (NLST) to estimate screening CT performance and outcomes for scans with nodules above the 4 mm NLST threshold used to classify a CT screen as positive. Outcomes assessed included screening results, subsequent diagnostic tests performed, lung cancer histology and stage distribution, and lung cancer mortality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the different nodule size thresholds. All statistical tests were two-sided. Results In 64% of positive screens (11 598/18 141), the largest nodule was 7 mm or less in greatest transverse diameter. By increasing the threshold, the percentages of lung cancer diagnoses that would have been missed or delayed and false positives that would have been avoided progressively increased, for example from 1.0% and 15.8% at a 5 mm threshold to 10.5% and 65.8% at an 8 mm threshold, respectively. The projected reductions in postscreening follow-up CT scans and invasive procedures also increased as the threshold was raised. Differences across nodules sizes for lung cancer histology and stage distribution were small but statistically significant. There were no differences across nodule sizes in survival or mortality. Conclusion Raising the nodule size threshold for a positive screen would substantially reduce false-positive CT screenings and medical resource utilization with a variable impact on screening outcomes.

Brain Tumor Detection using Curvelet Transform and Support Vector Machine

  • Gupta, Bhawna
  • Tiwari, Shamik
International Journal of Computer Science and Mobile Computing 2014 Journal Article, cited 8 times
Website

Web based tools for visualizing imaging data and development of XNATView, a zero footprint image viewer

  • Gutman, David A
  • Dunn Jr, William D
  • Cobb, Jake
  • Stoner, Richard M
  • Kalpathy-Cramer, Jayashree
  • Erickson, Bradley
Frontiers in Neuroinformatics 2014 Journal Article, cited 12 times
Website
Advances in web technologies now allow direct visualization of imaging data sets without necessitating the download of large file sets or the installation of software. This allows centralization of file storage and facilitates image review and analysis. XNATView is a light framework recently developed in our lab to visualize DICOM images stored in The Extensible Neuroimaging Archive Toolkit (XNAT). It consists of a PyXNAT-based framework to wrap around the REST application programming interface (API) and query the data in XNAT. XNATView was developed to simplify quality assurance, help organize imaging data, and facilitate data sharing for intra- and inter-laboratory collaborations. Its zero-footprint design allows the user to connect to XNAT from a web browser, navigate through projects, experiments, and subjects, and view DICOM images with accompanying metadata all within a single viewing instance.

A novel computer-aided detection system for pulmonary nodule identification in CT images

  • Han, Hao
  • Li, Lihong
  • Wang, Huafeng
  • Zhang, Hao
  • Moore, William
  • Liang, Zhengrong
2014 Conference Proceedings, cited 5 times
Website

Approaches to uncovering cancer diagnostic and prognostic molecular signatures

  • Hong, Shengjun
  • Huang, Yi
  • Cao, Yaqiang
  • Chen, Xingwei
  • Han, Jing-Dong J
Molecular & Cellular Oncology 2014 Journal Article, cited 2 times
Website
The recent rapid development of high-throughput technology enables the study of molecular signatures for cancer diagnosis and prognosis at multiple levels, from genomic and epigenomic to transcriptomic. These unbiased large-scale scans provide important insights into the detection of cancer-related signatures. In addition to single-layer signatures, such as gene expression and somatic mutations, integrating data from multiple heterogeneous platforms using a systematic approach has been proven to be particularly effective for the identification of classification markers. This approach not only helps to uncover essential driver genes and pathways in the cancer network that are responsible for the mechanisms of cancer development, but will also lead us closer to the ultimate goal of personalized cancer therapy.

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge

  • Huang, W.
  • Li, X.
  • Chen, Y.
  • Li, X.
  • Chang, M. C.
  • Oborski, M. J.
  • Malyarenko, D. I.
  • Muzi, M.
  • Jajamovich, G. H.
  • Fedorov, A.
  • Tudorica, A.
  • Gupta, S. N.
  • Laymon, C. M.
  • Marro, K. I.
  • Dyvorne, H. A.
  • Miller, J. V.
  • Barbodiak, D. P.
  • Chenevert, T. L.
  • Yankeelov, T. E.
  • Mountz, J. M.
  • Kinahan, P. E.
  • Kikinis, R.
  • Taouli, B.
  • Fennessy, F.
  • Kalpathy-Cramer, J.
Transl Oncol 2014 Journal Article, cited 60 times
Website
Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor

  • Jain, R.
  • Poisson, L. M.
  • Gutman, D.
  • Scarpace, L.
  • Hwang, S. N.
  • Holder, C. A.
  • Wintermark, M.
  • Rao, A.
  • Colen, R. R.
  • Kirby, J.
  • Freymann, J.
  • Jaffe, C. C.
  • Mikkelsen, T.
  • Flanders, A.
Radiology 2014 Journal Article, cited 86 times
Website
PURPOSE: To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. MATERIALS AND METHODS: An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. RESULTS: Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). CONCLUSION: Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

Radiological Atlas for Patient Specific Model Generation

  • Kawa, Jacek
  • Juszczyk, Jan
  • Pyciński, Bartłomiej
  • Badura, Paweł
  • Pietka, Ewa
2014 Book Section, cited 11 times
Website

eFis: A Fuzzy Inference Method for Predicting Malignancy of Small Pulmonary Nodules

  • Kaya, Aydın
  • Can, Ahmet Burak
2014 Book Section, cited 3 times
Website

The quest for'diagnostically lossless' medical image compression: a comparative study of objective quality metrics for compressed medical images

  • Kowalik-Urbaniak, Ilona
  • Brunet, Dominique
  • Wang, Jiheng
  • Koff, David
  • Smolarski-Koff, Nadine
  • Vrscay, Edward R
  • Wallace, Bill
  • Wang, Zhou
2014 Conference Proceedings, cited 0 times

Performance Analysis of Denoising in MR Images with Double Density Dual Tree Complex Wavelets, Curvelets and NonSubsampled Contourlet Transforms

  • Krishnakumar, V
  • Parthiban, Latha
Annual Review & Research in Biology 2014 Journal Article, cited 0 times

A Visual Analytics Approach using the Exploration of Multi-Dimensional Feature Spaces for Content-based Medical Image Retrieval

  • Kumar, Ajit
  • Nette, Falk
  • Klein, Krystal
  • Fulham, Michael
  • Kim, Jung-Ho
2014 Journal Article, cited 13 times
Website

Combining Generative Models for Multifocal Glioma Segmentation and Registration

  • Kwon, Dongjin
  • Shinohara, Russell T
  • Akbari, Hamed
  • Davatzikos, Christos
2014 Book Section, cited 55 times
Website
In this paper, we propose a new method for simultaneously segmenting brain scans of glioma patients and registering these scans to a normal atlas. Performing joint segmentation and registration for brain tumors is very challenging when tumors include multifocal masses and have complex shapes with heterogeneous textures. Our approach grows tumors for each mass from multiple seed points using a tumor growth model and modifies a normal atlas into one with tumors and edema using the combined results of grown tumors. We also generate a tumor shape prior via the random walk with restart, utilizing multiple tumor seeds as initial foreground information. We then incorporate this shape prior into an EM framework which estimates the mapping between the modified atlas and the scans, posteriors for each tissue labels, and the tumor growth model parameters. We apply our method to the BRATS 2013 leaderboard dataset to evaluate segmentation performance. Our method shows the best performance among all participants.

Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging

  • Mazurowski, Maciej A
  • Zhang, Jing
  • Grimm, Lars J
  • Yoon, Sora C
  • Silber, James I
Radiology 2014 Journal Article, cited 88 times
Website

Computer-extracted MR imaging features are associated with survival in glioblastoma patients

  • Mazurowski, Maciej A
  • Zhang, Jing
  • Peters, Katherine B
  • Hobbs, Hasan
Journal of neuro-oncology 2014 Journal Article, cited 33 times
Website
Automatic survival prognosis in glioblastoma (GBM) could result in improved treatment planning for the patient. The purpose of this research is to investigate the association of survival in GBM patients with tumor features in pre-operative magnetic resonance (MR) images assessed using a fully automatic computer algorithm. MR imaging data for 68 patients from two US institutions were used in this study. The images were obtained from the Cancer Imaging Archive. A fully automatic computer vision algorithm was applied to segment the images and extract eight imaging features from the MRI studies. The features included tumor side, proportion of enhancing tumor, proportion of necrosis, T1/FLAIR ratio, major axis length, minor axis length, tumor volume, and thickness of enhancing margin. We constructed a multivariate Cox proportional hazards regression model and used a likelihood ratio test to establish whether the imaging features are prognostic of survival. We also evaluated the individual prognostic value of each feature through multivariate analysis using the multivariate Cox model and univariate analysis using univariate Cox models for each feature. We found that the automatically extracted imaging features were predictive of survival (p = 0.031). Multivariate analysis of individual features showed that two individual features were predictive of survival: proportion of enhancing tumor (p = 0.013), and major axis length (p = 0.026). Univariate analysis indicated the same two features as significant (p = 0.021, and p = 0.017 respectively). We conclude that computer-extracted MR imaging features can be used for survival prognosis in GBM patients.

Automatic rectum limit detection by anatomical markers correlation

  • Namías, R
  • D’Amato, JP
  • Del Fresno, M
  • Vénere, M
Computerized Medical Imaging and Graphics 2014 Journal Article, cited 1 times
Website
Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems. In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems. (C) 2014 Elsevier Ltd. All rights reserved.

Image Processing and Classification Techniques for Early Detection of Lung Cancer for Preventive Health Care: A Survey

  • Naresh, Prashant
  • Shettar, Rajashree
Int. J. of Recent Trends in Engineering & Technology 2014 Journal Article, cited 6 times
Website

Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients

  • Nicolasjilwan, Manal
  • Hu, Ying
  • Yan, Chunhua
  • Meerzaman, Daoud
  • Holder, Chad A
  • Gutman, David
  • Jain, Rajan
  • Colen, Rivka
  • Rubin, Daniel L
  • Zinn, Pascal O
  • Hwang, Scott N
  • Raghavan, Prashant
  • Hammoud, Dima A
  • Scarpace, Lisa M
  • Mikkelsen, Tom
  • Chen, James
  • Gevaert, Olivier
  • Buetow, Kenneth
  • Freymann, John
  • Kirby, Justin
  • Flanders, Adam E
  • Wintermark, Max
Journal of Neuroradiology 2014 Journal Article, cited 49 times
Website
PURPOSE: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679+/-0.068, Akaike's information criterion 566.7, P<0.001). CONCLUSION: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.

Medical image retrieval using hybrid wavelet network classifier

  • Othman, Sufri
  • Jemai, Olfa
  • Zaied, Mourad
  • Ben Amar, Chokri
2014 Conference Proceedings, cited 3 times
Website

Fast and robust methods for non-rigid registration of medical images

  • Parraguez, Stefan Philippo Pszczolkowski
2014 Thesis, cited 1 times
Website

Swift Pre Rendering Volumetric Visualization of Magnetic Resonance Cardiac Images based on Isosurface Technique

  • Patel, Nikhilkumar P
  • Parmar, Shankar K
  • Jain, Kavindra R
Procedia Technology 2014 Journal Article, cited 0 times
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Short-and long-term lung cancer risk associated with noncalcified nodules observed on low-dose CT

  • Pinsky, Paul F
  • Nath, P Hrudaya
  • Gierada, David S
  • Sonavane, Sushil
  • Szabo, Eva
Cancer prevention research 2014 Journal Article, cited 10 times
Website

Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images

  • Qiu, Wu
  • Yuan, Jing
  • Ukwatta, Eranga
  • Sun, Yue
  • Rajchl, Martin
  • Fenster, Aaron
Medical Imaging, IEEE Transactions on 2014 Journal Article, cited 58 times
Website

3D-SCoBeP: 3D medical image registration using sparse coding and belief propagation

  • Roozgard, Aminmohammad
  • Barzigar, Nafise
  • Verma, Pramode
  • Cheng, Samuel
International Journal of Diagnostic Imaging 2014 Journal Article, cited 4 times
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A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations

  • Roth, Holger R
  • Lu, Le
  • Seff, Ari
  • Cherry, Kevin M
  • Hoffman, Joanne
  • Wang, Shijun
  • Liu, Jiamin
  • Turkbey, Evrim
  • Summers, Ronald M
2014 Book Section, cited 192 times
Website

Visual Interpretation with Three-Dimensional Annotations (VITA): Three-Dimensional Image Interpretation Tool for Radiological Reporting

  • Roy, Sharmili
  • Brown, Michael S
  • Shih, George L
Journal of Digital Imaging 2014 Journal Article, cited 5 times
Website
This paper introduces a software framework called Visual Interpretation with Three-Dimensional Annotations (VITA) that is able to automatically generate three-dimensional (3D) visual summaries based on radiological annotations made during routine exam reporting. VITA summaries are in the form of rotating 3D volumes where radiological annotations are highlighted to place important clinical observations into a 3D context. The rendered volume is produced as a Digital Imaging and Communications in Medicine (DICOM) object and is automatically added to the study for archival in Picture Archiving and Communication System (PACS). In addition, a video summary (e.g., MPEG4) can be generated for sharing with patients and for situations where DICOM viewers are not readily available to referring physicians. The current version of VITA is compatible with ClearCanvas; however, VITA can work with any PACS workstation that has a structured annotation implementation (e.g., Extendible Markup Language, Health Level 7, Annotation and Image Markup) and is able to seamlessly integrate into the existing reporting workflow. In a survey with referring physicians, the vast majority strongly agreed that 3D visual summaries improve the communication of the radiologists' reports and aid communication with patients.

2d view aggregation for lymph node detection using a shallow hierarchy of linear classifiers

  • Seff, Ari
  • Lu, Le
  • Cherry, Kevin M
  • Roth, Holger R
  • Liu, Jiamin
  • Wang, Shijun
  • Hoffman, Joanne
  • Turkbey, Evrim B
  • Summers, Ronald M
2014 Book Section, cited 21 times
Website
Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.

A STUDY ON IMAGE DENOISING FOR LUNG CT SCAN IMAGES

  • Sivakumar, S
  • Chandrasekar, C
International Journal of Emerging Technologies in Computational and Applied Sciences 2014 Journal Article, cited 1 times
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Survival analysis of pre-operative GBM patients by using quantitative image features

  • Wangaryattawanich, Pattana
  • Wang, Jixin
  • Thomas, Ginu A
  • Chaddad, Ahmad
  • Zinn, Pascal O
  • Colen, Rivka R
2014 Conference Proceedings, cited 1 times
Website

Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics

  • Xie, Qiuliang
  • Ruan, Dan
Medical physics 2014 Journal Article, cited 15 times
Website
PURPOSE: To improve the efficiency of atlas-based segmentation without compromising accuracy, and to demonstrate the validity of the proposed method on MRI-based prostate segmentation application. METHODS: Accurate and efficient automatic structure segmentation is an important task in medical image processing. Atlas-based methods, as the state-of-the-art, provide good segmentation at the cost of a large number of computationally intensive nonrigid registrations, for anatomical sites/structures that are subject to deformation. In this study, the authors propose to utilize a combination of global, regional, and local metrics to improve the accuracy yet significantly reduce the number of required nonrigid registrations. The authors first perform an affine registration to minimize the global mean squared error (gMSE) to coarsely align each atlas image to the target. Subsequently, a target-specific regional MSE (rMSE), demonstrated to be a good surrogate for dice similarity coefficient (DSC), is used to select a relevant subset from the training atlas. Only within this subset are nonrigid registrations performed between the training images and the target image, to minimize a weighted combination of gMSE and rMSE. Finally, structure labels are propagated from the selected training samples to the target via the estimated deformation fields, and label fusion is performed based on a weighted combination of rMSE and local MSE (lMSE) discrepancy, with proper total-variation-based spatial regularization. RESULTS: The proposed method was applied to a public database of 30 prostate MR images with expert-segmented structures. The authors' method, utilizing only eight nonrigid registrations, achieved a performance with a median/mean DSC of over 0.87/0.86, outperforming the state-of-the-art full-fledged atlas-based segmentation approach of which the median/mean DSC was 0.84/0.82 when applying to their data set. CONCLUSIONS: The proposed method requires a fixed number of nonrigid registrations, independent of atlas size, providing desirable scalability especially important for a large or growing atlas. When applied to prostate segmentation, the method achieved better performance to the state-of-the-art atlas-based approaches, with significant improvement in computation efficiency. The proposed rationale of utilizing jointly global, regional, and local metrics, based on the information characteristic and surrogate behavior for registration and fusion subtasks, can be extended naturally to similarity metrics beyond MSE, such as correlation or mutual information types.

A fully automatic extraction of magnetic resonance image features in glioblastoma patients

  • Zhang, Jing
  • Barboriak, Daniel P
  • Hobbs, Hasan
  • Mazurowski, Maciej A
Medical physics 2014 Journal Article, cited 21 times
Website
PURPOSE: Glioblastoma is the most common malignant brain tumor. It is characterized by low median survival time and high survival variability. Survival prognosis for glioblastoma is very important for optimized treatment planning. Imaging features observed in magnetic resonance (MR) images were shown to be a good predictor of survival. However, manual assessment of MR features is time-consuming and can be associated with a high inter-reader variability as well as inaccuracies in the assessment. In response to this limitation, the authors proposed and evaluated a computer algorithm that extracts important MR image features in a fully automatic manner. METHODS: The algorithm first automatically segmented the available volumes into a background region and four tumor regions. Then, it extracted ten features from the segmented MR imaging volumes, some of which were previously indicated as predictive of clinical outcomes. To evaluate the algorithm, the authors compared the extracted features for 73 glioblastoma patients to the reference standard established by manual segmentation of the tumors. RESULTS: The experiments showed that their algorithm was able to extract most of the image features with moderate to high accuracy. High correlation coefficients between the automatically extracted value and reference standard were observed for the tumor location, minor and major axis length as well as tumor volume. Moderately high correlation coefficients were also observed for proportion of enhancing tumor, proportion of necrosis, and thickness of enhancing margin. The correlation coefficients for all these features were statistically significant (p < 0.0001). CONCLUSIONS: The authors proposed and evaluated an algorithm that, given a set of MR volumes of a glioblastoma patient, is able to extract MR image features that correlate well with their reference standard. Future studies will evaluate how well the computer-extracted features predict survival.

A generalized framework for medical image classification and recognition

  • Abedini, M
  • Codella, NCF
  • Connell, JH
  • Garnavi, R
  • Merler, M
  • Pankanti, S
  • Smith, JR
  • Syeda-Mahmood, T
IBM Journal of Research and Development 2015 Journal Article, cited 19 times
Website
In this work, we study the performance of a two-stage ensemble visual machine learning framework for classification of medical images. In the first stage, models are built for subsets of features and data, and in the second stage, models are combined. We demonstrate the performance of this framework in four contexts: 1) The public ImageCLEF (Cross Language Evaluation Forum) 2013 medical modality recognition benchmark, 2) echocardiography view and mode recognition, 3) dermatology disease recognition across two datasets, and 4) a broad medical image dataset, merged from multiple data sources into a collection of 158 categories covering both general and specific medical concepts-including modalities, body regions, views, and disease states. In the first context, the presented system achieves state-of-art performance of 82.2% multiclass accuracy. In the second context, the system attains 90.48% multiclass accuracy. In the third, state-of-art performance of 90% specificity and 90% sensitivity is obtained on a small standardized dataset of 200 images using a leave-one-out strategy. For a larger dataset of 2,761 images, 95% specificity and 98% sensitivity is obtained on a 20% held-out test set. Finally, in the fourth context, the system achieves sensitivity and specificity of 94.7% and 98.4%, respectively, demonstrating the ability to generalize over domains.

Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments

  • Albalooshi, Fatema A
2015 Thesis, cited 0 times
Website
Boundary extraction for object region segmentation is one of the most challenging tasks in image processing and computer vision areas. The complexity of large variations in the appearance of the object and the background in a typical image causes the performance degradation of existing segmentation algorithms. One of the goals of computer vision studies is to produce algorithms to segment object regions to produce accurate object boundaries that can be utilized in feature extraction and classification. This dissertation research considers the incorporation of prior knowledge of intensity/color of objects of interest within segmentation framework to enhance the performance of object region and boundary extraction of targets in unconstrained environments. The information about intensity/color of object of interest is taken from small patches as seeds that are fed to learn a neural network. The main challenge is accounting for the projection transformation between the limited amount of prior information and the appearance of the real object of interest in the testing data. We address this problem by the use of a Self-organizing Map (SOM) which is an unsupervised learning neural network. The segmentation process is achieved by the construction of a local fitted image level-set cost function, in which, the dynamic variable is a Best Matching Unit (BMU) coming from the SOM map. The proposed method is demonstrated on the PASCAL 2011 challenging dataset, in which, images contain objects with variations of illuminations, shadows, occlusions and clutter. In addition, our method is tested on different types of imagery including thermal, hyperspectral, and medical imagery. Metrics illustrate the effectiveness and accuracy of the proposed algorithm in improving the efficiency of boundary extraction and object region detection. In order to reduce computational time, a lattice Boltzmann Method (LBM) convergence criteria is used along with the proposed self-organized active contour model for producing faster and effective segmentation. The lattice Boltzmann method is utilized to evolve the level-set function rapidly and terminate the evolution of the curve at the most optimum region. Experiments performed on our test datasets show promising results in terms of time and quality of the segmentation when compared to other state-of-the-art learning-based active contour model approaches. Our method is more than 53% faster than other state-of-the-art methods. Research is in progress to employ Time Adaptive Self- Organizing Map (TASOM) for improved segmentation and utilize the parallelization property of the LBM to achieve real-time segmentation.

Application of Fuzzy c-means and Neural networks to categorize tumor affected breast MR Images

  • Anand, Shruthi
  • Vinod, Viji
  • Rampure, Anand
International Journal of Applied Engineering Research 2015 Journal Article, cited 4 times
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Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned

  • Armato, Samuel G
  • Hadjiiski, Lubomir
  • Tourassi, Georgia D
  • Drukker, Karen
  • Giger, Maryellen L
  • Li, Feng
  • Redmond, George
  • Farahani, Keyvan
  • Kirby, Justin S
  • Clarke, Laurence P
Journal of Medical Imaging 2015 Journal Article, cited 20 times
Website
The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.

Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images

  • Benalcázar, Marco E
  • Brun, Marcel
  • Ballarin, Virginia
2015 Conference Proceedings, cited 0 times
Website

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation

  • Blessy, SA Praylin Selva
  • Sulochana, C Helen
Technology and Health Care 2015 Journal Article, cited 0 times
Website
BACKGROUND: Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. OBJECTIVE: To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. METHODS: Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. RESULTS: Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. CONCLUSIONS: Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

Singular value decomposition using block least mean square method for image denoising and compression

  • Boyat, Ajay Kumar
  • Khare, Parth
2015 Conference Proceedings, cited 1 times
Website

Comparing nonrigid registration techniques for motion corrected MR prostate diffusion imaging

  • Buerger, C
  • Sénégas, J
  • Kabus, S
  • Carolus, H
  • Schulz, H
  • Agarwal, H
  • Turkbey, B
  • Choyke, PL
  • Renisch, S
Medical physics 2015 Journal Article, cited 4 times
Website
PURPOSE: T2-weighted magnetic resonance imaging (MRI) is commonly used for anatomical visualization in the pelvis area, such as the prostate, with high soft-tissue contrast. MRI can also provide functional information such as diffusion-weighted imaging (DWI) which depicts the molecular diffusion processes in biological tissues. The combination of anatomical and functional imaging techniques is widely used in oncology, e.g., for prostate cancer diagnosis and staging. However, acquisition-specific distortions as well as physiological motion lead to misalignments between T2 and DWI and consequently to a reduced diagnostic value. Image registration algorithms are commonly employed to correct for such misalignment. METHODS: The authors compare the performance of five state-of-the-art nonrigid image registration techniques for accurate image fusion of DWI with T2. RESULTS: Image data of 20 prostate patients with cancerous lesions or cysts were acquired. All registration algorithms were validated using intensity-based as well as landmark-based techniques. CONCLUSIONS: The authors' results show that the "fast elastic image registration" provides most accurate results with a target registration error of 1.07 +/- 0.41 mm at minimum execution times of 11 +/- 1 s.

Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models

  • Chaddad, Ahmad
Journal of Biomedical Imaging 2015 Journal Article, cited 29 times
Website

High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features

  • Chaddad, Ahmad
  • Tanougast, Camel
Advances in Bioinformatics 2015 Journal Article, cited 5 times
Website
Statistical features are widely used in radiology for tumor heterogeneity assessment using magnetic resonance (MR) imaging technique. In this paper, feature selection based on decision tree is examined to determine the relevant subset of glioblastoma (GBM) phenotypes in the statistical domain. To discriminate between active tumor (vAT) and edema/invasion (vE) phenotype, we selected the significant features using analysis of variance (ANOVA) with p value < 0.01. Then, we implemented the decision tree to define the optimal subset features of phenotype classifier. Naive Bayes (NB), support vector machine (SVM), and decision tree (DT) classifier were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate vAT from vE. Whole nine features were statistically significant to classify the vAT from vE with p value < 0.01. Feature selection based on decision tree showed the best performance by the comparative study using full feature set. The feature selected showed that the two features Kurtosis and Skewness achieved a highest range value of 58.33-75.00% accuracy classifier and 73.88-92.50% AUC. This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression.

Automatic detection of spiculation of pulmonary nodules in computed tomography images

  • Ciompi, F
  • Jacobs, C
  • Scholten, ET
  • van Riel, SJ
  • Wille, MMW
  • Prokop, M
  • van Ginneken, B
2015 Conference Proceedings, cited 5 times
Website

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images

  • Cui, Yi
  • Tha, Khin Khin
  • Terasaka, Shunsuke
  • Yamaguchi, Shigeru
  • Wang, Jeff
  • Kudo, Kohsuke
  • Xing, Lei
  • Shirato, Hiroki
  • Li, Ruijiang
Radiology 2015 Journal Article, cited 45 times
Website
PURPOSE: To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis. MATERIALS AND METHODS: This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort. RESULTS: The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409). CONCLUSION: The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma.

Computer-aided detection of lung nodules using outer surface features

  • Demir, Önder
  • Yılmaz Çamurcu, Ali
Bio-Medical Materials and Engineering 2015 Journal Article, cited 28 times
Website
In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.

Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases

  • Dubey, Shiv Ram
  • Singh, Satish Kumar
  • Singh, Rajat Kumar
IEEE Trans Image Process 2015 Journal Article, cited 52 times
Website
A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.

Performance Analysis of Prediction Methods for Lossless Image Compression

  • Egorov, Nickolay
  • Novikov, Dmitriy
  • Gilmutdinov, Marat
2015 Book Section, cited 4 times
Website

A Content-Based-Image-Retrieval Approach for Medical Image Repositories

  • el Rifai, Diaa
  • Maeder, Anthony
  • Liyanage, Liwan
2015 Conference Paper, cited 2 times
Website

Imaging genomics of glioblastoma: state of the art bridge between genomics and neuroradiology

  • ElBanan, Mohamed G
  • Amer, Ahmed M
  • Zinn, Pascal O
  • Colen, Rivka R
Neuroimaging Clinics of North America 2015 Journal Article, cited 29 times
Website
Glioblastoma (GBM) is the most common and most aggressive primary malignant tumor of the central nervous system. Recently, researchers concluded that the "one-size-fits-all" approach for treatment of GBM is no longer valid and research should be directed toward more personalized and patient-tailored treatment protocols. Identification of the molecular and genomic pathways underlying GBM is essential for achieving this personalized and targeted therapeutic approach. Imaging genomics represents a new era as a noninvasive surrogate for genomic and molecular profile identification. This article discusses the basics of imaging genomics of GBM, its role in treatment decision-making, and its future potential in noninvasive genomic identification.

Diffusion MRI quality control and functional diffusion map results in ACRIN 6677/RTOG 0625: a multicenter, randomized, phase II trial of bevacizumab and chemotherapy in recurrent glioblastoma

  • Ellingson, Benjamin M
  • Kim, Eunhee
  • Woodworth, Davis C
  • Marques, Helga
  • Boxerman, Jerrold L
  • Safriel, Yair
  • McKinstry, Robert C
  • Bokstein, Felix
  • Jain, Rajan
  • Chi, T Linda
  • Sorensen, A Gregory
  • Gilbert, Mark R
  • Barboriak, Daniel P
Int J Oncol 2015 Journal Article, cited 27 times
Website
Functional diffusion mapping (fDM) is a cancer imaging technique that quantifies voxelwise changes in apparent diffusion coefficient (ADC). Previous studies have shown value of fDMs in bevacizumab therapy for recurrent glioblastoma multiforme (GBM). The aim of the present study was to implement explicit criteria for diffusion MRI quality control and independently evaluate fDM performance in a multicenter clinical trial (RTOG 0625/ACRIN 6677). A total of 123 patients were enrolled in the current multicenter trial and signed institutional review board-approved informed consent at their respective institutions. MRI was acquired prior to and 8 weeks following therapy. A 5-point QC scoring system was used to evaluate DWI quality. fDM performance was evaluated according to the correlation of these metrics with PFS and OS at the first follow-up time-point. Results showed ADC variability of 7.3% in NAWM and 10.5% in CSF. A total of 68% of patients had usable DWI data and 47% of patients had high quality DWI data when also excluding patients that progressed before the first follow-up. fDM performance was improved by using only the highest quality DWI. High pre-treatment contrast enhancing tumor volume was associated with shorter PFS and OS. A high volume fraction of increasing ADC after therapy was associated with shorter PFS, while a high volume fraction of decreasing ADC was associated with shorter OS. In summary, DWI in multicenter trials are currently of limited value due to image quality. Improvements in consistency of image quality in multicenter trials are necessary for further advancement of DWI biomarkers.

Computer-aided detection of Pulmonary Nodules based on SVM in thoracic CT images

  • Eskandarian, Parinaz
  • Bagherzadeh, Jamshid
2015 Conference Proceedings, cited 12 times
Website
Computer-Aided diagnosis of Solitary Pulmonary Nodules using the method of X-ray CT images is the early detection of lung cancer. In this study, a computer-aided system for detection of pulmonary nodules on CT scan based support vector machine classifier is provided for the diagnosis of solitary pulmonary nodules. So at the first step, by data mining techniques, volume of data are reduced. Then divided by the area of the chest, the suspicious nodules are identified and eventually nodules are detected. In comparison with the threshold-based methods, support vector machine classifier to classify more accurately describes areas of the lungs. In this study, the false positive rate is reduced by combination of threshold with support vector machine classifier. Experimental results based on data from 147 patients with lung LIDC image database show that the proposed system is able to obtained sensitivity of 89.9% and false positive of 3.9 per scan. In comparison to previous systems, the proposed system demonstrates good performance.

LCD-OpenPACS: sistema integrado de telerradiologia com auxílio ao diagnóstico de nódulos pulmonares em exames de tomografia computadorizada

  • Firmino Filho, José Macêdo
2015 Thesis, cited 1 times
Website

Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network

  • Gayathri Devi, K
  • Radhakrishnan, R
Computational and Mathematical Methods in Medicine 2015 Journal Article, cited 5 times
Website

Radiomics: Images are more than pictures, they are data

  • Gillies, Robert J
  • Kinahan, Paul E
  • Hricak, Hedvig
Radiology 2015 Journal Article, cited 694 times
Website

Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma

  • Grove, Olya
  • Berglund, Anders E
  • Schabath, Matthew B
  • Aerts, Hugo JWL
  • Dekker, Andre
  • Wang, Hua
  • Velazquez, Emmanuel Rios
  • Lambin, Philippe
  • Gu, Yuhua
  • Balagurunathan, Yoganand
  • Eikman, E.
  • Gatenby, Robert A
  • Eschrich, S
  • Gillies, Robert J
PLoS One 2015 Journal Article, cited 87 times
Website
Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC>/=0.897; dynamic range, DR>/=0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.

Smooth extrapolation of unknown anatomy via statistical shape models

  • Grupp, RB
  • Chiang, H
  • Otake, Y
  • Murphy, RJ
  • Gordon, CR
  • Armand, M
  • Taylor, RH
2015 Conference Proceedings, cited 2 times
Website

User-guided graph reduction for fast image segmentation

  • Gueziri, Houssem-Eddine
  • McGuffin, Michael J
  • Laporte, Catherine
2015 Conference Proceedings, cited 2 times
Website
Graph-based segmentation methods such as the random walker (RW) are known to be computationally expensive. For high resolution images, user interaction with the algorithm is significantly affected. This paper introduces a novel seeding approach for graph-based segmentation that reduces computation time. Instead of marking foreground and background pixels, the user roughly marks the object boundary forming separate regions. The image pixels are then grouped into a hierarchy of increasingly large layers based on their distance from these markings. Next, foreground and background seeds are automatically generated according to the hierarchical layers of each region. The highest layers of the hierarchy are ignored leading to a significant graph reduction. Finally, validation experiments based on multiple automatically generated input seeds were carried out on a variety of medical images. Results show a significant gain in time for high resolution images using the new approach.

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

  • Guo, Wentian
  • Li, Hui
  • Zhu, Yitan
  • Lan, Li
  • Yang, Shengjie
  • Drukker, Karen
  • Morris, Elizabeth
  • Burnside, Elizabeth
  • Whitman, Gary
  • Giger, Maryellen L
  • Ji, Yuan
Journal of Medical Imaging 2015 Journal Article, cited 57 times
Website
Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

  • Guo, Wentian
  • Li, Hui
  • Zhu, Yitan
  • Lan, Li
  • Yang, Shengjie
  • Drukker, Karen
  • Morris, Elizabeth
  • Burnside, Elizabeth
  • Whitman, Gary
  • Giger, Maryellen L
  • Ji, Y.
  • TCGA Breast Phenotype Research Group
Journal of Medical Imaging 2015 Journal Article, cited 57 times
Website
Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.

A tool for lung nodules analysis based on segmentation and morphological operation

  • Gupta, Anindya
  • Martens, Olev
  • Le Moullec, Yannick
  • Saar, Tonis
2015 Conference Proceedings, cited 4 times
Website

Somatic mutations associated with MRI-derived volumetric features in glioblastoma

  • Gutman, David A
  • Dunn Jr, William D
  • Grossmann, Patrick
  • Cooper, Lee AD
  • Holder, Chad A
  • Ligon, Keith L
  • Alexander, Brian M
  • Aerts, Hugo JWL
Neuroradiology 2015 Journal Article, cited 45 times
Website
INTRODUCTION: MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM). METHODS: Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status. RESULTS: Our results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature. CONCLUSION: MRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine.

OPTIMISING DELINEATION ACCURACY OF TUMOURS IN PET FOR RADIOTHERAPY PLANNING USING BLIND DECONVOLUTION

  • Guvenis, A
  • Koc, A
Radiation Protection Dosimetry 2015 Journal Article, cited 3 times
Website
Positron emission tomography (PET) imaging has been proven to be useful in radiotherapy planning for the determination of the metabolically active regions of tumours. Delineation of tumours, however, is a difficult task in part due to high noise levels and the partial volume effects originating mainly from the low camera resolution. The goal of this work is to study the effect of blind deconvolution on tumour volume estimation accuracy for different computer-aided contouring methods. The blind deconvolution estimates the point spread function (PSF) of the imaging system in an iterative manner in a way that the likelihood of the given image being the convolution output is maximised. In this way, the PSF of the imaging system does not need to be known. Data were obtained from a NEMA NU-2 IQ-based phantom with a GE DSTE-16 PET/CT scanner. The artificial tumour diameters were 13, 17, 22, 28 and 37 mm with a target/background ratio of 4:1. The tumours were delineated before and after blind deconvolution. Student's two-tailed paired t-test showed a significant decrease in volume estimation error (p < 0.001) when blind deconvolution was used in conjunction with computer-aided delineation methods. A manual delineation confirmation demonstrated an improvement from 26 to 16 % for the artificial tumour of size 37 mm while an improvement from 57 to 15 % was noted for the small tumour of 13 mm. Therefore, it can be concluded that blind deconvolution of reconstructed PET images may be used to increase tumour delineation accuracy.

Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

  • Itakura, Haruka
  • Achrol, Achal S
  • Mitchell, Lex A
  • Loya, Joshua J
  • Liu, Tiffany
  • Westbroek, Erick M
  • Feroze, Abdullah H
  • Rodriguez, Scott
  • Echegaray, Sebastian
  • Azad, Tej D
Science translational medicine 2015 Journal Article, cited 90 times
Website

Quantitative imaging in radiation oncology: An emerging science and clinical service

  • Jaffray, DA
  • Chung, C
  • Coolens, C
  • Foltz, W
  • Keller, H
  • Menard, C
  • Milosevic, M
  • Publicover, J
  • Yeung, I
2015 Conference Proceedings, cited 9 times
Website

A low cost approach for brain tumor segmentation based on intensity modeling and 3D Random Walker

  • Kanas, Vasileios G
  • Zacharaki, Evangelia I
  • Davatzikos, Christos
  • Sgarbas, Kyriakos N
  • Megalooikonomou, Vasileios
Biomedical Signal Processing and Control 2015 Journal Article, cited 15 times
Website

Public data and open source tools for multi-assay genomic investigation of disease

  • Kannan, Lavanya
  • Ramos, Marcel
  • Re, Angela
  • El-Hachem, Nehme
  • Safikhani, Zhaleh
  • Gendoo, Deena MA
  • Davis, Sean
  • Gomez-Cabrero, David
  • Castelo, Robert
  • Hansen, Kasper D
Briefings in bioinformatics 2015 Journal Article, cited 28 times
Website

Mediator: A data sharing synchronization platform for heterogeneous medical image archives

  • Kathiravelu, Pradeeban
  • Sharma, Ashish
2015 Conference Proceedings, cited 4 times
Website

Malignancy prediction by using characteristic-based fuzzy sets: A preliminary study

  • Kaya, Aydin
  • Can, Ahmet Burak
2015 Conference Proceedings, cited 0 times
Website

Computer-aided detection of brain tumors using image processing techniques

  • Kazdal, Seda
  • Dogan, Buket
  • Camurcu, Ali Yilmaz
2015 Conference Proceedings, cited 3 times
Website

Lung Nodule Classification Using Deep Features in CT Images

  • Kumar, Devinder
  • Wong, Alexander
  • Clausi, David A
2015 Conference Proceedings, cited 114 times
Website

Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

  • Lassen, BC
  • Jacobs, C
  • Kuhnigk, JM
  • van Ginneken, B
  • van Rikxoort, EM
Physics in medicine and biology 2015 Journal Article, cited 25 times
Website
The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of subsolid nodules in clinical routine.

Discrimination of Benign and Malignant Suspicious BreastTumors Based on Semi-Quantitative DCE-MRI ParametersEmploying Support Vector Machine

  • Lavasani, Saeedeh Navaei
  • Kazerooni, Anahita Fathi
  • Rad, Hamidreza Saligheh
  • Gity, Masoumeh
Frontiers in Biomedical Technologies 2015 Journal Article, cited 4 times
Website

Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme

  • Lee, Joonsang
  • Narang, Shivali
  • Martinez, Juan
  • Rao, Ganesh
  • Rao, Arvind
PLoS One 2015 Journal Article, cited 14 times
Website

Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation

  • Lee, Joonsang
  • Narang, Shivali
  • Martinez, Juan J
  • Rao, Ganesh
  • Rao, Arvind
Journal of Medical Imaging 2015 Journal Article, cited 15 times
Website
We analyzed the spatial diversity of tumor habitats, regions with distinctly different intensity characteristics of a tumor, using various measurements of habitat diversity within tumor regions. These features were then used for investigating the association with a 12-month survival status in glioblastoma (GBM) patients and for the identification of epidermal growth factor receptor (EGFR)-driven tumors. T1 postcontrast and T2 fluid attenuated inversion recovery images from 65 GBM patients were analyzed in this study. A total of 36 spatial diversity features were obtained based on pixel abundances within regions of interest. Performance in both the classification tasks was assessed using receiver operating characteristic (ROC) analysis. For association with 12-month overall survival, area under the ROC curve was 0.74 with confidence intervals [0.630 to 0.858]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.59 and 0.75, respectively. For the identification of EGFR-driven tumors, the area under the ROC curve (AUC) was 0.85 with confidence intervals [0.750 to 0.945]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.76 and 0.83, respectively. Our findings suggest that these spatial habitat diversity features are associated with these clinical characteristics and could be a useful prognostic tool for magnetic resonance imaging studies of patients with GBM.

Patient-specific biomechanical model as whole-body CT image registration tool

  • Li, Mao
  • Miller, Karol
  • Joldes, Grand Roman
  • Doyle, Barry
  • Garlapati, Revanth Reddy
  • Kikinis, Ron
  • Wittek, Adam
Medical Image Analysis 2015 Journal Article, cited 15 times
Website

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features

  • Magdy, Eman
  • Zayed, Nourhan
  • Fakhr, Mahmoud
International Journal of Biomedical Imaging 2015 Journal Article, cited 6 times
Website

Automatic Electronic Cleansing in Computed Tomography Colonography Images using Domain Knowledge

  • Manjunath, KN
  • Siddalingaswamy, PC
  • Prabhu, GK
Asian Pacific Journal of Cancer Prevention 2015 Journal Article, cited 0 times

Determining the variability of lesion size measurements from ct patient data sets acquired under “no change” conditions

  • McNitt-Gray, Michael F
  • Kim, Grace Hyun
  • Zhao, Binsheng
  • Schwartz, Lawrence H
  • Clunie, David
  • Cohen, Kristin
  • Petrick, Nicholas
  • Fenimore, Charles
  • Lu, ZQ John
  • Buckler, Andrew J
Translational oncology 2015 Journal Article, cited 0 times

Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique

  • Melouah, Ahlem
2015 Book Section, cited 7 times
Website

Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset

  • Messay, Temesguen
  • Hardie, Russell C
  • Tuinstra, Timothy R
Medical Image Analysis 2015 Journal Article, cited 55 times
Website

Automated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images

  • Nabizadeh, Nooshin
2015 Thesis, cited 10 times
Website

Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features

  • Nabizadeh, Nooshin
  • Kubat, Miroslav
Computers & Electrical Engineering 2015 Journal Article, cited 85 times
Website
Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure owing to the variability and complexity of the location, size, shape, and texture of these lesions. Because of intensity similarities between brain lesions and normal tissues, some approaches make use of multi-spectral anatomical MRI scans. However, the time and cost restrictions for collecting multi-spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single-spectral anatomical MRI images. In this paper, we present a fully automatic system, which is able to detect slices that include tumor and, to delineate the tumor area. The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity. Moreover, we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers. This contribution fills the gap in the literature, as is the first to compare these sets of features for tumor segmentation applications. (C) 2015 Elsevier Ltd. All rights reserved.

Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers

  • Natteshan, NVS
  • Jothi, J Angel Arul
2015 Book Section, cited 8 times
Website

Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine

  • Navaei-Lavasani, Saeedeh
  • Fathi-Kazerooni, Anahita
  • Saligheh-Rad, Hamidreza
  • Gity, Masoumeh
Frontiers in Biomedical Technologies 2015 Journal Article, cited 4 times
Website

Machine Learning methods for Quantitative Radiomic Biomarkers

  • Parmar, C.
  • Grossmann, P.
  • Bussink, J.
  • Lambin, P.
  • Aerts, H. J.
Sci RepScientific reports 2015 Journal Article, cited 178 times
Website
Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 +/- 0.05, AUC = 0.65 +/- 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 +/- 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.

Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer

  • Parmar, C.
  • Leijenaar, R. T.
  • Grossmann, P.
  • Rios Velazquez, E.
  • Bussink, J.
  • Rietveld, D.
  • Rietbergen, M. M.
  • Haibe-Kains, B.
  • Lambin, P.
  • Aerts, H. J.
Sci RepScientific reports 2015 Journal Article, cited 0 times
Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic features across cancer types, we investigated cancer-specific radiomic feature clusters in four independent Lung and Head &Neck (H) cancer cohorts (in total 878 patients). Radiomic features were extracted from the pre-treatment computed tomography (CT) images. Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H RS = 0.92, p < 0.001). Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features. Strongest associations with clinical parameters: Prognosis Lung CI = 0.60 +/- 0.01, Prognosis H CI = 0.68 +/- 0.01; Lung histology AUC = 0.56 +/- 0.03, Lung stage AUC = 0.61 +/- 0.01, H HPV AUC = 0.58 +/- 0.03, H stage AUC = 0.77 +/- 0.02. Full utilization of these cancer-specific characteristics of image features may further improve radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice.

Genomics of Brain Tumor Imaging

  • Pope, Whitney B
Neuroimaging Clinics of North America 2015 Journal Article, cited 26 times
Website

Intelligent texture feature extraction and indexing for MRI image retrieval using curvelet and PCA with HTF

  • Rajakumar, K
  • Muttan, S
  • Deepa, G
  • Revathy, S
  • Priya, B Shanmuga
Advances in Natural and Applied Sciences 2015 Journal Article, cited 0 times
Website

Multi-fractal detrended texture feature for brain tumor classification

  • Reza, Syed MS
  • Mays, Randall
  • Iftekharuddin, Khan M
2015 Conference Proceedings, cited 5 times
Website

A scheme for patient study retrieval from 3D brain MR volumes

  • Sarathi, Mangipudi Partha
  • Ansari, MA
2015 Conference Proceedings, cited 1 times
Website

Mir-21–Sox2 Axis Delineates Glioblastoma Subtypes with Prognostic Impact

  • Sathyan, Pratheesh
  • Zinn, Pascal O
  • Marisetty, Anantha L
  • Liu, Bin
  • Kamal, Mohamed Mostafa
  • Singh, Sanjay K
  • Bady, Pierre
  • Lu, Li
  • Wani, Khalida M
  • Veo, Bethany L
The Journal of Neuroscience 2015 Journal Article, cited 18 times
Website

Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial

  • Schmainda, K. M.
  • Zhang, Z.
  • Prah, M.
  • Snyder, B. S.
  • Gilbert, M. R.
  • Sorensen, A. G.
  • Barboriak, D. P.
  • Boxerman, J. L.
Neuro Oncol 2015 Journal Article, cited 0 times
Website
Background. The study goal was to determine whether changes in relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) MRI are predictive of overall survival (OS) in patients with recurrent glioblastoma multiforme (GBM) when measured 2, 8, and 16 weeks after treatment initiation. Methods. Patients with recurrent GBM (37/123) enrolled in ACRIN 6677/RTOG 0625, a multicenter, randomized, phase II trial of bevacizumab with irinotecan or temozolomide, consented to DSC-MRI plus conventional MRI, 21 with DSC-MRI at baseline and at least 1 postbaseline scan. Contrast-enhancing regions of interest were determined semi-automatically using pre- and postcontrast T1-weighted images. Mean tumor rCBV normalized to white matter (nRCBV) and standardized rCBV (sRCBV) were determined for these regions of interest. The OS rates for patients with positive versus negative changes from baseline in nRCBV and sRCBV were compared using Wilcoxon rank-sum and Kaplan-Meier survival estimates with log-rank tests. Results. Patients surviving at least 1 year (OS-1) had significantly larger decreases in nRCBV at week 2 (P=.0451) and sRCBV at week 16 (P=.014). Receiver operating characteristic analysis found the percent changes of nRCBV and sRCBV at week 2 and sRCBV at week 16, but not rCBV data at week 8, to be good prognostic markers for OS-1. Patients with positive change from baseline rCBV had significantly shorter OS than those with negative change at both week 2 and week 16 (P=.0015 and P=.0067 for nRCBV and P=.0251 and P=.0004 for sRCBV, respectively). Conclusions. Early decreases in rCBV are predictive of improved survival in patients with recurrent GBM treated with bevacizumab.

An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy

  • Shen, Shiwen
  • Bui, Alex AT
  • Cong, Jason
  • Hsu, William
Computers in biology and medicine 2015 Journal Article, cited 31 times
Website
Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists' diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert. (C) 2014 Elsevier Ltd. All rights reserved.

Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas–Renal Cell Carcinoma (TCGA–RCC) Imaging Research Group

  • Shinagare, Atul B
  • Vikram, Raghu
  • Jaffe, Carl
  • Akin, Oguz
  • Kirby, Justin
  • Huang, Erich
  • Freymann, John
  • Sainani, Nisha I
  • Sadow, Cheryl A
  • Bathala, Tharakeswara K
  • Rubin, D. L.
  • Oto, A.
  • Heller, M. T.
  • Surabhi, V. R.
  • Katabathina, V.
  • Silverman, S. G.
Abdominal imaging 2015 Journal Article, cited 47 times
Website
PURPOSE: To investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS: This multi-institutional, multi-reader study included 103 patients (77 men; median age 59 years, range 34-79) with ccRCC examined with CT in 81 patients, MRI in 19, and both CT and MRI in three; images were downloaded from The Cancer Imaging Archive, an NCI-funded project for genome-mapping and analyses. Imaging features [size (mm), margin (well-defined or ill-defined), composition (solid or cystic), necrosis (for solid tumors: 0%, 1%-33%, 34%-66% or >66%), growth pattern (endophytic, <50% exophytic, or >/=50% exophytic), and calcification (present, absent, or indeterminate)] were reviewed independently by three readers blinded to mutational data. The association of imaging features with mutational status (VHL, BAP1, PBRM1, SETD2, KDM5C, and MUC4) was assessed. RESULTS: Median tumor size was 49 mm (range 14-162 mm), 73 (71%) tumors had well-defined margins, 98 (95%) tumors were solid, 95 (92%) showed presence of necrosis, 46 (45%) had >/=50% exophytic component, and 18 (19.8%) had calcification. VHL (n = 52) and PBRM1 (n = 24) were the most common mutations. BAP1 mutation was associated with ill-defined margin and presence of calcification (p = 0.02 and 0.002, respectively, Pearson's chi (2) test); MUC4 mutation was associated with an exophytic growth pattern (p = 0.002, Mann-Whitney U test). CONCLUSIONS: BAP1 mutation was associated with ill-defined tumor margins and presence of calcification; MUC4 mutation was associated with exophytic growth. Given the known prognostic implications of BAP1 and MUC4 mutations, these results support using radiogenomics to aid in prognostication and management.

A Novel Noise Removal Method for Lung CT SCAN Images Using Statistical Filtering Techniques

  • Sivakumar, S
  • Chandrasekar, C
International Journal of Algorithms Design and Analysis 2015 Journal Article, cited 0 times

Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images

  • Steed, TC
  • Treiber, JM
  • Patel, KS
  • Taich, Z
  • White, NS
  • Treiber, ML
  • Farid, N
  • Carter, BS
  • Dale, AM
  • Chen, CC
American Journal of Neuroradiology 2015 Journal Article, cited 12 times
Website
BACKGROUND AND PURPOSE: Robust, automated segmentation algorithms are required for quantitative analysis of large imaging datasets. We developed an automated method that identifies and labels brain tumor-associated pathology by using an iterative probabilistic voxel labeling using k-nearest neighbor and Gaussian mixture model classification. Our purpose was to develop a segmentation method which could be applied to a variety of imaging from The Cancer Imaging Archive. MATERIALS AND METHODS: Images from 2 sets of 15 randomly selected subjects with glioblastoma from The Cancer Imaging Archive were processed by using the automated algorithm. The algorithm-defined tumor volumes were compared with those segmented by trained operators by using the Dice similarity coefficient. RESULTS: Compared with operator volumes, algorithm-generated segmentations yielded mean Dice similarities of 0.92 +/- 0.03 for contrast-enhancing volumes and 0.84 +/- 0.09 for FLAIR hyperintensity volumes. These values compared favorably with the means of Dice similarity coefficients between the operator-defined segmentations: 0.92 +/- 0.03 for contrast-enhancing volumes and 0.92 +/- 0.05 for FLAIR hyperintensity volumes. Robust segmentations can be achieved when only postcontrast T1WI and FLAIR images are available. CONCLUSIONS: Iterative probabilistic voxel labeling defined tumor volumes that were highly consistent with operator-defined volumes. Application of this algorithm could facilitate quantitative assessment of neuroimaging from patients with glioblastoma for both research and clinical indications.

A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme

  • Upadhaya, T
  • Morvan, Y
  • Stindel, E
  • Le Reste, P-J
  • Hatt, M
IRBM 2015 Journal Article, cited 11 times
Website
In Glioblastoma Multiforme (GBM) image-derived features ("radiomics") could help in individualizing patient management. Simple geometric features of tumors (necrosis, edema, active tumor) and first-order statistics in Magnetic Resonance Imaging (MRI) are used in clinical practice. However, these features provide limited characterization power because they do not incorporate spatial information and thus cannot differentiate patterns. The aim of this work is to develop and evaluate a methodological framework dedicated to building a prognostic model based on heterogeneity textural features of multimodal MRI sequences (T1. T1-contrast. T2 and FLAIR) in GBM. The proposed workflow consists in i) registering the available 3D multimodal MR images and segmenting the tumor volume, ii) extracting image features such as heterogeneity metrics and iii) building a prognostic model by selecting, ranking and combining optimal features through machine learning (Support Vector Machine). This framework was applied to 40 histologically proven GBM patients with the endpoint being overall survival (OS) classified as above or below the median survival (15 months). The models combining features from a maximum of two modalities were evaluated using leave-one-out cross-validation (LOOCV). A classification accuracy of 90% (sensitivity 85%, specificity 95%) was obtained by combining features from T1 pre-contrast and T1 post-contrast sequences. Our results suggest that several textural features in each MR sequence have prognostic value in GBM. (C) 2015 AGBM. Published by Elsevier Masson SAS. All rights reserved.

Prognostic value of multimodal MRI tumor features in Glioblastoma multiforme using textural features analysis

  • Upadhaya, Taman
  • Morvan, Yannick
  • Stindel, Eric
  • Reste, Le
  • Hatt, Mathieu
2015 Conference Proceedings, cited 12 times
Website

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

  • Vallières, Martin
  • Freeman, CR
  • Skamene, SR
  • El Naqa, I
Physics in medicine and biology 2015 Journal Article, cited 199 times
Website
This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDGPET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 +/- 0.002, a sensitivity of 0.955 +/- 0.006, and a specificity of 0.926 +/- 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features

  • Velazquez, Emmanuel Rios
  • Meier, Raphael
  • Dunn Jr, William D
  • Alexander, Brian
  • Wiest, Roland
  • Bauer, Stefan
  • Gutman, David A
  • Reyes, Mauricio
  • Aerts, Hugo JWL
Sci RepScientific reports 2015 Journal Article, cited 42 times
Website
Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.

Data Analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative

  • Wang, Weisheng
  • Luo, Jiawei
  • Yang, Xuedong
  • Lin, Hongli
Academic radiology 2015 Journal Article, cited 5 times
Website
RATIONALE AND OBJECTIVES: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) is the largest publicly available computed tomography (CT) image reference data set of lung nodules. In this article, a comprehensive data analysis of the data set and a uniform data model are presented with the purpose of facilitating potential researchers to have an in-depth understanding to and efficient use of the data set in their lung cancer-related investigations. MATERIALS AND METHODS: A uniform data model was designed for representation and organization of various types of information contained in different source data files. A software tool was developed for the processing and analysis of the database, which 1) automatically aligns and graphically displays the nodule outlines marked manually by radiologists onto the corresponding CT images; 2) extracts diagnostic nodule characteristics annotated by radiologists; 3) calculates a variety of nodule image features based on the outlines of nodules, including diameter, volume, and degree of roundness, and so forth; 4) integrates all the extracted nodule information into the uniform data model and stores it in a common and easy-to-access data format; and 5) analyzes and summarizes various feature distributions of nodules in several different categories. Using this data processing and analysis tool, all 1018 CT scans from the data set were processed and analyzed for their statistical distribution. RESULTS: The information contained in different source data files with different formats was extracted and integrated into a new and uniform data model. Based on the new data model, the statistical distributions of nodules in terms of nodule geometric features and diagnostic characteristics were summarized. In the LIDC/IDRI data set, 2655 nodules >/=3 mm, 5875 nodules <3 mm, and 7411 non-nodules are identified, respectively. Among the 2655 nodules, 1) 775, 488, 481, and 911 were marked by one, two, three, or four radiologists, respectively; 2) most of nodules >/=3 mm (85.7%) have a diameter <10.0 mm with the mean value of 6.72 mm; and 3) 10.87%, 31.4%, 38.8%, 16.4%, and 2.6% of nodules were assessed with a malignancy score of 1, 2, 3, 4, and 5, respectively. CONCLUSIONS: This study demonstrates the usefulness of the proposed software tool to the potential users for an in-depth understanding of the LIDC/IDRI data set, therefore likely to be beneficial to their future investigations. The analysis results also demonstrate the distribution diversity of nodules characteristics, therefore being useful as a reference resource for assessing the performance of a new and existing nodule detection and/or segmentation schemes.

Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival

  • Wangaryattawanich, Pattana
  • Hatami, Masumeh
  • Wang, Jixin
  • Thomas, Ginu
  • Flanders, Adam
  • Kirby, Justin
  • Wintermark, Max
  • Huang, Erich S.
  • Bakhtiari, Ali Shojaee
  • Luedi, Markus M.
  • Hashmi, Syed S.
  • Rubin, Daniel L.
  • Chen, James Y.
  • Hwang, Scott N.
  • Freymann, John
  • Holder, Chad A.
  • Zinn, Pascal O.
  • Colen, Rivka R.
Neuro-oncology 2015 Journal Article, cited 40 times
Website
Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM.We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image.Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P &lt; .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm3 and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively).Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.

A 3D semi-automated co-segmentation method for improved tumor target delineation in 3D PET/CT imaging

  • Yu, Zexi
  • Bui, Francis M
  • Babyn, Paul
2015 Conference Proceedings, cited 1 times
Website

Effect of color visualization and display hardware on the visual assessment of pseudocolor medical images

  • Zabala-Travers, Silvina
  • Choi, Mina
  • Cheng, Wei-Chung
  • Badano, Aldo
Medical physics 2015 Journal Article, cited 4 times
Website
PURPOSE: Even though the use of color in the interpretation of medical images has increased significantly in recent years, the ad hoc manner in which color is handled and the lack of standard approaches have been associated with suboptimal and inconsistent diagnostic decisions with a negative impact on patient treatment and prognosis. The purpose of this study is to determine if the choice of color scale and display device hardware affects the visual assessment of patterns that have the characteristics of functional medical images. METHODS: Perfusion magnetic resonance imaging (MRI) was the basis for designing and performing experiments. Synthetic images resembling brain dynamic-contrast enhanced MRI consisting of scaled mixtures of white, lumpy, and clustered backgrounds were used to assess the performance of a rainbow ("jet"), a heated black-body ("hot"), and a gray ("gray") color scale with display devices of different quality on the detection of small changes in color intensity. The authors used a two-alternative, forced-choice design where readers were presented with 600 pairs of images. Each pair consisted of two images of the same pattern flipped along the vertical axis with a small difference in intensity. Readers were asked to select the image with the highest intensity. Three differences in intensity were tested on four display devices: a medical-grade three-million-pixel display, a consumer-grade monitor, a tablet device, and a phone. RESULTS: The estimates of percent correct show that jet outperformed hot and gray in the high and low range of the color scales for all devices with a maximum difference in performance of 18% (confidence intervals: 6%, 30%). Performance with hot was different for high and low intensity, comparable to jet for the high range, and worse than gray for lower intensity values. Similar performance was seen between devices using jet and hot, while gray performance was better for handheld devices. Time of performance was shorter with jet. CONCLUSIONS: Our findings demonstrate that the choice of color scale and display hardware affects the visual comparative analysis of pseudocolor images. Follow-up studies in clinical settings are being considered to confirm the results with patient images.

Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

  • Zayed, Nourhan
  • Elnemr, Heba A
International Journal of Biomedical Imaging 2015 Journal Article, cited 30 times
Website
The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.

Two-stage fusion set selection in multi-atlas-based image segmentation

  • Zhao, Tingting
  • Ruan, Dan
2015 Conference Proceedings, cited 0 times
Website
Conventional multi-atlas-based segmentation demands pairwise full-fledged registration between each atlas image and the target image, which leads to high computational cost and poses great challenge in the new era of big data. On the other hand, only the most relevant atlases should contribute to final label fusion. In this work, we introduce a two-stage fusion set selection method by first trimming the atlas collection into an augmented subset based on a low-cost registration and the preliminary relevance metric, followed by a further refinement based on a full-fledged registration and the corresponding relevance metric. A statistical inference model is established to relate the preliminary and the refined relevance metrics, and a proper augmented subset size is derived based on it. Empirical evidence supported the inference model, and end-to-end performance assessment demonstrated the proposed scheme to be computationally efficient without compromising segmentation accuracy.

A statistical method for lung tumor segmentation uncertainty in PET images based on user inference

  • Zheng, Chaojie
  • Wang, Xiuying
  • Feng, Dagan
2015 Conference Proceedings, cited 0 times
Website

Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma

  • Zhu, Yitan
  • Li, Hui
  • Guo, Wentian
  • Drukker, Karen
  • Lan, Li
  • Giger, Maryellen L
  • Ji, Yuan
Sci RepScientific reports 2015 Journal Article, cited 52 times
Website
Magnetic Resonance Imaging (MRI) has been routinely used for the diagnosis and treatment of breast cancer. However, the relationship between the MRI tumor phenotypes and the underlying genetic mechanisms remains under-explored. We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with MRI data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas. Quantitative MRI phenotypes of tumors (such as tumor size, shape, margin, and blood flow kinetics) were associated with their corresponding molecular profiles (including DNA mutation, miRNA expression, protein expression, pathway gene expression and copy number variation). We found that transcriptional activities of various genetic pathways were positively associated with tumor size, blurred tumor margin, and irregular tumor shape and that miRNA expressions were associated with the tumor size and enhancement texture, but not with other types of radiomic phenotypes. We provide all the association findings as a resource for the research community (available at http://compgenome.org/Radiogenomics/). These findings pave potential paths for the discovery of genetic mechanisms regulating specific tumor phenotypes and for improving MRI techniques as potential non-invasive approaches to probe the cancer molecular status.

Robust Computer-Aided Detection of Pulmonary Nodules from Chest Computed Tomography

  • Abduh, Zaid
  • Wahed, Manal Abdel
  • Kadah, Yasser M
Journal of Medical Imaging and Health Informatics 2016 Journal Article, cited 5 times
Website
Detection of pulmonary nodules in chest computed tomography scans play an important role in the early diagnosis of lung cancer. A simple yet effective computer-aided detection system is developed to distinguish pulmonary nodules in chest CT scans. The proposed system includes feature extraction, normalization, selection and classification steps. One hundred forty-nine gray level statistical features are extracted from selected regions of interest. A min-max normalization method is used followed by sequential forward feature selection technique with logistic regression model used as criterion function that selected an optimal set of five features for classification. The classification step was done using nearest neighbor and support vector machine (SVM) classifiers with separate training and testing sets. Several measures to evaluate the system performance were used including the area under ROC curve (AUC), sensitivity, specificity, precision, accuracy, F1 score and Cohen-k factor. Excellent performance with high sensitivity and specificity is reported using data from two reference datasets as compared to previous work.

Adaptive Enhancement Technique for Cancerous Lung Nodule in Computed Tomography Images

  • AbuBaker, Ayman A
International Journal of Engineering and Technology 2016 Journal Article, cited 1 times
Website
Diagnosis the Computed Tomography Images (CT-Images) may take a lot of time by the radiologist. This will increase the radiologist fatigue and may miss some of the cancerous lung nodule lesions. Therefore, an adaptive local enhancement Computer Aided Diagnosis (CAD) system is proposed. The proposed technique is design to enhance the suspicious cancerous regions in the CT-Images. The visual characteristics of the cancerous lung nodules in the CT-Images was the main criteria in designing this technique. The new approach is divided into two phases, pre-processing phase and image enhancement phase. The image noise reduction, thresholding process, and extraction the lung regions are considered as a pre-processing phase. Whereas, the new adaptive local enhancement method for the CTImages were implemented as a second phase. The proposed algorithm is tested and evaluated on 42 normal and cancerous lung nodule CT-Images. As a result, this new approach can efficiently enhance the cancerous lung nodules by 25% comparing with the original images.

Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC

  • Aerts, Hugo JWL
  • Grossmann, Patrick
  • Tan, Yongqiang
  • Oxnard, Geoffrey G
  • Rizvi, Naiyer
  • Schwartz, Lawrence H
  • Zhao, Binsheng
Sci RepScientific reports 2016 Journal Article, cited 40 times
Website
Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan (p > 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74-0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean +/- std: ICC = 0.96 +/- 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.

Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface

  • Ahmadvand, Payam
  • Duggan, Nóirín
  • Bénard, François
  • Hamarneh, Ghassan
2016 Conference Proceedings, cited 4 times
Website

Increased robustness in reference region model analysis of DCE MRI using two‐step constrained approaches

  • Ahmed, Zaki
  • Levesque, Ives R
Magnetic Resonance in Medicine 2016 Journal Article, cited 1 times
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Quantitative assessment of colorectal morphology: Implications for robotic colonoscopy

  • Alazmani, A
  • Hood, A
  • Jayne, D
  • Neville, A
  • Culmer, P
Medical engineering & physics 2016 Journal Article, cited 11 times
Website
This paper presents a method of characterizing the distribution of colorectal morphometrics. It uses three-dimensional region growing and topological thinning algorithms to determine and visualize the luminal volume and centreline of the colon, respectively. Total and segmental lengths, diameters, volumes, and tortuosity angles were then quantified. The effects of body orientations on these parameters were also examined. Variations in total length were predominately due to differences in the transverse colon and sigmoid segments, and did not significantly differ between body orientations. The diameter of the proximal colon was significantly larger than the distal colon, with the largest value at the ascending and cecum segments. The volume of the transverse colon was significantly the largest, while those of the descending colon and rectum were the smallest. The prone position showed a higher frequency of high angles and consequently found to be more torturous than the supine position. This study yielded a method for complete segmental measurements of healthy colorectal anatomy and its tortuosity. The transverse and sigmoid colons were the major determinant in tortuosity and morphometrics between body orientations. Quantitative understanding of these parameters may potentially help to facilitate colonoscopy techniques, accuracy of polyp spatial distribution detection, and design of novel endoscopic devices.

Breast Cancer Response Prediction in Neoadjuvant Chemotherapy Treatment Based on Texture Analysis

  • Ammar, Mohammed
  • Mahmoudi, Saïd
  • Stylianos, Drisis
Procedia Computer Science 2016 Journal Article, cited 2 times
Website
MRI modality is one of the most usual techniques used for diagnosis and treatment planning of breast cancer. The aim of this study is to prove that texture based feature techniques such as co-occurrence matrix features extracted from MRI images can be used to quantify response of tumor treatment. To this aim, we use a dataset composed of two breast MRI examinations for 9 patients. Three of them were responders and six non responders. The first exam was achieved before the initiation of the treatment (baseline). The later one was done after the first cycle of the chemo treatment (control). A set of selected texture parameters have been selected and calculated for each exam. These selected parameters are: Cluster Shade, dissimilarity, entropy, homogeneity. The p-values estimated for the pathologic complete responders pCR and non pathologic complete responders pNCR patients prove that homogeneity (P-value=0.027) and cluster shade (P-value=0.0013) are the more relevant parameters related to pathologic complete responders pCR.

Imaging Genomics in Glioblastoma Multiforme: A Predictive Tool for Patients Prognosis, Survival, and Outcome

  • Anil, Rahul
  • Colen, Rivka R
Magnetic Resonance Imaging Clinics of North America 2016 Journal Article, cited 3 times
Website
The integration of imaging characteristics and genomic data has started a new trend in approach toward management of glioblastoma (GBM). Many ongoing studies are investigating imaging phenotypical signatures that could explain more about the behavior of GBM and its outcome. The discovery of biomarkers has played an adjuvant role in treating and predicting the outcome of patients with GBM. Discovering these imaging phenotypical signatures and dysregulated pathways/genes is needed and required to engineer treatment based on specific GBM manifestations. Characterizing these parameters will establish well-defined criteria so researchers can build on the treatment of GBM through personal medicine.

Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

  • Anirudh, Rushil
  • Thiagarajan, Jayaraman J
  • Bremer, Timo
  • Kim, Hyojin
2016 Conference Proceedings, cited 33 times
Website

Brain tumour classification using two-tier classifier with adaptive segmentation technique

  • Anitha, V
  • Murugavalli, S
IET Computer Vision 2016 Journal Article, cited 46 times
Website
A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.

Imaging genomics in cancer research: limitations and promises

  • Bai, Harrison X
  • Lee, Ashley M
  • Yang, Li
  • Zhang, Paul
  • Davatzikos, Christos
  • Maris, John M
  • Diskin, Sharon J
The British journal of radiology 2016 Journal Article, cited 28 times
Website

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

  • Bakas, S.
  • Zeng, K.
  • Sotiras, A.
  • Rathore, S.
  • Akbari, H.
  • Gaonkar, B.
  • Rozycki, M.
  • Pati, S.
  • Davatzikos, C.
Brainlesion 2016 Journal Article, cited 49 times
Website
We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

A New Adaptive-Weighted Fusion Rule for Wavelet based PET/CT Fusion

  • Barani, R
  • Sumathi, M
International Journal of Signal Processing, Image Processing and Pattern Recognition 2016 Journal Article, cited 1 times
Website
In recent years the Wavelet Transform (WT) had an important role in various applications of signal and image processing. In Image Processing, WT is more useful in many domains like image denoising, feature segmentation, compression, restoration, image fusion, etc. In WT based image fusion, initially the source images are decomposed into approximation and detail coefficients and followed by combining the coefficients using the suitable fusion rules. The resultant fused image is reconstructed by applying inverse WT on the combined coefficients. This paper proposes a new adaptive fusion rule for combining the approximation coefficients of CT and PET images. The Excellency of the proposed fusion rule is stamped by measuring the image information metrics, EOG, SD and ENT on the decomposed approximation coefficients. On the other hand, the detail coefficients are combined using several existing fusion rules. The resultant fused images are quantitatively analyzed using the non-reference image quality, image fusion and error metrics. The analysis declares that the newly proposed fusion rule is more suitable for extracting the complementary information from CT and PET images and also produces the fused image which is rich in content with good contrast and sharpness.

Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data

  • Benedict, Stanley H
  • Hoffman, Karen
  • Martel, Mary K
  • Abernethy, Amy P
  • Asher, Anthony L
  • Capala, Jacek
  • Chen, Ronald C
  • Chera, Bhisham
  • Couch, Jennifer
  • Deye, James
International Journal of Radiation Oncology• Biology• Physics 2016 Journal Article, cited 0 times

Pulmonary nodule detection using a cascaded SVM classifier

  • Bergtholdt, Martin
  • Wiemker, Rafael
  • Klinder, Tobias
2016 Conference Proceedings, cited 9 times
Website
Automatic detection of lung nodules from chest CT has been researched intensively over the last decades resulting also in several commercial products. However, solutions are adopted only slowly into daily clinical routine as many current CAD systems still potentially miss true nodules while at the same time generating too many false positives (FP). While many earlier approaches had to rely on rather few cases for development, larger databases become now available and can be used for algorithmic development. In this paper, we address the problem of lung nodule detection via a cascaded SVM classifier. The idea is to sequentially perform two classification tasks in order to select from an extremely large pool of potential candidates the few most likely ones. As the initial pool is allowed to contain thousands of candidates, very loose criteria could be applied during this pre-selection. In this way, the chances that a true nodule is falsely rejected as a candidate are reduced significantly. The final algorithm is trained and tested on the full LIDC/IDRI database. Comparison is done against two previously published CAD systems. Overall, the algorithm achieved sensitivity of 0.859 at 2.5 FP/volume where the other two achieved sensitivity values of 0.321 and 0.625, respectively. On low dose data sets, only slight increase in the number of FP/volume was observed, while the sensitivity was not affected.

G-DOC Plus–an integrative bioinformatics platform for precision medicine

  • Bhuvaneshwar, Krithika
  • Belouali, Anas
  • Singh, Varun
  • Johnson, Robert M
  • Song, Lei
  • Alaoui, Adil
  • Harris, Michael A
  • Clarke, Robert
  • Weiner, Louis M
  • Gusev, Yuriy
BMC bioinformatics 2016 Journal Article, cited 14 times
Website

Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage

  • Burnside, Elizabeth S
  • Drukker, Karen
  • Li, Hui
  • Bonaccio, Ermelinda
  • Zuley, Margarita
  • Ganott, Marie
  • Net, Jose M
  • Sutton, Elizabeth J
  • Brandt, Kathleen R
  • Whitman, Gary J
Cancer 2016 Journal Article, cited 28 times
Website

Selección de un algoritmo para la clasificación de Nódulos Pulmonares Solitarios

  • Castro, Arelys Rivero
  • Correa, Luis Manuel Cruz
  • Lezcano, Jeffrey Artiles
Revista Cubana de Informática Médica 2016 Journal Article, cited 0 times
Website

GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes

  • Chaddad, Ahmad
  • Desrosiers, Christian
  • Toews, Matthew
2016 Conference Proceedings, cited 4 times
Website

Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme

  • Chaddad, Ahmad
  • Desrosiers, Christian
  • Toews, Matthew
2016 Conference Proceedings, cited 11 times
Website
Image texture features are effective at characterizing the microstructure of cancerous tissues. This paper proposes predicting the survival times of glioblastoma multiforme (GBM) patients using texture features extracted in multi-contrast brain MRI images. Texture features are derived locally from contrast enhancement, necrosis and edema regions in T1-weighted post-contrast and fluid-attenuated inversion-recovery (FLAIR) MRIs, based on the gray-level co-occurrence matrix representation. A statistical analysis based on the Kaplan-Meier method and log-rank test is used to identify the texture features related with the overall survival of GBM patients. Results are presented on a dataset of 39 GBM patients. For FLAIR images, four features (Energy, Correlation, Variance and Inverse of Variance) from contrast enhancement regions and a feature (Homogeneity) from edema regions were shown to be associated with survival times (p-value <; 0.01). Likewise, in T1-weighted images, three features (Energy, Correlation, and Variance) from contrast enhancement regions were found to be useful for predicting the overall survival of GBM patients. These preliminary results show the advantages of texture analysis in predicting the prognosis of GBM patients from multi-contrast brain MRI.

Phenotypic characterization of glioblastoma identified through shape descriptors

  • Chaddad, Ahmad
  • Desrosiers, Christian
  • Toews, Matthew
2016 Conference Proceedings, cited 4 times
Website
This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.

Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images

  • Chaddad, Ahmad
  • Tanougast, Camel
Brain Informatics 2016 Journal Article, cited 28 times
Website

Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients

  • Chaddad, Ahmad
  • Tanougast, Camel
Medical & biological engineering & computing 2016 Journal Article, cited 16 times
Website
GBM is a markedly heterogeneous brain tumor consisting of three main volumetric phenotypes identifiable on magnetic resonance imaging: necrosis (vN), active tumor (vAT), and edema/invasion (vE). The goal of this study is to identify the three glioblastoma multiforme (GBM) phenotypes using a texture-based gray-level co-occurrence matrix (GLCM) approach and determine whether the texture features of phenotypes are related to patient survival. MR imaging data in 40 GBM patients were analyzed. Phenotypes vN, vAT, and vE were segmented in a preprocessing step using 3D Slicer for rigid registration by T1-weighted imaging and corresponding fluid attenuation inversion recovery images. The GBM phenotypes were segmented using 3D Slicer tools. Texture features were extracted from GLCM of GBM phenotypes. Thereafter, Kruskal-Wallis test was employed to select the significant features. Robust predictive GBM features were identified and underwent numerous classifier analyses to distinguish phenotypes. Kaplan-Meier analysis was also performed to determine the relationship, if any, between phenotype texture features and survival rate. The simulation results showed that the 22 texture features were significant with p value < 0.05. GBM phenotype discrimination based on texture features showed the best accuracy, sensitivity, and specificity of 79.31, 91.67, and 98.75 %, respectively. Three texture features derived from active tumor parts: difference entropy, information measure of correlation, and inverse difference were statistically significant in the prediction of survival, with log-rank p values of 0.001, 0.001, and 0.008, respectively. Among 22 features examined, three texture features have the ability to predict overall survival for GBM patients demonstrating the utility of GLCM analyses in both the diagnosis and prognosis of this patient population.

Primer for Image Informatics in Personalized Medicine

  • Chang, Young Hwan
  • Foley, Patrick
  • Azimi, Vahid
  • Borkar, Rohan
  • Lefman, Jonathan
Procedia Engineering 2016 Journal Article, cited 0 times
Website

“Big data” and “open data”: What kind of access should researchers enjoy?

  • Chatellier, Gilles
  • Varlet, Vincent
  • Blachier-Poisson, Corinne
Thérapie 2016 Journal Article, cited 0 times

Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma

  • Choi, Yoon Seong
  • Ahn, Sung Soo
  • Kim, Dong Wook
  • Chang, Jong Hee
  • Kang, Seok-Gu
  • Kim, Eui Hyun
  • Kim, Se Hoon
  • Rim, Tyler Hyungtaek
  • Lee, Seung-Koo
Radiology 2016 Journal Article, cited 18 times
Website
Purpose To investigate the incremental prognostic value of apparent diffusion coefficient (ADC) histogram analysis over oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in patients with glioblastoma and the correlation between ADC parameters and MGMT status. Materials and Methods This retrospective study was approved by institutional review board, and informed consent was waived. A total of 112 patients with glioblastoma were divided into training (74 patients) and test (38 patients) sets. Overall survival (OS) and progression-free survival (PFS) was analyzed with ADC parameters, MGMT status, and other clinical factors. Multivariate Cox regression models with and without ADC parameters were constructed. Model performance was assessed with c index and receiver operating characteristic curve analyses for 12- and 16-month OS and 12-month PFS in the training set and validated in the test set. ADC parameters were compared according to MGMT status for the entire cohort. Results By using ADC parameters, the c indices and diagnostic accuracies for 12- and 16-month OS and 12-month PFS in the models showed significant improvement, with the exception of c indices in the models for PFS (P < .05 for all) in the training set. In the test set, the diagnostic accuracy was improved by using ADC parameters and was significant, with the 25th and 50th percentiles of ADC for 16-month OS (P = .040 and P = .047) and the 25th percentile of ADC for 12-month PFS (P = .026). No significant correlation was found between ADC parameters and MGMT status. Conclusion ADC histogram analysis had incremental prognostic value over MGMT promoter methylation status in patients with glioblastoma. ((c)) RSNA, 2016 Online supplemental material is available for this article.

ST3GAL1-associated transcriptomic program in glioblastoma tumor growth, invasion, and prognosis

  • Chong, Yuk Kien
  • Sandanaraj, Edwin
  • Koh, Lynnette WH
  • Thangaveloo, Moogaambikai
  • Tan, Melanie SY
  • Koh, Geraldene RH
  • Toh, Tan Boon
  • Lim, Grace GY
  • Holbrook, Joanna D
  • Kon, Oi Lian
  • Nadarajah, M.
  • Ng, I.
  • Ng, W. H.
  • Tan, N. S.
  • Lim, K. L.
  • Tang, C.
  • Ang, B. T.
Journal of the National Cancer Institute 2016 Journal Article, cited 16 times
Website
BACKGROUND: Cell surface sialylation is associated with tumor cell invasiveness in many cancers. Glioblastoma is the most malignant primary brain tumor and is highly infiltrative. ST3GAL1 sialyltransferase gene is amplified in a subclass of glioblastomas, and its role in tumor cell self-renewal remains unexplored. METHODS: Self-renewal of patient glioma cells was evaluated using clonogenic, viability, and invasiveness assays. ST3GAL1 was identified from differentially expressed genes in Peanut Agglutinin-stained cells and validated in REMBRANDT (n = 390) and Gravendeel (n = 276) clinical databases. Gene set enrichment analysis revealed upstream processes. TGFbeta signaling on ST3GAL1 transcription was assessed using chromatin immunoprecipitation. Transcriptome analysis of ST3GAL1 knockdown cells was done to identify downstream pathways. A constitutively active FoxM1 mutant lacking critical anaphase-promoting complex/cyclosome ([APC/C]-Cdh1) binding sites was used to evaluate ST3Gal1-mediated regulation of FoxM1 protein. Finally, the prognostic role of ST3Gal1 was determined using an orthotopic xenograft model (3 mice groups comprising nontargeting and 2 clones of ST3GAL1 knockdown in NNI-11 [8 per group] and NNI-21 [6 per group]), and the correlation with patient clinical information. All statistical tests on patients' data were two-sided; other P values below are one-sided. RESULTS: High ST3GAL1 expression defines an invasive subfraction with self-renewal capacity; its loss of function prolongs survival in a mouse model established from mesenchymal NNI-11 (P < .001; groups of 8 in 3 arms: nontargeting, C1, and C2 clones of ST3GAL1 knockdown). ST3GAL1 transcriptomic program stratifies patient survival (hazard ratio [HR] = 2.47, 95% confidence interval [CI] = 1.72 to 3.55, REMBRANDT P = 1.92 x 10(-)(8); HR = 2.89, 95% CI = 1.94 to 4.30, Gravendeel P = 1.05 x 10(-)(1)(1)), independent of age and histology, and associates with higher tumor grade and T2 volume (P = 1.46 x 10(-)(4)). TGFbeta signaling, elevated in mesenchymal patients, correlates with high ST3GAL1 (REMBRANDT gliomacor = 0.31, P = 2.29 x 10(-)(1)(0); Gravendeel gliomacor = 0.50, P = 3.63 x 10(-)(2)(0)). The transcriptomic program upon ST3GAL1 knockdown enriches for mitotic cell cycle processes. FoxM1 was identified as a statistically significantly modulated gene (P = 2.25 x 10(-)(5)) and mediates ST3Gal1 signaling via the (APC/C)-Cdh1 complex. CONCLUSIONS: The ST3GAL1-associated transcriptomic program portends poor prognosis in glioma patients and enriches for higher tumor grades of the mesenchymal molecular classification. We show that ST3Gal1-regulated self-renewal traits are crucial to the sustenance of glioblastoma multiforme growth.

Reproducing 2D breast mammography images with 3D printed phantoms

  • Clark, Matthew
  • Ghammraoui, Bahaa
  • Badal, Andreu
2016 Conference Proceedings, cited 2 times
Website

Extended Modality Propagation: Image Synthesis of Pathological Cases

  • N. Cordier
  • H. Delingette
  • M. Le
  • N. Ayache
IEEE Transactions on Medical Imaging 2016 Journal Article, cited 18 times
Website

Primary lung tumor segmentation from PET–CT volumes with spatial–topological constraint

  • Cui, Hui
  • Wang, Xiuying
  • Lin, Weiran
  • Zhou, Jianlong
  • Eberl, Stefan
  • Feng, Dagan
  • Fulham, Michael
International journal of computer assisted radiology and surgery 2016 Journal Article, cited 14 times
Website

Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype

  • Czarnek, Nicholas M
  • Clark, Kal
  • Peters, Katherine B
  • Collins, Leslie M
  • Mazurowski, Maciej A
2016 Conference Proceedings, cited 3 times
Website

Directional local ternary quantized extrema pattern: A new descriptor for biomedical image indexing and retrieval

  • Deep, G
  • Kaur, L
  • Gupta, S
Engineering Science and Technology, an International Journal 2016 Journal Article, cited 9 times
Website

Local mesh ternary patterns: a new descriptor for MRI and CT biomedical image indexing and retrieval

  • Deep, G
  • Kaur, L
  • Gupta, S
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2016 Journal Article, cited 3 times
Website

Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data

  • DeGrandchamp, Joseph B
  • Whisenant, Jennifer G
  • Arlinghaus, Lori R
  • Abramson, VG
  • Yankeelov, Thomas E
  • Cárdenas-Rodríguez, Julio
2016 Conference Proceedings, cited 5 times
Website

Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

  • Desseroit, Marie-Charlotte
  • Visvikis, Dimitris
  • Tixier, Florent
  • Majdoub, Mohamed
  • Perdrisot, Rémy
  • Guillevin, Rémy
  • Le Rest, Catherine Cheze
  • Hatt, Mathieu
European journal of nuclear medicine and molecular imaging 2016 Journal Article, cited 34 times
Website

Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma

  • Dunn, William D Jr
  • Aerts, Hugo J W L
  • Cooper, Lee A
  • Holder, Chad A
  • Hwang, Scott N
  • Jaffe, Carle C
  • Brat, Daniel J
  • Jain, Rajan
  • Flanders, Adam E
  • Zinn, Pascal O
  • Colen, Rivka R
  • Gutman, David A
J Neuroimaging Psychiatry Neurol 2016 Journal Article, cited 0 times
Website
Background: Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis. Methods: Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression. Results: We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman's r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features. Conclusion: Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.

Decision forests for learning prostate cancer probability maps from multiparametric MRI

  • Ehrenberg, Henry R
  • Cornfeld, Daniel
  • Nawaf, Cayce B
  • Sprenkle, Preston C
  • Duncan, James S
2016 Conference Proceedings, cited 2 times
Website

A COMPUTER AIDED DIAGNOSIS SYSTEM FOR LUNG CANCER DETECTION USING SVM

  • EMİRZADE, ERKAN
2016 Thesis, cited 1 times
Website
Computer aided diagnosis is starting to be implemented broadly in the diagnosis and detection of many varieties of abnormities acquired during various imaging procedures. The main aim of the CAD systems is to increase the accuracy and decrease the time of diagnoses, while the general achievement for CAD systems are to find the place of nodules and to determine the characteristic features of the nodule. As lung cancer is one of the fatal and leading cancer types, there has been plenty of studies for the usage of the CAD systems to detect lung cancer. Yet, the CAD systems need to be developed a lot in order to identify the different shapes of nodules, lung segmentation and to have higher level of sensitivity, specifity and accuracy. This challenge is the motivation of this study in implementation of CAD system for lung cancer detection. In the study, LIDC database is used which comprises of an image set of lung cancer thoracic documented CT scans. The presented CAD system consists of CT image reading, image pre-processing, segmentation, feature extraction and classification steps. To avoid losing important features, the CT images were read as a raw form in DICOM file format. Then, filtration and enhancement techniques were used as an image processing. Otsu’s algorithm, edge detection and morphological operations are applied for the segmentation, following the feature extractions step. Finally, support vector machine with Gaussian RBF is utilized for the classification step which is widely used as a supervised classifier.

Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network

  • Farahani, Keyvan
  • Kalpathy-Cramer, Jayashree
  • Chenevert, Thomas L
  • Rubin, Daniel L
  • Sunderland, John J
  • Nordstrom, Robert J
  • Buatti, John
  • Hylton, Nola
Tomography 2016 Journal Article, cited 2 times
Website
The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image assessment parameters through network-wide cooperative projects. To more effectively use the cooperative power of the network in conducting computational challenges in benchmarking of tools and methods and collaborative projects in analytical assessment of imaging technologies, the QIN Challenge Task Force has developed policies and procedures to enhance the value of these activities by developing guidelines and leveraging NCI resources to help their administration and manage dissemination of results. Challenges and Collaborative Projects (CCPs) are further divided into technical and clinical CCPs. As the first NCI network to engage in CCPs, we anticipate a variety of CCPs to be conducted by QIN teams in the coming years. These will be aimed to benchmark advanced software tools for clinical decision support, explore new imaging biomarkers for therapeutic assessment, and establish consensus on a range of methods and protocols in support of the use of quantitative imaging to predict and assess response to cancer therapy.

Signal intensity analysis of ecological defined habitat in soft tissue sarcomas to predict metastasis development

  • Farhidzadeh, Hamidreza
  • Chaudhury, Baishali
  • Scott, Jacob G
  • Goldgof, Dmitry B
  • Hall, Lawrence O
  • Gatenby, Robert A
  • Gillies, Robert J
  • Raghavan, Meera
2016 Conference Proceedings, cited 6 times
Website

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research

  • Fedorov, Andriy
  • Clunie, David
  • Ulrich, Ethan
  • Bauer, Christian
  • Wahle, Andreas
  • Brown, Bartley
  • Onken, Michael
  • Riesmeier, Jörg
  • Pieper, Steve
  • Kikinis, Ron
PeerJ 2016 Journal Article, cited 20 times
Website

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research

  • Fedorov, Andriy
  • Clunie, David
  • Ulrich, Ethan
  • Bauer, Christian
  • Wahle, Andreas
  • Brown, Bartley
  • Onken, Michael
  • Riesmeier, Jörg
  • Pieper, Steve
  • Kikinis, Ron
  • Buatti, John
  • Beichel, Reinhard R
PeerJ 2016 Journal Article, cited 20 times
Website
Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM((R))) international standard and free open-source software. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). Supporting tools for data analysis and DICOM conversion were made available as free open-source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open-source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard.

HEVC optimizations for medical environments

  • Fernández, DG
  • Del Barrio, AA
  • Botella, Guillermo
  • García, Carlos
  • Meyer-Baese, Uwe
  • Meyer-Baese, Anke
2016 Conference Proceedings, cited 5 times
Website

Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy

  • Firmino, Macedo
  • Angelo, Giovani
  • Morais, Higor
  • Dantas, Marcel R
  • Valentim, Ricardo
Biomedical engineering online 2016 Journal Article, cited 63 times
Website
BACKGROUND: CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. METHODS: The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. RESULTS: The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. CONCLUSIONS: From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.

Computer-aided nodule assessment and risk yield risk management of adenocarcinoma: the future of imaging?

  • Foley, Finbar
  • Rajagopalan, Srinivasan
  • Raghunath, Sushravya M
  • Boland, Jennifer M
  • Karwoski, Ronald A
  • Maldonado, Fabien
  • Bartholmai, Brian J
  • Peikert, Tobias
2016 Conference Proceedings, cited 7 times
Website

Lung nodule detection in CT images using deep convolutional neural networks

  • Golan, Rotem
  • Jacob, Christian
  • Denzinger, Jörg
2016 Conference Proceedings, cited 26 times
Website
Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

  • Greenspan, Hayit
  • van Ginneken, Bram
  • Summers, Ronald M
IEEE Transactions on Medical Imaging 2016 Journal Article, cited 395 times
Website

Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma

  • Grossmann, Patrick
  • Gutman, David A
  • Dunn, William D
  • Holder, Chad A
  • Aerts, Hugo JWL
BMC cancer 2016 Journal Article, cited 21 times
Website

Using Deep Learning for Pulmonary Nodule Detection & Diagnosis

  • Gruetzemacher, Richard
  • Gupta, Ashish
2016 Conference Paper, cited 0 times

A generalized graph reduction framework for interactive segmentation of large images

  • Gueziri, Houssem-Eddine
  • McGuffin, Michael J
  • Laporte, Catherine
Computer Vision and Image Understanding 2016 Journal Article, cited 5 times
Website
The speed of graph-based segmentation approaches, such as random walker (RW) and graph cut (GC), depends strongly on image size. For high-resolution images, the time required to compute a segmentation based on user input renders interaction tedious. We propose a novel method, using an approximate contour sketched by the user, to reduce the graph before passing it on to a segmentation algorithm such as RW or GC. This enables a significantly faster feedback loop. The user first draws a rough contour of the object to segment. Then, the pixels of the image are partitioned into "layers" (corresponding to different scales) based on their distance from the contour. The thickness of these layers increases with distance to the contour according to a Fibonacci sequence. An initial segmentation result is rapidly obtained after automatically generating foreground and background labels according to a specifically selected layer; all vertices beyond this layer are eliminated, restricting the segmentation to regions near the drawn contour. Further foreground background labels can then be added by the user to refine the segmentation. All iterations of the graph-based segmentation benefit from a reduced input graph, while maintaining full resolution near the object boundary. A user study with 16 participants was carried out for RW segmentation of a multi-modal dataset of 22 medical images, using either a standard mouse or a stylus pen to draw the contour. Results reveal that our approach significantly reduces the overall segmentation time compared with the status quo approach (p < 0.01). The study also shows that our approach works well with both input devices. Compared to super-pixel graph reduction, our approach provides full resolution accuracy at similar speed on a high-resolution benchmark image with both RW and GC segmentation methods. However, graph reduction based on super-pixels does not allow interactive correction of clustering errors. Finally, our approach can be combined with super-pixel clustering methods for further graph reduction, resulting in even faster segmentation. (C) 2016 Elsevier Inc. All rights reserved.

Feature selection and patch-based segmentation in MRI for prostate radiotherapy

  • Guinin, M
  • Ruan, S
  • Dubray, B
  • Massoptier, L
  • Gardin, I
2016 Conference Proceedings, cited 0 times
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Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images

  • Gupta, Suneet
  • Porwal, Rabins
International Journal of Biomedical Imaging 2016 Journal Article, cited 10 times
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Publishing descriptions of non-public clinical datasets: proposed guidance for researchers, repositories, editors and funding organisations

  • Hrynaszkiewicz, Iain
  • Khodiyar, Varsha
  • Hufton, Andrew L
  • Sansone, Susanna-Assunta
Research Integrity and Peer Review 2016 Journal Article, cited 8 times
Website
Sharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles-where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. Using the journal Scientific Data as a case study, we propose and show examples of changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).

Computer-aided grading of gliomas based on local and global MRI features

  • Hsieh, Kevin Li-Chun
  • Lo, Chung-Ming
  • Hsiao, Chih-Jou
Computer methods and programs in biomedicine 2016 Journal Article, cited 13 times
Website
BACKGROUND AND OBJECTIVES: A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. METHODS: The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model. RESULTS: Performances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement kappa = 0.698, p < 0.001. CONCLUSIONS: Numerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.

A neural network approach to lung nodule segmentation

  • Hu, Yaoxiu
  • Menon, Prahlad G
2016 Conference Proceedings, cited 1 times
Website

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

  • Huang, Wei
  • Chen, Yiyi
  • Fedorov, Andriy
  • Li, Xia
  • Jajamovich, Guido H
  • Malyarenko, Dariya I
  • Aryal, Madhava P
  • LaViolette, Peter S
  • Oborski, Matthew J
  • O'Sullivan, Finbarr
Tomography: a journal for imaging research 2016 Journal Article, cited 21 times
Website

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

  • Jajamovich, Guido H
  • Valiathan, Chandni R
  • Cristescu, Razvan
  • Somayajula, Sangeetha
Journal of neuro-oncology 2016 Journal Article, cited 4 times
Website
Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

Cloud-based NoSQL open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research

  • Junior, José Raniery Ferreira
  • Oliveira, Marcelo Costa
  • de Azevedo-Marques, Paulo Mazzoncini
Journal of Digital Imaging 2016 Journal Article, cited 14 times
Website

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features

  • Kalpathy-Cramer, J.
  • Mamomov, A.
  • Zhao, B.
  • Lu, L.
  • Cherezov, D.
  • Napel, S.
  • Echegaray, S.
  • Rubin, D.
  • McNitt-Gray, M.
  • Lo, P.
  • Sieren, J. C.
  • Uthoff, J.
  • Dilger, S. K.
  • Driscoll, B.
  • Yeung, I.
  • Hadjiiski, L.
  • Cha, K.
  • Balagurunathan, Y.
  • Gillies, R.
  • Goldgof, D.
Tomography: a journal for imaging research 2016 Journal Article, cited 19 times
Website

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

  • Kalpathy-Cramer, Jayashree
  • Zhao, Binsheng
  • Goldgof, Dmitry
  • Gu, Yuhua
  • Wang, Xingwei
  • Yang, Hao
  • Tan, Yongqiang
  • Gillies, Robert
  • Napel, Sandy
Journal of Digital Imaging 2016 Journal Article, cited 18 times
Website
Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 mul to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

Identification of Tumor area from Brain MR Image

  • Kasım, Ömer
  • Kuzucuoğlu, Ahmet Emin
2016 Conference Proceedings, cited 1 times
Website

“Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment

  • Katrib, Amal
  • Hsu, William
  • Bui, Alex
  • Xing, Yi
Quantitative Biology 2016 Journal Article, cited 0 times

A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images

  • Kaur, Taranjit
  • Saini, Barjinder Singh
  • Gupta, Savita
Neural Computing and Applications 2016 Journal Article, cited 1 times
Website

Preliminary Detection and Analysis of Lung Cancer on CT images using MATLAB: A Cost-effective Alternative

  • Khan, Md Daud Hossain
  • Ahmed, Mansur
  • Bach, Christian
Journal of Biomedical Engineering and Medical Imaging 2016 Journal Article, cited 0 times

Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning

  • Korfiatis, Panagiotis
  • Kline, Timothy L
  • Erickson, Bradley J
Tomography: a journal for imaging research 2016 Journal Article, cited 16 times
Website

Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives

  • Krishnamurthy, Senthilkumar
  • Narasimhan, Ganesh
  • Rengasamy, Umamaheswari
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2016 Journal Article, cited 17 times
Website

An Level Set Evolution Morphology Based Segmentation of Lung Nodules and False Nodule Elimination by 3D Centroid Shift and Frequency Domain DC Constant Analysis

  • Krishnamurthy, Senthilkumar
  • Narasimhan, Ganesh
  • Rengasamy, Umamaheswari
International Journal of u- and e- Service, Science and Technology 2016 Journal Article, cited 0 times
Website
A Level Set Evolution with Morphology (LSEM) based segmentation algorithm is proposed in this work to segment all the possible lung nodules from a series of CT scan images. All the segmented nodule candidates were not cancerous in nature. Initially the vessels and calcifications were also segmented as nodule candidates. The structural feature analysis was carried out to remove the vessels. The nodules with more centroid shift in the consecutive slices were eliminated since malignant nodule’s resultant position did not usually deviate. The calcifications were eliminated by frequency domain analysis. DC constant of nodule candidates were computed in frequency domain. The nodule candidates with high DC constant value could be the calcifications as the calcification patterns were homogeneous in nature. This algorithm was applied on a database of 40 patient cases with 58 malignant nodules. The algorithms proposed in this paper precisely detected 55 malignant nodules and failed to detect 3 with a sensitivity of 95%. Further, this algorithm correctly eliminated 778 tissue clusters that were initially segmented as nodules, however, 79 non-malignant tissue clusters were detected as malignant nodules. Therefore, the false positive of this algorithm was 1.98 per patient.

Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

  • Le, Trong-Ngoc
  • Bao, Pham The
  • Huynh, Hieu Trung
BioMed Research International 2016 Journal Article, cited 5 times
Website
Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI) image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN), which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the "ground truth." Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.

Texture feature ratios from relative CBV maps of perfusion MRI are associated with patient survival in glioblastoma

  • Lee, J
  • Jain, R
  • Khalil, K
  • Griffith, B
  • Bosca, R
  • Rao, G
  • Rao, A
American Journal of Neuroradiology 2016 Journal Article, cited 27 times
Website
BACKGROUND AND PURPOSE: Texture analysis has been applied to medical images to assist in tumor tissue classification and characterization. In this study, we obtained textural features from parametric (relative CBV) maps of dynamic susceptibility contrast-enhanced MR images in glioblastoma and assessed their relationship with patient survival. MATERIALS AND METHODS: MR perfusion data of 24 patients with glioblastoma from The Cancer Genome Atlas were analyzed in this study. One- and 2D texture feature ratios and kinetic textural features based on relative CBV values in the contrast-enhancing and nonenhancing lesions of the tumor were obtained. Receiver operating characteristic, Kaplan-Meier, and multivariate Cox proportional hazards regression analyses were used to assess the relationship between texture feature ratios and overall survival. RESULTS: Several feature ratios are capable of stratifying survival in a statistically significant manner. These feature ratios correspond to homogeneity (P = .008, based on the log-rank test), angular second moment (P = .003), inverse difference moment (P = .013), and entropy (P = .008). Multivariate Cox proportional hazards regression analysis showed that homogeneity, angular second moment, inverse difference moment, and entropy from the contrast-enhancing lesion were significantly associated with overall survival. For the nonenhancing lesion, skewness and variance ratios of relative CBV texture were associated with overall survival in a statistically significant manner. For the kinetic texture analysis, the Haralick correlation feature showed a P value close to .05. CONCLUSIONS: Our study revealed that texture feature ratios from contrast-enhancing and nonenhancing lesions and kinetic texture analysis obtained from perfusion parametric maps provide useful information for predicting survival in patients with glioblastoma.

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

  • Li, Hui
  • Zhu, Yitan
  • Burnside, Elizabeth S
  • Drukker, Karen
  • Hoadley, Katherine A
  • Fan, Cheng
  • Conzen, Suzanne D
  • Whitman, Gary J
  • Sutton, Elizabeth J
  • Net, Jose M
Radiology 2016 Journal Article, cited 103 times
Website

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

  • Li, Hui
  • Zhu, Yitan
  • Burnside, Elizabeth S
  • Huang, Erich
  • Drukker, Karen
  • Hoadley, Katherine A
  • Fan, Cheng
  • Conzen, Suzanne D
  • Zuley, Margarita
  • Net, Jose M
npj Breast Cancer 2016 Journal Article, cited 63 times
Website

Biomechanical model for computing deformations for whole‐body image registration: A meshless approach

  • Li, Mao
  • Miller, Karol
  • Joldes, Grand Roman
  • Kikinis, Ron
  • Wittek, Adam
International Journal for Numerical Methods in Biomedical Engineering 2016 Journal Article, cited 13 times
Website

Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas

  • Liu, T T
  • Achrol, A S
  • Mitchell, L A
  • Du, W A
  • Loya, J J
  • Rodriguez, S A
  • Feroze, A
  • Westbroek, E M
  • Yeom, K W
  • Stuart, J M
  • Chang, S D
  • Harsh, G R 4th
  • Rubin, D L
American Journal of Neuroradiology 2016 Journal Article, cited 6 times
Website
BACKGROUND AND PURPOSE: Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma. The purpose of this study was to characterize glioblastoma lesions by identifying MR imaging voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics, and clinical outcomes. MATERIALS AND METHODS: Preoperative T1 anatomic MR images of 384 patients with glioblastomas were obtained from 2 independent cohorts (n = 253 from the Stanford University Medical Center for training and n = 131 from The Cancer Genome Atlas for validation). An automated computational image-analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival of >17 months) and poor (overall survival of <11 months) survival groups identified in the training cohort were used to classify patients in The Cancer Genome Atlas cohort into 2 brain-location groups, for which clinical features, messenger RNA expression, and copy number changes were compared to elucidate the biologic basis of tumors located in different brain regions. RESULTS: Tumors in the right occipitotemporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both, log-rank P < .05) and had larger tumor volume compared with tumors in other locations. Tumors in the right periatrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies. CONCLUSIONS: Voxel-based location in glioblastoma is associated with patient outcome and may have a potential role for guiding personalized treatment.

Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment

  • Liu, Tiffany T.
  • Achrol, Achal S.
  • Mitchell, Lex A.
  • Rodriguez, Scott A.
  • Feroze, Abdullah
  • Michael Iv
  • Kim, Christine
  • Chaudhary, Navjot
  • Gevaert, Olivier
  • Stuart, Josh M.
  • Harsh, Griffith R.
  • Chang, Steven D.
  • Rubin, Daniel L.
Neuro-oncology 2016 Journal Article, cited 15 times
Website
Background. In previous clinical trials, antiangiogenic therapies such as bevacizumab did not show efficacy in patients with newly diagnosed glioblastoma (GBM). This may be a result of the heterogeneity of GBM, which has a variety of imaging-based phenotypes and gene expression patterns. In this study, we sought to identify a phenotypic subtype of GBM patients who have distinct tumor-image features and molecular activities and who may benefit from antiangiogenic therapies.Methods. Quantitative image features characterizing subregions of tumors and the whole tumor were extracted from preoperative and pretherapy perfusion magnetic resonance (MR) images of 117 GBM patients in 2 independent cohorts. Unsupervised consensus clustering was performed to identify robust clusters of GBM in each cohort. Cox survival and gene set enrichment analyses were conducted to characterize the clinical significance and molecular pathway activities of the clusters. The differential treatment efficacy of antiangiogenic therapy between the clusters was evaluated.Results. A subgroup of patients with elevated perfusion features was identified and was significantly associated with poor patient survival after accounting for other clinical covariates (P values <.01; hazard ratios > 3) consistently found in both cohorts. Angiogenesis and hypoxia pathways were enriched in this subgroup of patients, suggesting the potential efficacy of antiangiogenic therapy. Patients of the angiogenic subgroups pooled from both cohorts, who had chemotherapy information available, had significantly longer survival when treated with antiangiogenic therapy (log-rank P=.022).Conclusions. Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.

Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes

  • Lo, Wei-Ching
  • Li, Wen
  • Jones, Ella F
  • Newitt, David C
  • Kornak, John
  • Wilmes, Lisa J
  • Esserman, Laura J
  • Hylton, Nola M
PLoS One 2016 Journal Article, cited 7 times
Website

Automatic lung nodule classification with radiomics approach

  • Ma, Jingchen
  • Wang, Qian
  • Ren, Yacheng
  • Hu, Haibo
  • Zhao, Jun
2016 Conference Proceedings, cited 10 times
Website

An improved method of colon segmentation in computed tomography colonography images using domain knowledge

  • Manjunath, KN
  • Siddalingaswamy, PC
  • Gopalakrishna Prabhu, K
Journal of Medical Imaging and Health Informatics 2016 Journal Article, cited 0 times

Tumor Growth in the Brain: Complexity and Fractality

  • Martín-Landrove, Miguel
  • Brú, Antonio
  • Rueda-Toicen, Antonio
  • Torres-Hoyos, Francisco
2016 Book Section, cited 1 times
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Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data

  • Mazurowski, Maciej A
  • Czarnek, Nicholas M
  • Collins, Leslie M
  • Peters, Katherine B
  • Clark, Kal L
2016 Conference Proceedings, cited 6 times
Website

Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study

  • McCann, Stephanie M
  • Jiang, Yulei
  • Fan, Xiaobing
  • Wang, Jianing
  • Antic, Tatjana
  • Prior, Fred
  • VanderWeele, David
  • Oto, Aytekin
American Journal of Roentgenology 2016 Journal Article, cited 11 times
Website

Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging

  • Mehrtash, Alireza
  • Gupta, Sandeep N
  • Shanbhag, Dattesh
  • Miller, James V
  • Kapur, Tina
  • Fennessy, Fiona M
  • Kikinis, Ron
  • Fedorov, Andriy
Journal of Medical Imaging 2016 Journal Article, cited 6 times
Website
Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant ([Formula: see text]) and extravascular extracellular volume fraction ([Formula: see text]) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of [Formula: see text] and [Formula: see text] values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

  • Meier, Raphael
  • Knecht, Urspeter
  • Loosli, Tina
  • Bauer, Stefan
  • Slotboom, Johannes
  • Wiest, Roland
  • Reyes, Mauricio
Sci RepScientific reports 2016 Journal Article, cited 26 times
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Detection of Lung Cancer Nodule on CT scan Images by using Region Growing Method

  • Mhetre, Rajani R
  • Sache, Rukhsana G
International Journal of Current Trends in Engineering & Research 2016 Journal Article, cited 0 times
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Optimization Methods for Medical Image Super Resolution Reconstruction

  • Moustafa, Marwa
  • Ebied, Hala M
  • Helmy, Ashraf
  • Nazamy, Taymoor M
  • Tolba, Mohamed F
2016 Book Section, cited 0 times
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Forschungsanwendungen in der digitalen Radiologie

  • Müller, H
  • Hanbury, A
Der Radiologe 2016 Journal Article, cited 1 times
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Security of Multi-frame DICOM Images Using XOR Encryption Approach

  • Natsheh, QN
  • Li, B
  • Gale, AG
Procedia Computer Science 2016 Journal Article, cited 4 times
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Big biomedical image processing hardware acceleration: A case study for K-means and image filtering

  • Neshatpour, Katayoun
  • Koohi, Arezou
  • Farahmand, Farnoud
  • Joshi, Rajiv
  • Rafatirad, Setareh
  • Sasan, Avesta
  • Homayoun, Houman
IEEE International Symposium on Circuits and Systems (ISCAS) 2016 Journal Article, cited 7 times
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Efficient Colorization of Medical Imaging based on Colour Transfer Method

  • Nida, Nudrat
  • Khan, Muhammad Usman Ghani
Proceedings of the Pakistan Academy of Sciences: Pakistan Academy of Sciences: B. Life and Environmental Sciences 2016 Journal Article, cited 0 times
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A FRAMEWORK FOR AUTOMATIC COLORIZATION OF MEDICAL IMAGING

  • Nida, Nudrat
  • Sharif, Muhammad
  • Khan, Muhammad Usman Ghani
  • Yasmin, Mussarat
  • Fernandes, Steven Lawrence
IIOABJ 2016 Journal Article, cited 3 times
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Image descriptors in radiology images: a systematic review

  • Nogueira, Mariana A
  • Abreu, Pedro Henriques
  • Martins, Pedro
  • Machado, Penousal
  • Duarte, Hugo
  • Santos, João
Artificial Intelligence Review 2016 Journal Article, cited 8 times
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Development of Clinically-Informed 3D Tumor Models for Microwave Imaging Applications

  • Oliveira, Barbara
  • O'Halloran, Martin
  • Conceicao, Raquel
  • Glavin, Martin
  • Jones, Edward
IEEE Antennas and Wireless Propagation Letters 2016 Journal Article, cited 8 times
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Uma Proposta Para Utilização De Workflows Científicos Para A Definição De Pipelines Para A Recuperação De Imagens Médicas Por Conteúdo Em Um Ambiente Distribuído

  • Oliveira, Luis Fernando Milano
2016 Thesis, cited 1 times
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Content dependent intra mode selection for medical image compression using HEVC

  • Parikh, S
  • Ruiz, D
  • Kalva, H
  • Fern, G
2016 Conference Proceedings, cited 3 times
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Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications

  • Parks, Connie L
  • Monson, Keith L
Journal of Digital Imaging 2016 Journal Article, cited 3 times
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An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features

  • Patil, Ravindra
  • Mahadevaiah, Geetha
  • Dekker, Andre
Tomography: a journal for imaging research 2016 Journal Article, cited 2 times
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Lung cancer incidence and mortality in National Lung Screening Trial participants who underwent low-dose CT prevalence screening: a retrospective cohort analysis of a randomised, multicentre, diagnostic screening trial

  • Patz Jr, Edward F
  • Greco, Erin
  • Gatsonis, Constantine
  • Pinsky, Paul
  • Kramer, Barnett S
  • Aberle, Denise R
The Lancet Oncology 2016 Journal Article, cited 67 times
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Auto Diagnostics of Lung Nodules Using Minimal Characteristics Extraction Technique

  • Peña, Diego M
  • Luo, Shouhua
  • Abdelgader, Abdeldime
Diagnostics 2016 Journal Article, cited 6 times
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Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network

  • Petousis, Panayiotis
  • Han, Simon X
  • Aberle, Denise
  • Bui, Alex AT
Artificial intelligence in medicine 2016 Journal Article, cited 13 times
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Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

  • Prasanna, Prateek
  • Patel, Jay
  • Partovi, Sasan
  • Madabhushi, Anant
  • Tiwari, Pallavi
European Radiology 2016 Journal Article, cited 45 times
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Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study

  • Qian, Xiaohua
  • Tan, Hua
  • Zhang, Jian
  • Liu, Keqin
  • Yang, Tielin
  • Wang, Maode
  • Debinskie, Waldemar
  • Zhao, Weilin
  • Chan, Michael D
  • Zhou, Xiaobo
Oncotarget 2016 Journal Article, cited 8 times
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Integrative Analysis of mRNA, microRNA, and Protein Correlates of Relative Cerebral Blood Volume Values in GBM Reveals the Role for Modulators of Angiogenesis and Tumor Proliferation

  • Rao, Arvind
  • Manyam, Ganiraju
  • Rao, Ganesh
  • Jain, Rajan
Cancer Informatics 2016 Journal Article, cited 5 times
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A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma

  • Rao, Arvind
  • Rao, Ganesh
  • Gutman, David A
  • Flanders, Adam E
  • Hwang, Scott N
  • Rubin, Daniel L
  • Colen, Rivka R
  • Zinn, Pascal O
  • Jain, Rajan
  • Wintermark, Max
Journal of neurosurgery 2016 Journal Article, cited 19 times
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Automated pulmonary nodule CT image characterization in lung cancer screening

  • Reeves, Anthony P
  • Xie, Yiting
  • Jirapatnakul, Artit
International journal of computer assisted radiology and surgery 2016 Journal Article, cited 19 times
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DEMARCATE: Density-based Magnetic Resonance Image Clustering for Assessing Tumor Heterogeneity in Cancer

  • Saha, Abhijoy
  • Banerjee, Sayantan
  • Kurtek, Sebastian
  • Narang, Shivali
  • Lee, Joonsang
  • Rao, Ganesh
  • Martinez, Juan
  • Bharath, Karthik
  • Rao, Arvind UK
  • Baladandayuthapani, Veerabhadran
NeuroImage: Clinical 2016 Journal Article, cited 4 times
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Wwox–Brca1 interaction: role in DNA repair pathway choice

  • Schrock, MS
  • Batar, B
  • Lee, J
  • Druck, T
  • Ferguson, B
  • Cho, JH
  • Akakpo, K
  • Hagrass, H
  • Heerema, NA
  • Xia, F
Oncogene 2016 Journal Article, cited 12 times
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Wireless Distributive Personal Communication for Early Detection of Collateral Cancer Using Optimized Machine Learning Methodology

  • Sivaganesan, D
Wireless Personal Communications 2016 Journal Article, cited 1 times
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Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis

  • Song, Jiangdian
  • Liu, Zaiyi
  • Zhong, Wenzhao
  • Huang, Yanqi
  • Ma, Zelan
  • Dong, Di
  • Liang, Changhong
  • Tian, Jie
Sci RepScientific reports 2016 Journal Article, cited 14 times
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MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study

  • Song, Sung Eun
  • Bae, Min Sun
  • Chang, Jung Min
  • Cho, Nariya
  • Ryu, Han Suk
  • Moon, Woo Kyung
Acta Radiologica 2016 Journal Article, cited 2 times
Website
Background Human epidermal growth factor receptor 2-positive (HER2+) breast cancer has two distinct subtypes according to hormone receptor (HR) status. Survival, pattern of recurrence, and treatment response differ between HR-/HER2+ and HR+/HER2+ cancers. Purpose To investigate imaging and clinicopathologic features of HER2+ cancers and their correlation with HR expression. Material and Methods Between 2011 and 2013, 252 consecutive patients with 252 surgically confirmed HER2+ cancers (125 HR- and 127 HR+) were included. Two experienced breast radiologists blinded to the clinicopathologic findings reviewed the mammograms and magnetic resonance (MR) images using the BI-RADS lexicon. Tumor kinetic features were acquired by computer-aided detection (CAD). The imaging and clinicopathologic features of 125 HR-/HER2+ cancers were compared with those of 127 HR+/HER2+ cancers. Association between the HR status and each feature was assessed. Results Multiple logistic regression analysis showed that circumscribed mass margin (odds ratio [OR], 4.73; P < 0.001), associated non-mass enhancement (NME) on MR images (OR, 3.29; P = 0.001), high histologic grade (OR, 3.89; P = 0.002), high Ki-67 index (OR, 3.06; P = 0.003), and older age (OR, 2.43; P = 0.006) remained independent indicators associated with HR-/HER2+ cancers. Between the two HER2+ subtypes, there were no differences in mammographic imaging presentations and calcification features and MR kinetic features by a CAD. Conclusion HER2+ breast cancers have different MR imaging (MRI) phenotypes and clinicopathologic feature according to HR status. MRI features related to HR and HER2 status have the potential to be used for the diagnosis and treatment decisions in HER2+ breast cancer patients.

Differential localization of glioblastoma subtype: implications on glioblastoma pathogenesis

  • Steed, Tyler C
  • Treiber, Jeffrey M
  • Patel, Kunal
  • Ramakrishnan, Valya
  • Merk, Alexander
  • Smith, Amanda R
  • Carter, Bob S
  • Dale, Anders M
  • Chow, LM
  • Chen, Clark C
Oncotarget 2016 Journal Article, cited 8 times
Website

Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations

  • Stoll, Markus
  • Stoiber, Eva Maria
  • Grimm, Sarah
  • Debus, Jürgen
  • Bendl, Rolf
  • Giske, Kristina
PLoS One 2016 Journal Article, cited 2 times
Website
PURPOSE: Intensity modulated radiation therapy (IMRT) of head and neck tumors allows a precise conformation of the high-dose region to clinical target volumes (CTVs) while respecting dose limits to organs a risk (OARs). Accurate patient setup reduces translational and rotational deviations between therapy planning and therapy delivery days. However, uncertainties in the shape of the CTV and OARs due to e.g. small pose variations in the highly deformable anatomy of the head and neck region can still compromise the dose conformation. Routinely applied safety margins around the CTV cause higher dose deposition in adjacent healthy tissue and should be kept as small as possible. MATERIALS AND METHODS: In this work we evaluate and compare three approaches for margin generation 1) a clinically used approach with a constant isotropic 3 mm margin, 2) a previously proposed approach adopting a spatial model of the patient and 3) a newly developed approach adopting a biomechanical model of the patient. All approaches are retrospectively evaluated using a large patient cohort of over 500 fraction control CT images with heterogeneous pose changes. Automatic methods for finding landmark positions in the control CT images are combined with a patient specific biomechanical finite element model to evaluate the CTV deformation. RESULTS: The applied methods for deformation modeling show that the pose changes cause deformations in the target region with a mean motion magnitude of 1.80 mm. We found that the CTV size can be reduced by both variable margin approaches by 15.6% and 13.3% respectively, while maintaining the CTV coverage. With approach 3 an increase of target coverage was obtained. CONCLUSION: Variable margins increase target coverage, reduce risk to OARs and improve healthy tissue sparing at the same time.

Progress in Fully Automated Abdominal CT Interpretation

  • Summers, Ronald M
American Journal of Roentgenology 2016 Journal Article, cited 25 times
Website
OBJECTIVE: Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically. CONCLUSION: This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.

Breast cancer molecular subtype classifier that incorporates MRI features

  • Sutton, Elizabeth J
  • Dashevsky, Brittany Z
  • Oh, Jung Hun
  • Veeraraghavan, Harini
  • Apte, Aditya P
  • Thakur, Sunitha B
  • Morris, Elizabeth A
  • Deasy, Joseph O
Journal of Magnetic Resonance Imaging 2016 Journal Article, cited 34 times
Website
Purpose: To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. Materials and Methods: This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR+ (n=95, 53.4%), ERPR-/HER2+ (n=35, 19.6%), or triple negative (TN, n=48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test. Results: Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), ;63.6% (ERPR-/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR-/HER2+), and 81.0% (TN). Conclusion: We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power.

Automated detection of glioblastoma tumor in brain magnetic imaging using ANFIS classifier

  • Thirumurugan, P
  • Ramkumar, D
  • Batri, K
  • Sundhara Raja, D
International Journal of Imaging Systems and Technology 2016 Journal Article, cited 3 times
Website
This article proposes a novel and efficient methodology for the detection of Glioblastoma tumor in brain MRI images. The proposed method consists of the following stages as preprocessing, Non-subsampled Contourlet transform (NSCT), feature extraction and Adaptive neuro fuzzy inference system classification. Euclidean direction algorithm is used to remove the impulse noise from the brain image during image acquisition process. NSCT decomposes the denoised brain image into approximation bands and high frequency bands. The features mean, standard deviation and energy are computed for the extracted coefficients and given to the input of the classifier. The classifier classifies the brain MRI image into normal or Glioblastoma tumor image based on the feature set. The proposed system achieves 99.8% sensitivity, 99.7% specificity, and 99.8% accuracy with respect to the ground truth images available in the dataset.

Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices

  • Upadhaya, Taman
  • Morvan, Yannick
  • Stindel, Eric
  • Le Reste, Pierre-Jean
  • Hatt, Mathieu
2016 Conference Paper, cited 4 times
Website

Automatic 3D pulmonary nodule detection in CT images: a survey

  • Valente, Igor Rafael S
  • Cortez, Paulo César
  • Neto, Edson Cavalcanti
  • Soares, José Marques
  • de Albuquerque, Victor Hugo C
  • Tavares, João Manuel RS
Computer methods and programs in biomedicine 2016 Journal Article, cited 67 times
Website

Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz

  • Wang, Qingzhu
  • Chen, Xiaoming
  • Wei, Mengying
  • Miao, Zhuang
Biomedical engineering online 2016 Journal Article, cited 1 times
Website

Single NMR image super-resolution based on extreme learning machine

  • Wang, Zhiqiong
  • Xin, Junchang
  • Wang, Zhongyang
  • Tian, Shuo
  • Qiu, Xuejun
Physica Medica 2016 Journal Article, cited 0 times
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Evaluating long-term outcomes via computed tomography in lung cancer screening

  • Wu, D
  • Liu, R
  • Levitt, B
  • Riley, T
  • Baumgartner, KB
J Biom Biostat 2016 Journal Article, cited 0 times

Intratumor partitioning and texture analysis of dynamic contrast‐enhanced (DCE)‐MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy

  • Wu, Jia
  • Gong, Guanghua
  • Cui, Yi
  • Li, Ruijiang
Journal of Magnetic Resonance Imaging 2016 Journal Article, cited 43 times
Website
PURPOSE: To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. RESULTS: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). CONCLUSION: The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115.

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition

  • Wu, Panpan
  • Xia, Kewen
  • Yu, Hengyong
Computer methods and programs in biomedicine 2016 Journal Article, cited 5 times
Website
BACKGROUND AND OBJECTIVE: Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. METHODS: An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. RESULTS: Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. CONCLUSIONS: Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE.

Independent external validation of predictive models for urinary dysfunction following external beam radiotherapy of the prostate: Issues in model development and reporting

  • Yahya, Noorazrul
  • Ebert, Martin A
  • Bulsara, Max
  • Kennedy, Angel
  • Joseph, David J
  • Denham, James W
Radiotherapy and Oncology 2016 Journal Article, cited 4 times
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Automatic 3D Mesh-Based Centerline Extraction from a Tubular Geometry Form

  • Yahya-Zoubir, Bahia
  • Hamami, Latifa
  • Saadaoui, Llies
  • Ouared, Rafik
Information Technology And Control 2016 Journal Article, cited 0 times
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Lung cancer deaths in the National Lung Screening Trial attributed to nonsolid nodules

  • Yip, Rowena
  • Yankelevitz, David F
  • Hu, Minxia
  • Li, Kunwei
  • Xu, Dong Ming
  • Jirapatnakul, Artit
  • Henschke, Claudia I
Radiology 2016 Journal Article, cited 0 times

Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images

  • Yu, Zexi
2016 Thesis, cited 0 times
Website

Segmentation of gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework

  • Zeng, Ke
  • Bakas, Spyridon
  • Sotiras, Aristeidis
  • Akbari, Hamed
  • Rozycki, Martin
  • Rathore, Saima
  • Pati, Sarthak
  • Davatzikos, Christos
2016 Conference Proceedings, cited 8 times
Website

Reproducibility of radiomics for deciphering tumor phenotype with imaging

  • Zhao, Binsheng
  • Tan, Yongqiang
  • Tsai, Wei-Yann
  • Qi, Jing
  • Xie, Chuanmiao
  • Lu, Lin
  • Schwartz, Lawrence H
Sci RepScientific reports 2016 Journal Article, cited 91 times
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Topology guided demons registration with local rigidity preservation

  • Zheng, Chaojie
  • Wang, Xiuying
  • Feng, Dagan
2016 Conference Proceedings, cited 1 times
Website

Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system

  • Zhu, H
  • Chen, H
  • Lin, Z
  • Shi, G
  • Lin, X
  • Wu, Z
  • Zhang, X
  • Zhang, X
Oncotarget 2016 Journal Article, cited 3 times
Website
Although the preoperative aspects and dimensions used for the PADUA scoring system were successfully applied in macroscopic clinical practice for renal tumor, the relevant molecular genetic basis remained unclear. To uncover meaningful correlations between the genetic aberrations and radiological features, we enrolled 112 patients with clear cell renal cell carcinoma (ccRCC) whose clinicopathological data, genomics data and CT data were obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). Overall PADUA score and several radiological features included in the PADUA system were assigned for each ccRCC. Despite having observed no significant association between the gene mutation frequency and the overall PADUA score, correlations between gene mutations and a few radiological features (tumor rim location and tumor size) were identified. A significant association between rim location and miRNA molecular subtypes was also observed. Survival analysis revealed that tumor size > 7 cm was significantly associated with poor survival. In addition, Gene Set Enrichment Analysis (GSEA) on mRNA expression revealed that the high PADUA score was related to numerous cancer-related networks, especially epithelial to mesenchymal transition (EMT) related pathways. This preliminary analysis of ccRCC revealed meaningful correlations between PADUA anatomical features and molecular basis including genomic aberrations and molecular subtypes.

Diffusion Weighted Magnetic Resonance Imaging Radiophenotypes and Associated Molecular Pathways in Glioblastoma

  • Zinn, Pascal O
  • Hatami, Masumeh
  • Youssef, Eslam
  • Thomas, Ginu A
  • Luedi, Markus M
  • Singh, Sanjay K
  • Colen, Rivka R
Neurosurgery 2016 Journal Article, cited 2 times
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Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

  • Abdollahzadeh Rezaie, Afsaneh
  • Habiboghli, Ali
International Journal of Interactive Multimedia and Artificial Inteligence 2017 Journal Article, cited 0 times
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A review of lung cancer screening and the role of computer-aided detection

  • Al Mohammad, B
  • Brennan, PC
  • Mello-Thoms, C
Clinical Radiology 2017 Journal Article, cited 23 times
Website

Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine

  • AL-Dabagh, Mustafa Zuhaer
  • AL-Mukhtar, Firas H
IJAERS 2017 Journal Article, cited 0 times
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Automatic intensity windowing of mammographic images based on a perceptual metric

  • Albiol, Alberto
  • Corbi, Alberto
  • Albiol, Francisco
Medical physics 2017 Journal Article, cited 0 times
Website
PURPOSE: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. METHODS: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at https://github.com/TheAnswerIsFortyTwo/GRAIL. RESULTS: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. CONCLUSIONS: A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.

Robust Detection of Circles in the Vessel Contours and Application to Local Probability Density Estimation

  • Alvarez, Luis
  • González, Esther
  • Esclarín, Julio
  • Gomez, Luis
  • Alemán-Flores, Miguel
  • Trujillo, Agustín
  • Cuenca, Carmelo
  • Mazorra, Luis
  • Tahoces, Pablo G
  • Carreira, José M
2017 Book Section, cited 3 times
Website

Hybrid Mass Detection in Breast MRI Combining Unsupervised Saliency Analysis and Deep Learning

  • Amit, Guy
  • Hadad, Omer
  • Alpert, Sharon
  • Tlusty, Tal
  • Gur, Yaniv
  • Ben-Ari, Rami
  • Hashoul, Sharbell
2017 Conference Paper, cited 15 times
Website
To interpret a breast MRI study, a radiologist has to examine over 1000 images, and integrate spatial and temporal information from multiple sequences. The automated detection and classification of suspicious lesions can help reduce the workload and improve accuracy. We describe a hybrid mass-detection algorithm that combines unsupervised candidate detection with deep learning-based classification. The detection algorithm first identifies image-salient regions, as well as regions that are cross-salient with respect to the contralateral breast image. We then use a convolutional neural network (CNN) to classify the detected candidates into true-positive and false-positive masses. The network uses a novel multi-channel image representation; this representation encompasses information from the anatomical and kinetic image features, as well as saliency maps. We evaluated our algorithm on a dataset of MRI studies from 171 patients, with 1957 annotated slices of malignant (59%) and benign (41%) masses. Unsupervised saliency-based detection provided a sensitivity of 0.96 with 9.7 false-positive detections per slice. Combined with CNN classification, the number of false positive detections dropped to 0.7 per slice, with 0.85 sensitivity. The multi-channel representation achieved higher classification performance compared to single-channel images. The combination of domain-specific unsupervised methods and general-purpose supervised learning offers advantages for medical imaging applications, and may improve the ability of automated algorithms to assist radiologists.

Fast wavelet based image characterization for content based medical image retrieval

  • Anwar, Syed Muhammad
  • Arshad, Fozia
  • Majid, Muhammad
2017 Conference Proceedings, cited 4 times
Website
A large collection of medical images surrounds health care centers and hospitals. Medical images produced by different modalities like magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and X-rays have increased incredibly with the advent of latest technologies for image acquisition. Retrieving clinical images of interest from these large data sets is a thought-provoking and demanding task. In this paper, a fast wavelet based medical image retrieval system is proposed that can aid physicians in the identification or analysis of medical images. The image signature is calculated using kurtosis and standard deviation as features. A possible use case is when the radiologist has some suspicion on diagnosis and wants further case histories, the acquired clinical images are sent (e.g. MRI images of brain) as a query to the content based medical image retrieval system. The system is tuned to retrieve the top most relevant images to the query. The proposed system is computationally efficient and more accurate in terms of the quality of retrieved images.

Analysis of Classification Methods for Diagnosis of Pulmonary Nodules in CT Images

  • Baboo, Capt Dr S Santhosh
  • Iyyapparaj, E
IOSR Journal of Electrical and Electronics Engineering 2017 Journal Article, cited 0 times
Website
The main aim of this work is to propose a novel Computer-aided detection (CAD) system based on a Contextual clustering combined with region growing for assisting radiologists in early identification of lung cancer from computed tomography(CT) scans. Instead of using conventional thresholding approach, this proposed work uses Contextual Clustering which yields a more accurate segmentation of the lungs from the chest volume. Following segmentation GLCM features are extracted which are then classified using three different classifiers namely Random forest, SVM and k-NN.

Detection of Brain Tumour in MRI Scan Images using Tetrolet Transform and SVM Classifier

  • Babu, B Shoban
  • Varadarajan, S
Indian Journal of Science and Technology 2017 Journal Article, cited 1 times
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BIOMEDICAL IMAGE RETRIEVAL USING LBWP

  • Babu, Joyce Sarah
  • Mathew, Soumya
  • Simon, Rini
International Research Journal of Engineering and Technology 2017 Journal Article, cited 0 times
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BraTS Multimodal Brain Tumor Segmentation Challenge

  • Bakas, Spyridon
2017 Conference Proceedings, cited 2030 times
Website

Multi‐site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data

  • Beichel, Reinhard R
  • Smith, Brian J
  • Bauer, Christian
  • Ulrich, Ethan J
  • Ahmadvand, Payam
  • Budzevich, Mikalai M
  • Gillies, Robert J
  • Goldgof, Dmitry
  • Grkovski, Milan
  • Hamarneh, Ghassan
Medical physics 2017 Journal Article, cited 7 times
Website
PURPOSE: Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making. METHODS: To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis. RESULTS: On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively. CONCLUSION: Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in Glioblastoma

  • Beig, Niha
  • Patel, Jay
  • Prasanna, Prateek
  • Partovi, Sasan
  • Varadhan, Vinay
  • Madabhushi, Anant
  • Tiwari, Pallavi
2017 Conference Proceedings, cited 3 times
Website

Segmentation of three-dimensional images with parametric active surfaces and topology changes

  • Benninghoff, Heike
  • Garcke, Harald
Journal of Scientific Computing 2017 Journal Article, cited 1 times
Website
In this paper, we introduce a novel parametric finite element method for segmentation of three-dimensional images. We consider a piecewise constant version of the Mumford-Shah and the Chan-Vese functionals and perform a region-based segmentation of 3D image data. An evolution law is derived from energy minimization problems which push the surfaces to the boundaries of 3D objects in the image. We propose a parametric scheme which describes the evolution of parametric surfaces. An efficient finite element scheme is proposed for a numerical approximation of the evolution equations. Since standard parametric methods cannot handle topology changes automatically, an efficient method is presented to detect, identify and perform changes in the topology of the surfaces. One main focus of this paper are the algorithmic details to handle topology changes like splitting and merging of surfaces and change of the genus of a surface. Different artificial images are studied to demonstrate the ability to detect the different types of topology changes. Finally, the parametric method is applied to segmentation of medical 3D images.

Predicting survival time of lung cancer patients using radiomic analysis

  • Chaddad, Ahmad
  • Desrosiers, Christian
  • Toews, Matthew
  • Abdulkarim, Bassam
Oncotarget 2017 Journal Article, cited 4 times
Website
Objectives: This study investigates the prediction of Non-small cell lung cancer (NSCLC) patient survival outcomes based on radiomic texture and shape features automatically extracted from tumor image data. Materials and Methods: Retrospective analysis involves CT scans of 315 NSCLC patients from The Cancer Imaging Archive (TCIA). A total of 24 image features are computed from labeled tumor volumes of patients within groups defined using NSCLC subtype and TNM staging information. Spearman's rank correlation, Kaplan-Meier estimation and log-rank tests were used to identify features related to long/short NSCLC patient survival groups. Automatic random forest classification was used to predict patient survival group from multivariate feature data. Significance is assessed at P < 0.05 following Holm-Bonferroni correction for multiple comparisons. Results: Significant correlations between radiomic features and survival were observed for four clinical groups: (group, [absolute correlation range]): (large cell carcinoma (LCC) [0.35, 0.43]), (tumor size T2, [0.31, 0.39]), (non lymph node metastasis N0, [0.3, 0.33]), (TNM stage I, [0.39, 0.48]). Significant log-rank relationships between features and survival time were observed for three clinical groups: (group, hazard ratio): (LCC, 3.0), (LCC, 3.9), (T2, 2.5) and (stage I, 2.9). Automatic survival prediction performance (i.e. below/above median) is superior for combined radiomic features with age-TNM in comparison to standard TNM clinical staging information (clinical group, mean area-under-the-ROC-curve (AUC)): (LCC, 75.73%), (N0, 70.33%), (T2, 70.28%) and (TNM-I, 76.17%). Conclusion: Quantitative lung CT imaging features can be used as indicators of survival, in particular for patients with large-cell-carcinoma (LCC), primary-tumor-sizes (T2) and no lymph-node-metastasis (N0).

A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images

  • Chen, Andrew X
  • Rabadán, Raúl
2017 Book Section, cited 0 times
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Low-dose CT via convolutional neural network

  • Chen, Hu
  • Zhang, Yi
  • Zhang, Weihua
  • Liao, Peixi
  • Li, Ke
  • Zhou, Jiliu
  • Wang, Ge
Biomedical Optics Express 2017 Journal Article, cited 342 times
Website
In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.

Bayesian Kernel Models for Statistical Genetics and Cancer Genomics

  • Crawford, Lorin
2017 Thesis, cited 0 times

Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma

  • Cui, Yi
  • Ren, Shangjie
  • Tha, Khin Khin
  • Wu, Jia
  • Shirato, Hiroki
  • Li, Ruijiang
European Radiology 2017 Journal Article, cited 10 times
Website

Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study

  • Czarnek, Nicholas
  • Clark, Kal
  • Peters, Katherine B
  • Mazurowski, Maciej A
Journal of neuro-oncology 2017 Journal Article, cited 15 times
Website
In this retrospective, IRB-exempt study, we analyzed data from 68 patients diagnosed with glioblastoma (GBM) in two institutions and investigated the relationship between tumor shape, quantified using algorithmic analysis of magnetic resonance images, and survival. Each patient's Fluid Attenuated Inversion Recovery (FLAIR) abnormality and enhancing tumor were manually delineated, and tumor shape was analyzed by automatic computer algorithms. Five features were automatically extracted from the images to quantify the extent of irregularity in tumor shape in two and three dimensions. Univariate Cox proportional hazard regression analysis was performed to determine how prognostic each feature was of survival. Kaplan Meier analysis was performed to illustrate the prognostic value of each feature. To determine whether the proposed quantitative shape features have additional prognostic value compared with standard clinical features, we controlled for tumor volume, patient age, and Karnofsky Performance Score (KPS). The FLAIR-based bounding ellipsoid volume ratio (BEVR), a 3D complexity measure, was strongly prognostic of survival, with a hazard ratio of 0.36 (95% CI 0.20-0.65), and remained significant in regression analysis after controlling for other clinical factors (P = 0.0061). Three enhancing-tumor based shape features were prognostic of survival independently of clinical factors: BEVR (P = 0.0008), margin fluctuation (P = 0.0013), and angular standard deviation (P = 0.0078). Algorithmically assessed tumor shape is statistically significantly prognostic of survival for patients with GBM independently of patient age, KPS, and tumor volume. This shows promise for extending the utility of MR imaging in treatment of GBM patients.

Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype?

  • Demerath, Theo
  • Simon-Gabriel, Carl Philipp
  • Kellner, Elias
  • Schwarzwald, Ralf
  • Lange, Thomas
  • Heiland, Dieter Henrik
  • Reinacher, Peter
  • Staszewski, Ori
  • Mast, Hansjorg
  • Kiselev, Valerij G
  • Egger, Karl
  • Urbach, Horst
  • Weyerbrock, Astrid
  • Mader, Irina
Neuroradiology Journal 2017 Journal Article, cited 5 times
Website
The purpose of this study was to identify markers from perfusion, diffusion, and chemical shift imaging in glioblastomas (GBMs) and to correlate them with genetically determined and previously published patterns of structural magnetic resonance (MR) imaging. Twenty-six patients (mean age 60 years, 13 female) with GBM were investigated. Imaging consisted of native and contrast-enhanced 3D data, perfusion, diffusion, and spectroscopic imaging. In the presence of minor necrosis, cerebral blood volume (CBV) was higher (median +/- SD, 2.23% +/- 0.93) than in pronounced necrosis (1.02% +/- 0.71), pcorr = 0.0003. CBV adjacent to peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity was lower in edema (1.72% +/- 0.31) than in infiltration (1.91% +/- 0.35), pcorr = 0.039. Axial diffusivity adjacent to peritumoral FLAIR hyperintensity was lower in severe mass effect (1.08*10(-3) mm(2)/s +/- 0.08) than in mild mass effect (1.14*10(-3) mm(2)/s +/- 0.06), pcorr = 0.048. Myo-inositol was positively correlated with a marker for mitosis (Ki-67) in contrast-enhancing tumor, r = 0.5, pcorr = 0.0002. Changed CBV and axial diffusivity, even outside FLAIR hyperintensity, in adjacent normal-appearing matter can be discussed as to be related to angiogenesis pathways and to activated proliferation genes. The correlation between myo-inositol and Ki-67 might be attributed to its binding to cell surface receptors regulating tumorous proliferation of astrocytic cells.

Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma

  • Dextraze, Katherine
  • Saha, Abhijoy
  • Kim, Donnie
  • Narang, Shivali
  • Lehrer, Michael
  • Rao, Anita
  • Narang, Saphal
  • Rao, Dinesh
  • Ahmed, Salmaan
  • Madhugiri, Venkatesh
  • Fuller, Clifton David
  • Kim, Michelle M
  • Krishnan, Sunil
  • Rao, Ganesh
  • Rao, Arvind
Oncotarget 2017 Journal Article, cited 0 times
Website
Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, "imaging habitats" were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions ("spatial imaging habitats") were derived, and those associated with overall survival (denoted "relevant" habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.

Radiology and Enterprise Medical Imaging Extensions (REMIX)

  • Erdal, Barbaros S
  • Prevedello, Luciano M
  • Qian, Songyue
  • Demirer, Mutlu
  • Little, Kevin
  • Ryu, John
  • O’Donnell, Thomas
  • White, Richard D
Journal of Digital Imaging 2017 Journal Article, cited 1 times
Website

New prognostic factor telomerase reverse transcriptase promotor mutation presents without MR imaging biomarkers in primary glioblastoma

  • Ersoy, Tunc F
  • Keil, Vera C
  • Hadizadeh, Dariusch R
  • Gielen, Gerrit H
  • Fimmers, Rolf
  • Waha, Andreas
  • Heidenreich, Barbara
  • Kumar, Rajiv
  • Schild, Hans H
  • Simon, Matthias
Neuroradiology 2017 Journal Article, cited 1 times
Website
PURPOSE: Magnetic resonance (MR) imaging biomarkers can assist in the non-invasive assessment of the genetic status in glioblastomas (GBMs). Telomerase reverse transcriptase (TERT) promoter mutations are associated with a negative prognosis. This study was performed to identify MR imaging biomarkers to forecast the TERT mutation status. METHODS: Pre-operative MRIs of 64/67 genetically confirmed primary GBM patients (51/67 TERT-mutated with rs2853669 polymorphism) were analyzed according to Visually AcceSAble Rembrandt Images (VASARI) ( https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project ) imaging criteria by three radiological raters. TERT mutation and O(6)-methylguanine-DNA methyltransferase (MGMT) hypermethylation data were obtained through direct and pyrosequencing as described in a previous study. Clinical data were derived from a prospectively maintained electronic database. Associations of potential imaging biomarkers and genetic status were assessed by Fisher and Mann-Whitney U tests and stepwise linear regression. RESULTS: No imaging biomarkers could be identified to predict TERT mutational status (alone or in conjunction with TERT promoter polymorphism rs2853669 AA-allele). TERT promoter mutations were more common in patients with tumor-associated seizures as first symptom (26/30 vs. 25/37, p = 0.07); these showed significantly smaller tumors [13.1 (9.0-19.0) vs. 24.0 (16.6-37.5) all cm(3); p = 0.007] and prolonged median overall survival [17.0 (11.5-28.0) vs. 9.0 (4.0-12.0) all months; p = 0.02]. TERT-mutated GBMs were underrepresented in the extended angularis region (p = 0.03), whereas MGMT-methylated GBMs were overrepresented in the corpus callosum (p = 0.03) and underrepresented temporomesially (p = 0.01). CONCLUSION: Imaging biomarkers for prediction of TERT mutation status remain weak and cannot be derived from the VASARI protocol. Tumor-associated seizures are less common in TERT mutated glioblastomas.

Feature fusion for lung nodule classification

  • Farag, Amal A
  • Ali, Asem
  • Elshazly, Salwa
  • Farag, Aly A
International journal of computer assisted radiology and surgery 2017 Journal Article, cited 3 times
Website

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture

  • Ferreira, José Raniery
  • Oliveira, Marcelo Costa
  • de Azevedo-Marques, Paulo Mazzoncini
Journal of Digital Imaging 2017 Journal Article, cited 1 times
Website

An Improved Mammogram Classification Approach Using Back Propagation Neural Network

  • Gautam, Aman
  • Bhateja, Vikrant
  • Tiwari, Ananya
  • Satapathy, Suresh Chandra
2017 Book Section, cited 16 times
Website
Mammograms are generally contaminated by quantum noise, degrading their visual quality and thereby the performance of the classifier in Computer-Aided Diagnosis (CAD). Hence, enhancement of mammograms is necessary to improve the visual quality and detectability of the anomalies present in the breasts. In this paper, a sigmoid based non-linear function has been applied for contrast enhancement of mammograms. The enhanced mammograms are used to define the texture of the detected anomaly using Gray Level Co-occurrence Matrix (GLCM) features. Later, a Back Propagation Artificial Neural Network (BP-ANN) is used as a classification tool for segregating the mammogram into abnormal or normal. The proposed classifier approach has reported to be the one with considerably better accuracy in comparison to other existing approaches.

Segmentation of colon and removal of opacified fluid for virtual colonoscopy

  • Gayathri, Devi K
  • Radhakrishnan, R
  • Rajamani, Kumar
Pattern Analysis and Applications 2017 Journal Article, cited 0 times
Website
Colorectal cancer (CRC) is the third most common type of cancer. The use of techniques such as flexible sigmoidoscopy and capsule endoscopy for the screening of colorectal cancer causes physical pain and hardship to the patients. Hence, to overcome the above disadvantages, computed tomography (CT) can be employed for the identification of polyps or growth, while screening for CRC. This proposed approach was implemented to improve the accuracy and to reduce the computation time of the accurate segmentation of the colon segments from the abdominal CT images which contain anatomical organs such as lungs, small bowels, large bowels (Colon), ribs, opacified fluid and bones. The segmentation is performed in two major steps. The first step segments the air-filled colon portions by placing suitable seed points using modified 3D seeded region growing which identify and match the similar voxels by 6-neighborhood connectivity technique. The segmentation of the opacified fluid portions is done using fuzzy connectedness approach enhanced with interval thresholding. The membership classes are defined and the voxels are categorized based on the class value. Interval thresholding is performed so that the bones and opacified fluid parts may be extracted. The bones are removed by the placement of seed points as the existence of the continuity of the bone region is more in the axial slices. The resultant image containing bones is subtracted from the threshold output to segment the opacified fluid segments in all the axial slices of a dataset. Finally, concatenation of the opacified fluid with the segmented colon is performed for the 3D rendering of the segmented colon. This method was implemented in 15 datasets downloaded from TCIA and in real-time dataset in both supine and prone position and the accuracy achieved was 98.73%.

Medical Imaging Segmentation Assessment via Bayesian Approaches to Fusion, Accuracy and Variability Estimation with Application to Head and Neck Cancer

  • Ghattas, Andrew Emile
2017 Thesis, cited 0 times
Website

Brain tumor detection from MRI image: An approach

  • Ghosh, Debjyoti
  • Bandyopadhyay, Samir Kumar
International Journal of Applied Research 2017 Journal Article, cited 0 times
Website
A brain tumor is an abnormal growth of cells within the brain, which can be cancerous or noncancerous (benign). This paper detects different types of tumors and cancerous growth within the brain and other associated areas within the brain by using computerized methods on MRI images of a patient. It is also possible to track the growth patterns of such tumors.

Role of Imaging in the Era of Precision Medicine

  • Giardino, Angela
  • Gupta, Supriya
  • Olson, Emmi
  • Sepulveda, Karla
  • Lenchik, Leon
  • Ivanidze, Jana
  • Rakow-Penner, Rebecca
  • Patel, Midhir J
  • Subramaniam, Rathan M
  • Ganeshan, Dhakshinamoorthy
Academic radiology 2017 Journal Article, cited 12 times
Website
Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.

Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks

  • Gibson, Eli
  • Giganti, Francesco
  • Hu, Yipeng
  • Bonmati, Ester
  • Bandula, Steve
  • Gurusamy, Kurinchi
  • Davidson, Brian R
  • Pereira, Stephen P
  • Clarkson, Matthew J
  • Barratt, Dean C
2017 Conference Proceedings, cited 14 times
Website

Intuitive Error Space Exploration of Medical Image Data in Clinical Daily Routine

  • Gillmann, Christina
  • Arbeláez, Pablo
  • Peñaloza, José Tiberio Hernández
  • Hagen, Hans
  • Wischgoll, Thomas
2017 Conference Paper, cited 3 times
Website

Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration

  • Goerres, J.
  • Uneri, A.
  • Jacobson, M.
  • Ramsay, B.
  • De Silva, T.
  • Ketcha, M.
  • Han, R.
  • Manbachi, A.
  • Vogt, S.
  • Kleinszig, G.
  • Wolinsky, J. P.
  • Osgood, G.
  • Siewerdsen, J. H.
Phys Med Biol 2017 Journal Article, cited 4 times
Website
Percutaneous pelvic screw placement is challenging due to narrow bone corridors surrounded by vulnerable structures and difficult visual interpretation of complex anatomical shapes in 2D x-ray projection images. To address these challenges, a system for planning, guidance, and quality assurance (QA) is presented, providing functionality analogous to surgical navigation, but based on robust 3D-2D image registration techniques using fluoroscopy images already acquired in routine workflow. Two novel aspects of the system are investigated: automatic planning of pelvic screw trajectories and the ability to account for deformation of surgical devices (K-wire deflection). Atlas-based registration is used to calculate a patient-specific plan of screw trajectories in preoperative CT. 3D-2D registration aligns the patient to CT within the projective geometry of intraoperative fluoroscopy. Deformable known-component registration (dKC-Reg) localizes the surgical device, and the combination of plan and device location is used to provide guidance and QA. A leave-one-out analysis evaluated the accuracy of automatic planning, and a cadaver experiment compared the accuracy of dKC-Reg to rigid approaches (e.g. optical tracking). Surgical plans conformed within the bone cortex by 3-4 mm for the narrowest corridor (superior pubic ramus) and >5 mm for the widest corridor (tear drop). The dKC-Reg algorithm localized the K-wire tip within 1.1 mm and 1.4 degrees and was consistently more accurate than rigid-body tracking (errors up to 9 mm). The system was shown to automatically compute reliable screw trajectories and accurately localize deformed surgical devices (K-wires). Such capability could improve guidance and QA in orthopaedic surgery, where workflow is impeded by manual planning, conventional tool trackers add complexity and cost, rigid tool assumptions are often inaccurate, and qualitative interpretation of complex anatomy from 2D projections is prone to trial-and-error with extended fluoroscopy time.

User-centered design and evaluation of interactive segmentation methods for medical images

  • Gueziri, Houssem-Eddine
2017 Thesis, cited 1 times
Website
Segmentation of medical images is a challenging task that aims to identify a particular structure present on the image. Among the existing methods involving the user at different levels, from a fully-manual to a fully-automated task, interactive segmentation methods provide assistance to the user during the task to reduce the variability in the results and allow occasional corrections of segmentation failures. Therefore, they offer a compromise between the segmentation efficiency and the accuracy of the results. It is the user who judges whether the results are satisfactory and how to correct them during the segmentation, making the process subject to human factors. Despite the strong influence of the user on the outcomes of a segmentation task, the impact of such factors has received little attention, with the literature focusing the assessment of segmentation processes on computational performance. Yet, involving the user performance in the analysis is more representative of a realistic scenario. Our goal is to explore the user behaviour in order to improve the efficiency of interactive image segmentation processes. This is achieved through three contributions. First, we developed a method which is based on a new user interaction mechanism to provide hints as to where to concentrate the computations. This significantly improves the computation efficiency without sacrificing the quality of the segmentation. The benefits of using such hints are twofold: (i) because our contribution is based on user interaction, it generalizes to a wide range of segmentation methods, and (ii) it gives comprehensive indications about where to focus the segmentation search. The latter advantage is used to achieve the second contribution. We developed an automated method based on a multi-scale strategy to: (i) reduce the user’s workload and, (ii) improve the computational time up to tenfold, allowing real-time segmentation feedback. Third, we have investigated the effects of such improvements in computations on the user’s performance. We report an experiment that manipulates the delay induced by the computation time while performing an interactive segmentation task. Results reveal that the influence of this delay can be significantly reduced with an appropriate interaction mechanism design. In conclusion, this project provides an effective image segmentation solution that has been developed in compliance with user performance requirements. We validated our approach through multiple user studies that provided a step forward into understanding the user behaviour during interactive image segmentation. Titre traduit Conception et évaluation orientées utilisateur des méthodes de segmentation interactives des images médicales Résumé traduit La segmentation d’images consiste à identifier une structure particulière dans une image. Parmi les méthodes existantes qui impliquent l’utilisateur à différents niveaux, les méthodes de segmentation interactives fournissent un support logiciel pour assister l’utilisateur dans cette tâche, ce qui aide à réduire la variabilité des résultats et permet de corriger les erreurs occasionnelles. Ces méthodes offrent un compromis entre l’efficacité et la précision des résultats. En effet, durant la segmentation, l’utilisateur décide si les résultats sont satisfaisants et dans le cas contraire, comment les corriger, rendant le processus sujet aux facteurs humains. Malgré la forte influence qu’a l’utilisateur sur l’issue de la segmentation, l’impact de ces facteurs a reçu peu d’attention de la part de la communauté scientifique, qui souvent, réduit l’évaluation des methods de segmentation à leurs performances de calcul. Pourtant, inclure la performance de l’utilisateur lors de l’évaluation de la segmentation permet une représentation plus fidèle de la réalité. Notre but est d’explorer le comportement de l’utilisateur afin d’améliorer l’efficacité des méthodes de segmentation interactives. Cette tâche est réalisée en trois contributions. Dans un premier temps, nous avons développé un nouveau mécanisme d’interaction utilisateur qui oriente la méthode de segmentation vers les endroits de l’image où concentrer les calculs. Ceci augmente significativement l’efficacité des calculs sans atténuer la qualité de la segmentation. Il y a un double avantage à utiliser un tel mécanisme: (i) puisque notre contribution est base sur l’interaction utilisateur, l’approche est généralisable à un grand nombre de méthodes de segmentation, et (ii) ce mécanisme permet une meilleure compréhension des endroits de l’image où l’on doit orienter la recherche du contour lors de la segmentation. Ce dernier point est exploité pour réaliser la deuxième contribution. En effet, nous avons remplacé le mécanisme d’interaction par une méthode automatique basée sur une stratégie multi-échelle qui permet de: (i) réduire l’effort produit par l’utilisateur lors de la segmentation, et (ii) améliorer jusqu’à dix fois le temps de calcul, permettant une segmentation en temps-réel. Dans la troisième contribution, nous avons étudié l’effet d’une telle amélioration des performances de calculs sur l’utilisateur. Nous avons mené une expérience qui manipule les délais des calculs lors de la segmentation interactive. Les résultats révèlent qu’une conception appropriée du mécanisme d’interaction peut réduire l’effet de ces délais sur l’utilisateur. En conclusion, ce projet offer une solution interactive de segmentation d’images développée en tenant compte de la performance de l’utilisateur. Nous avons validé notre approche à travers de multiples études utilisateurs qui nous ont permis une meilleure compréhension du comportement utilisateur durant la segmentation interactive des images.

Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset

  • Hancock, Matthew C
  • Magnan, Jerry F
2017 Conference Proceedings, cited 0 times
Website

Quantitative glioma grading using transformed gray-scale invariant textures of MRI

  • Hsieh, Kevin Li-Chun
  • Chen, Cheng-Yu
  • Lo, Chung-Ming
Computers in biology and medicine 2017 Journal Article, cited 8 times
Website
Background: A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings. Method: In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement. Results: The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334. Conclusions: More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.

Computer-aided grading of gliomas based on local and global MRI features

  • Hsieh, Kevin Li-Chun
  • Lo, Chung-Ming
  • Hsiao, Chih-Jou
Computer methods and programs in biomedicine 2017 Journal Article, cited 13 times
Website
BACKGROUND AND OBJECTIVES: A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. METHODS: The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model. RESULTS: Performances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement kappa = 0.698, p < 0.001. CONCLUSIONS: Numerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.

Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI

  • Hsieh, Kevin Li-Chun
  • Tsai, Ruei-Je
  • Teng, Yu-Chuan
  • Lo, Chung-Ming
PLoS One 2017 Journal Article, cited 0 times
Website
The effects of a computer-aided diagnosis (CAD) system based on quantitative intensity features with magnetic resonance (MR) imaging (MRI) were evaluated by examining radiologists' performance in grading gliomas. The acquired MRI database included 71 lower-grade gliomas and 34 glioblastomas. Quantitative image features were extracted from the tumor area and combined in a CAD system to generate a prediction model. The effect of the CAD system was evaluated in a two-stage procedure. First, a radiologist performed a conventional reading. A sequential second reading was determined with a malignancy estimation by the CAD system. Each MR image was regularly read by one radiologist out of a group of three radiologists. The CAD system achieved an accuracy of 87% (91/105), a sensitivity of 79% (27/34), a specificity of 90% (64/71), and an area under the receiver operating characteristic curve (Az) of 0.89. In the evaluation, the radiologists' Az values significantly improved from 0.81, 0.87, and 0.84 to 0.90, 0.90, and 0.88 with p = 0.0011, 0.0076, and 0.0167, respectively. Based on the MR image features, the proposed CAD system not only performed well in distinguishing glioblastomas from lower-grade gliomas but also provided suggestions about glioma grading to reinforce radiologists' confidence rating.

A longitudinal four‐dimensional computed tomography and cone beam computed tomography dataset for image‐guided radiation therapy research in lung cancer

  • Hugo, Geoffrey D
  • Weiss, Elisabeth
  • Sleeman, William C
  • Balik, Salim
  • Keall, Paul J
  • Lu, Jun
  • Williamson, Jeffrey F
Medical physics 2017 Journal Article, cited 8 times
Website
PURPOSE: To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community. ACQUISITION AND VALIDATION METHODS: As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. DATA FORMAT AND USAGE NOTES: 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available. DISCUSSION: Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.

Collage CNN for Renal Cell Carcinoma Detection from CT

  • Hussain, Mohammad Arafat
  • Amir-Khalili, Alborz
  • Hamarneh, Ghassan
  • Abugharbieh, Rafeef
2017 Conference Proceedings, cited 0 times
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Advanced MRI Techniques in the Monitoring of Treatment of Gliomas

  • Hyare, Harpreet
  • Thust, Steffi
  • Rees, Jeremy
Current treatment options in neurology 2017 Journal Article, cited 11 times
Website
OPINION STATEMENT: With advances in treatments and survival of patients with glioblastoma (GBM), it has become apparent that conventional imaging sequences have significant limitations both in terms of assessing response to treatment and monitoring disease progression. Both 'pseudoprogression' after chemoradiation for newly diagnosed GBM and 'pseudoresponse' after anti-angiogenesis treatment for relapsed GBM are well-recognised radiological entities. This in turn has led to revision of response criteria away from the standard MacDonald criteria, which depend on the two-dimensional measurement of contrast-enhancing tumour, and which have been the primary measure of radiological response for over three decades. A working party of experts published RANO (Response Assessment in Neuro-oncology Working Group) criteria in 2010 which take into account signal change on T2/FLAIR sequences as well as the contrast-enhancing component of the tumour. These have recently been modified for immune therapies, which are associated with specific issues related to the timing of radiological response. There has been increasing interest in quantification and validation of physiological and metabolic parameters in GBM over the last 10 years utilising the wide range of advanced imaging techniques available on standard MRI platforms. Previously, MRI would provide structural information only on the anatomical location of the tumour and the presence or absence of a disrupted blood-brain barrier. Advanced MRI sequences include proton magnetic resonance spectroscopy (MRS), vascular imaging (perfusion/permeability) and diffusion imaging (diffusion weighted imaging/diffusion tensor imaging) and are now routinely available. They provide biologically relevant functional, haemodynamic, cellular, metabolic and cytoarchitectural information and are being evaluated in clinical trials to determine whether they offer superior biomarkers of early treatment response than conventional imaging, when correlated with hard survival endpoints. Multiparametric imaging, incorporating different combinations of these modalities, improves accuracy over single imaging modalities but has not been widely adopted due to the amount of post-processing analysis required, lack of clinical trial data, lack of radiology training and wide variations in threshold values. New techniques including diffusion kurtosis and radiomics will offer a higher level of quantification but will require validation in clinical trial settings. Given all these considerations, it is clear that there is an urgent need to incorporate advanced techniques into clinical trial design to avoid the problems of under or over assessment of treatment response.

CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance

  • Jesson, Andrew
  • Guizard, Nicolas
  • Ghalehjegh, Sina Hamidi
  • Goblot, Damien
  • Soudan, Florian
  • Chapados, Nicolas
2017 Conference Proceedings, cited 18 times
Website
We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED improves the optimization of deep segmentation models by allowing them to first learn how to distinguish nodules from their immediate surroundings, while continuously adding a greater proportion of difficult-to-classify global context, until uniformly sampling from the empirical data distribution. Using CASED during training yields a minimalist proposal to the lung nodule detection problem that tops the LUNA16 nodule detection benchmark with an average sensitivity score of 88.35%. Furthermore, we find that models trained using CASED are robust to nodule annotation quality by showing that comparable results can be achieved when only a point and radius for each ground truth nodule are provided during training. Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent problem.

Interactive 3D Virtual Colonoscopic Navigation For Polyp Detection From CT Images

  • Joseph, Jinu
  • Kumar, Rajesh
  • Chandran, Pournami S
  • Vidya, PV
Procedia Computer Science 2017 Journal Article, cited 0 times
Website

Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma

  • Kanas, Vasileios G
  • Zacharaki, Evangelia I
  • Thomas, Ginu A
  • Zinn, Pascal O
  • Megalooikonomou, Vasileios
  • Colen, Rivka R
Computer methods and programs in biomedicine 2017 Journal Article, cited 16 times
Website
Background and objective: The O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively. Methods: A retrospective analysis of MR images from GBM patients was conducted. Multivariate prediction models were obtained by machine-learning methods and tested on data from The Cancer Genome Atlas (TCGA) database. Results: The status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%. Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in GBM. Conclusions: The obtained results provide further evidence of an association between standard preoperative MRI variables and MGMT methylation status in GBM.

3D multi-view convolutional neural networks for lung nodule classification

  • Kang, Guixia
  • Liu, Kui
  • Hou, Beibei
  • Zhang, Ningbo
PLoS One 2017 Journal Article, cited 7 times
Website

Supervised Dimension-Reduction Methods for Brain Tumor Image Data Analysis

  • Kawaguchi, Atsushi
2017 Book Section, cited 1 times
Website
The purpose of this study was to construct a risk score for glioblastomas based on magnetic resonance imaging (MRI) data. Tumor identification requires multimodal voxel-based imaging data that are highly dimensional, and multivariate models with dimension reduction are desirable for their analysis. We propose a two-step dimension-reduction method using a radial basis function–supervised multi-block sparse principal component analysis (SMS–PCA) method. The method is first implemented through the basis expansion of spatial brain images, and the scores are then reduced through regularized matrix decomposition in order to produce simultaneous data-driven selections of related brain regions supervised by univariate composite scores representing linear combinations of covariates such as age and tumor location. An advantage of the proposed method is that it identifies the associations of brain regions at the voxel level, and supervision is helpful in the interpretation.

Associations between gene expression profiles of invasive breast cancer and Breast Imaging Reporting and Data System MRI lexicon

  • Kim, Ga Ram
  • Ku, You Jin
  • Cho, Soon Gu
  • Kim, Sei Joong
  • Min, Byung Soh
Annals of Surgical Treatment and Research 2017 Journal Article, cited 3 times
Website
Purpose: To evaluate whether the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon could reflect the genomic information of breast cancers and to suggest intuitive imaging features as biomarkers. Methods: Matched breast MRI data from The Cancer Imaging Archive and gene expression profile from The Cancer Genome Atlas of 70 invasive breast cancers were analyzed. Magnetic resonance images were reviewed according to the BI-RADS MRI lexicon of mass morphology. The cancers were divided into 2 groups of gene clustering by gene set enrichment analysis. Clinicopathologic and imaging characteristics were compared between the 2 groups. Results: The luminal subtype was predominant in the group 1 gene set and the triple-negative subtype was predominant in the group 2 gene set (55 of 56, 98.2% vs. 9 of 14, 64.3%). Internal enhancement descriptors were different between the 2 groups; heterogeneity was most frequent in group 1 (27 of 56, 48.2%) and rim enhancement was dominant in group 2 (10 of 14, 71.4%). In group 1, the gene sets related to mammary gland development were overexpressed whereas the gene sets related to mitotic cell division were overexpressed in group 2. Conclusion: We identified intuitive imaging features of breast MRI associated with distinct gene expression profiles using the standard imaging variables of BI-RADS. The internal enhancement pattern on MRI might reflect specific gene expression profiles of breast cancers, which can be recognized by visual distinction.

Discovery radiomics for pathologically-proven computed tomography lung cancer prediction

  • Kumar, Devinder
  • Chung, Audrey G
  • Shaifee, Mohammad J
  • Khalvati, Farzad
  • Haider, Masoom A
  • Wong, Alexander
2017 Conference Proceedings, cited 30 times
Website

Textural Analysis of Tumour Imaging: A Radiomics Approach

  • Lambrecht, Joren
2017 Thesis, cited 0 times
Website

A simple texture feature for retrieval of medical images

  • Lan, Rushi
  • Zhong, Si
  • Liu, Zhenbing
  • Shi, Zhuo
  • Luo, Xiaonan
Multimedia Tools and Applications 2017 Journal Article, cited 2 times
Website
Texture characteristic is an important attribute of medical images, and has been applied in many medical image applications. This paper proposes a simple approach to employ the texture features of medical images for retrieval. The developed approach first conducts image filtering to medical images using different Gabor and Schmid filters, and then uniformly partitions the filtered images into non-overlapping patches. These operations provide extensive local texture information of medical images. The bag-of-words model is finally used to obtain feature representations of the images. Compared with several existing features, the proposed one is more discriminative and efficient. Experiments on two benchmark medical CT image databases have demonstrated the effectiveness of the proposed approach.

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

  • Lao, Jiangwei
  • Chen, Yinsheng
  • Li, Zhi-Cheng
  • Li, Qihua
  • Zhang, Ji
  • Liu, Jing
  • Zhai, Guangtao
Sci RepScientific reports 2017 Journal Article, cited 32 times
Website
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.

4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers

  • Larue, Ruben THM
  • Van De Voorde, Lien
  • van Timmeren, Janna E
  • Leijenaar, Ralph TH
  • Berbée, Maaike
  • Sosef, Meindert N
  • Schreurs, Wendy MJ
  • van Elmpt, Wouter
  • Lambin, Philippe
Radiotherapy and Oncology 2017 Journal Article, cited 7 times
Website
BACKGROUND AND PURPOSE: Quantitative tissue characteristics derived from medical images, also called radiomics, contain valuable prognostic information in several tumour-sites. The large number of features available increases the risk of overfitting. Typically test-retest CT-scans are used to reduce dimensionality and select robust features. However, these scans are not always available. We propose to use different phases of respiratory-correlated 4D CT-scans (4DCT) as alternative. MATERIALS AND METHODS: In test-retest CT-scans of 26 non-small cell lung cancer (NSCLC) patients and 4DCT-scans (8 breathing phases) of 20 NSCLC and 20 oesophageal cancer patients, 1045 radiomics features of the primary tumours were calculated. A concordance correlation coefficient (CCC) >0.85 was used to identify robust features. Correlation with prognostic value was tested using univariate cox regression in 120 oesophageal cancer patients. RESULTS: Features based on unfiltered images demonstrated greater robustness than wavelet-filtered features. In total 63/74 (85%) unfiltered features and 268/299 (90%) wavelet features stable in the 4D-lung dataset were also stable in the test-retest dataset. In oesophageal cancer 397/1045 (38%) features were robust, of which 108 features were significantly associated with overall-survival. CONCLUSION: 4DCT-scans can be used as alternative to eliminate unstable radiomics features as first step in a feature selection procedure. Feature robustness is tumour-site specific and independent of prognostic value.

Comparison of novel multi-level Otsu (MO-PET) and conventional PET segmentation methods for measuring FDG metabolic tumor volume in patients with soft tissue sarcoma

  • Lee, Inki
  • Im, Hyung-Jun
  • Solaiyappan, Meiyappan
  • Cho, Steve Y
EJNMMI physics 2017 Journal Article, cited 0 times
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Spatiotemporal genomic architecture informs precision oncology in glioblastoma

  • Lee, Jin-Ku
  • Wang, Jiguang
  • Sa, Jason K.
  • Ladewig, Erik
  • Lee, Hae-Ock
  • Lee, In-Hee
  • Kang, Hyun Ju
  • Rosenbloom, Daniel S.
  • Camara, Pablo G.
  • Liu, Zhaoqi
  • van Nieuwenhuizen, Patrick
  • Jung, Sang Won
  • Choi, Seung Won
  • Kim, Junhyung
  • Chen, Andrew
  • Kim, Kyu-Tae
  • Shin, Sang
  • Seo, Yun Jee
  • Oh, Jin-Mi
  • Shin, Yong Jae
  • Park, Chul-Kee
  • Kong, Doo-Sik
  • Seol, Ho Jun
  • Blumberg, Andrew
  • Lee, Jung-Il
  • Iavarone, Antonio
  • Park, Woong-Yang
  • Rabadan, Raul
  • Nam, Do-Hyun
Nat Genet 2017 Journal Article, cited 45 times
Website
Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies. However, this proposition is complicated by spatial and temporal heterogeneity. Here we study genomic and expression profiles across 127 multisector or longitudinal specimens from 52 individuals with glioblastoma (GBM). Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, whereas geographically separated, multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated with genetic similarity, and multifocal tumors that are enriched with PIK3CA mutations have a heterogeneous drug-response pattern. We show that targeting truncal events is more efficacious than targeting private events in reducing the tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multisector biopsies can inform targeted therapeutic interventions for patients with GBM.

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software

  • Lee, Myungeun
  • Woo, Boyeong
  • Kuo, Michael D
  • Jamshidi, Neema
  • Kim, Jong Hyo
Korean journal of radiology 2017 Journal Article, cited 7 times
Website

Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma

  • Lehrer, Michael
  • Bhadra, Anindya
  • Ravikumar, Visweswaran
  • Chen, James Y
  • Wintermark, Max
  • Hwang, Scott N
  • Holder, Chad A
  • Huang, Erich P
  • Fevrier-Sullivan, Brenda
  • Freymann, John B
  • Rao, Arvind
Oncoscience 2017 Journal Article, cited 1 times
Website
BACKGROUND AND PURPOSE: Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements. The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features. MATERIALS AND METHODS: Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions. RESULTS: The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways. CONCLUSION: Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.

Low-Dose CT streak artifacts removal using deep residual neural network

  • Li, Heyi
  • Mueller, Klaus
2017 Conference Proceedings, cited 6 times
Website

A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme

  • Li, Qihua
  • Bai, Hongmin
  • Chen, Yinsheng
  • Sun, Qiuchang
  • Liu, Lei
  • Zhou, Sijie
  • Wang, Guoliang
  • Liang, Chaofeng
  • Li, Zhi-Cheng
Sci RepScientific reports 2017 Journal Article, cited 9 times
Website

Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial

  • Li, Qian
  • Balagurunathan, Yoganand
  • Liu, Ying
  • Qi, Jin
  • Schabath, Matthew B
  • Ye, Zhaoxiang
  • Gillies, Robert J
Clinical lung cancer 2017 Journal Article, cited 3 times
Website

Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-OR Network

  • Liao, Fangzhou
  • Liang, Ming
  • Li, Zhe
  • Hu, Xiaolin
  • Song, Sen
IEEE Trans Neural Netw Learn Syst 2017 Journal Article, cited 15 times
Website
Automatic diagnosing lung cancer from computed tomography scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3-D deep neural network to solve this problem. The model consists of two modules. The first one is a 3-D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities, and combines them with a leaky noisy-OR gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The overfitting caused by the shortage of the training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition.

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

  • Limkin, EJ
  • Sun, R
  • Dercle, L
  • Zacharaki, EI
  • Robert, C
  • Reuzé, S
  • Schernberg, A
  • Paragios, N
  • Deutsch, E
  • Ferté, C
Annals of Oncology 2017 Journal Article, cited 49 times
Website

High-resolution anatomic correlation of cyclic motor patterns in the human colon: Evidence of a rectosigmoid brake

  • Lin, Anthony Y
  • Du, Peng
  • Dinning, Philip G
  • Arkwright, John W
  • Kamp, Jozef P
  • Cheng, Leo K
  • Bissett, Ian P
  • O'Grady, Gregory
American Journal of Physiology-Gastrointestinal and Liver Physiology 2017 Journal Article, cited 12 times
Website

Normalized Euclidean Super-Pixels for Medical Image Segmentation

  • Liu, Feihong
  • Feng, Jun
  • Su, Wenhuo
  • Lv, Zhaohui
  • Xiao, Fang
  • Qiu, Shi
2017 Conference Proceedings, cited 0 times
Website
We propose a super-pixel segmentation algorithm based on normalized Euclidean distance for handling the uncertainty and complexity in medical image. Benefited from the statistic characteristics, compactness within super-pixels is described by normalized Euclidean distance. Our algorithm banishes the balance factor of the Simple Linear Iterative Clustering framework. In this way, our algorithm properly responses to the lesion tissues, such as tiny lung nodules, which have a little difference in luminance with their neighbors. The effectiveness of proposed algorithm is verified in The Cancer Imaging Archive (TCIA) database. Compared with Simple Linear Iterative Clustering (SLIC) and Linear Spectral Clustering (LSC), the experiment results show that, the proposed algorithm achieves competitive performance over super-pixel segmentation in the state of art.

The Current Role of Image Compression Standards in Medical Imaging

  • Liu, Feng
  • Hernandez-Cabronero, Miguel
  • Sanchez, Victor
  • Marcellin, Michael W
  • Bilgin, Ali
Information 2017 Journal Article, cited 4 times
Website

Synthetic minority image over-sampling technique: How to improve AUC for glioblastoma patient survival prediction

  • Liu, Renhao
  • Hall, Lawrence O.
  • Bowyer, Kevin W.
  • Goldgof, Dmitry B.
  • Gatenby, Robert
  • Ben Ahmed, Kaoutar
2017 Conference Proceedings, cited 3 times
Website
Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt with at the feature vector level (create synthetic feature vectors or discard some examples from the larger class) or by assigning differential costs to errors. Here, we introduce a novel method for over-sampling minority class examples at the image level, rather than the feature vector level. Our method was applied to the problem of Glioblastoma patient survival group prediction. Synthetic minority class examples were created by adding Gaussian noise to original medical images from the minority class. Uniform local binary patterns (LBP) histogram features were then extracted from the original and synthetic image examples with a random forests classifier. Experimental results show the new method (Image SMOTE) increased minority class predictive accuracy and also the AUC (area under the receiver operating characteristic curve), compared to using the imbalanced dataset directly or to creating synthetic feature vectors.

A CADe system for nodule detection in thoracic CT images based on artificial neural network

  • Liu, Xinglong
  • Hou, Fei
  • Qin, Hong
  • Hao, Aimin
Science China Information Sciences 2017 Journal Article, cited 11 times
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Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis

  • Liu, Y
  • Xu, X
  • Yin, L
  • Zhang, X
  • Li, L
  • Lu, H
American Journal of Neuroradiology 2017 Journal Article, cited 8 times
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Brain tumor segmentation using morphological processing and the discrete wavelet transform

  • Lojzim, Joshua Michael
  • Fries, Marcus
Journal of Young Investigators 2017 Journal Article, cited 0 times
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Harmonizing the pixel size in retrospective computed tomography radiomics studies

  • Mackin, Dennis
  • Fave, Xenia
  • Zhang, Lifei
  • Yang, Jinzhong
  • Jones, A Kyle
  • Ng, Chaan S
PLoS One 2017 Journal Article, cited 19 times
Website

Measurement of smaller colon polyp in CT colonography images using morphological image processing

  • Manjunath, KN
  • Siddalingaswamy, PC
  • Prabhu, GK
International journal of computer assisted radiology and surgery 2017 Journal Article, cited 1 times
Website

Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data

  • Mazurowski, Maciej A
  • Clark, Kal
  • Czarnek, Nicholas M
  • Shamsesfandabadi, Parisa
  • Peters, Katherine B
  • Saha, Ashirbani
Journal of neuro-oncology 2017 Journal Article, cited 8 times
Website
Recent studies identified distinct genomic subtypes of lower-grade gliomas that could potentially be used to guide patient treatment. This study aims to determine whether there is an association between genomics of lower-grade glioma tumors and patient outcomes using algorithmic measurements of tumor shape in magnetic resonance imaging (MRI). We analyzed preoperative imaging and genomic subtype data from 110 patients with lower-grade gliomas (WHO grade II and III) from The Cancer Genome Atlas. Computer algorithms were applied to analyze the imaging data and provided five quantitative measurements of tumor shape in two and three dimensions. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. Patient outcomes were quantified by overall survival. We found that there is a strong association between angular standard deviation (ASD), which measures irregularity of the tumor boundary, and the IDH-1p/19q subtype (p < 0.0017), RNASeq cluster (p < 0.0002), DNA copy number cluster (p < 0.001), and the cluster of clusters (p < 0.0002). The RNASeq cluster was also associated with bounding ellipsoid volume ratio (p < 0.0005). Tumors in the IDH wild type cluster and R2 RNASeq cluster which are associated with much poorer outcomes generally had higher ASD reflecting more irregular shape. ASD also showed association with patient overall survival (p = 0.006). Shape features in MRI were strongly associated with genomic subtypes and patient outcomes in lower-grade glioma.

Transcription elongation factors represent in vivo cancer dependencies in glioblastoma

  • Miller, Tyler E
  • Liau, Brian B
  • Wallace, Lisa C
  • Morton, Andrew R
  • Xie, Qi
  • Dixit, Deobrat
  • Factor, Daniel C
  • Kim, Leo J Y
  • Morrow, James J
  • Wu, Qiulian
  • Mack, Stephen C
  • Hubert, Christopher G
  • Gillespie, Shawn M
  • Flavahan, William A
  • Hoffmann, Thomas
  • Thummalapalli, Rohit
  • Hemann, Michael T
  • Paddison, Patrick J
  • Horbinski, Craig M
  • Zuber, Johannes
  • Scacheri, Peter C
  • Bernstein, Bradley E
  • Tesar, Paul J
  • Rich, Jeremy N
Nature 2017 Journal Article, cited 41 times
Website
Glioblastoma is a universally lethal cancer with a median survival time of approximately 15 months. Despite substantial efforts to define druggable targets, there are no therapeutic options that notably extend the lifespan of patients with glioblastoma. While previous work has largely focused on in vitro cellular models, here we demonstrate a more physiologically relevant approach to target discovery in glioblastoma. We adapted pooled RNA interference (RNAi) screening technology for use in orthotopic patient-derived xenograft models, creating a high-throughput negative-selection screening platform in a functional in vivo tumour microenvironment. Using this approach, we performed parallel in vivo and in vitro screens and discovered that the chromatin and transcriptional regulators needed for cell survival in vivo are non-overlapping with those required in vitro. We identified transcription pause-release and elongation factors as one set of in vivo-specific cancer dependencies, and determined that these factors are necessary for enhancer-mediated transcriptional adaptations that enable cells to survive the tumour microenvironment. Our lead hit, JMJD6, mediates the upregulation of in vivo stress and stimulus response pathways through enhancer-mediated transcriptional pause-release, promoting cell survival specifically in vivo. Targeting JMJD6 or other identified elongation factors extends survival in orthotopic xenograft mouse models, suggesting that targeting transcription elongation machinery may be an effective therapeutic strategy for glioblastoma. More broadly, this study demonstrates the power of in vivo phenotypic screening to identify new classes of 'cancer dependencies' not identified by previous in vitro approaches, and could supply new opportunities for therapeutic intervention.

Volumetric brain tumour detection from MRI using visual saliency

  • Mitra, Somosmita
  • Banerjee, Subhashis
  • Hayashi, Yoichi
PLoS One 2017 Journal Article, cited 2 times
Website
Medical image processing has become a major player in the world of automatic tumour region detection and is tantamount to the incipient stages of computer aided design. Saliency detection is a crucial application of medical image processing, and serves in its potential aid to medical practitioners by making the affected area stand out in the foreground from the rest of the background image. The algorithm developed here is a new approach to the detection of saliency in a three dimensional multi channel MR image sequence for the glioblastoma multiforme (a form of malignant brain tumour). First we enhance the three channels, FLAIR (Fluid Attenuated Inversion Recovery), T2 and T1C (contrast enhanced with gadolinium) to generate a pseudo coloured RGB image. This is then converted to the CIE L*a*b* color space. Processing on cubes of sizes k = 4, 8, 16, the L*a*b* 3D image is then compressed into volumetric units; each representing the neighbourhood information of the surrounding 64 voxels for k = 4, 512 voxels for k = 8 and 4096 voxels for k = 16, respectively. The spatial distance of these voxels are then compared along the three major axes to generate the novel 3D saliency map of a 3D image, which unambiguously highlights the tumour region. The algorithm operates along the three major axes to maximise the computation efficiency while minimising loss of valuable 3D information. Thus the 3D multichannel MR image saliency detection algorithm is useful in generating a uniform and logistically correct 3D saliency map with pragmatic applicability in Computer Aided Detection (CADe). Assignment of uniform importance to all three axes proves to be an important factor in volumetric processing, which helps in noise reduction and reduces the possibility of compromising essential information. The effectiveness of the algorithm was evaluated over the BRATS MICCAI 2015 dataset having 274 glioma cases, consisting both of high grade and low grade GBM. The results were compared with that of the 2D saliency detection algorithm taken over the entire sequence of brain data. For all comparisons, the Area Under the receiver operator characteristic (ROC) Curve (AUC) has been found to be more than 0.99 ± 0.01 over various tumour types, structures and locations.

Deep Learning For Brain Tumor Segmentation

  • Moreno Lopez, Marc
2017 Thesis, cited 393 times
Website

Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm

  • Nabizadeh, Nooshin
  • Kubat, Miroslav
Expert Systems with Applications 2017 Journal Article, cited 8 times
Website
Automatic detection of brain tumors in single-spectral magnetic resonance images is a challenging task. Existing techniques suffer from inadequate performance, dependence on initial assumptions, and, sometimes, the need for manual interference. The research reported in this paper seeks to reduce some of these shortcomings, and to remove others, achieving satisfactory performance at reasonable computational costs. The success of the system described here is explained by the synergy of the following aspects: (1) a broad choice of high-level features to characterize the image's texture, (2) an efficient mechanism to eliminate less useful features (3) a machine-learning technique to induce a classifier that signals the presence of a tumor-affected tissue, and (4) an improved version of the skippy greedy snake algorithm to outline the tumor's contours. The paper describes the system and reports experiments with synthetic as well as real data. (C) 2017 Elsevier Ltd. All rights reserved.

Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma

  • Narang, Shivali
  • Kim, Donnie
  • Aithala, Sathvik
  • Heimberger, Amy B
  • Ahmed, Salmaan
  • Rao, Dinesh
  • Rao, Ganesh
  • Rao, Arvind
Oncotarget 2017 Journal Article, cited 1 times
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Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network

  • Newitt, David C
  • Malyarenko, Dariya
  • Chenevert, Thomas L
  • Quarles, C Chad
  • Bell, Laura
  • Fedorov, Andriy
  • Fennessy, Fiona
  • Jacobs, Michael A
  • Solaiyappan, Meiyappan
  • Hectors, Stefanie
  • Taouli, B.
  • Muzi, M.
  • Kinahan, P. E. E.
  • Schmainda, K. M.
  • Prah, M. A.
  • Taber, E. N.
  • Kroenke, C.
  • Huang, W., Arlinghaus, L.
  • Yankeelov, T. E.
  • Cao, Y.
  • Aryal, M.
  • Yen, Y.-F.
  • Kalpathy-Cramer, J.
  • Shukla-Dave, A.
  • Fung, M.
  • Liang, J.
  • Boss, M.
  • Hylton, N.
Journal of Medical Imaging 2017 Journal Article, cited 6 times
Website

Pulmonary nodule classification with deep residual networks

  • Nibali, Aiden
  • He, Zhen
  • Wollersheim, Dennis
International journal of computer assisted radiology and surgery 2017 Journal Article, cited 19 times
Website
Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules.

Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity

  • Nishio, Mizuho
  • Nagashima, Chihiro
Academic radiology 2017 Journal Article, cited 12 times
Website
RATIONALE AND OBJECTIVES: To develop a computer-aided diagnosis system to differentiate between malignant and benign nodules. MATERIALS AND METHODS: Seventy-three lung nodules revealed on 60 sets of computed tomography (CT) images were analyzed. Contrast-enhanced CT was performed in 46 CT examinations. The images were provided by the LUNGx Challenge, and the ground truth of the lung nodules was unavailable; a surrogate ground truth was, therefore, constructed by radiological evaluation. Our proposed method involved novel patch-based feature extraction using principal component analysis, image convolution, and pooling operations. This method was compared to three other systems for the extraction of nodule features: histogram of CT density, local binary pattern on three orthogonal planes, and three-dimensional random local binary pattern. The probabilistic outputs of the systems and surrogate ground truth were analyzed using receiver operating characteristic analysis and area under the curve. The LUNGx Challenge team also calculated the area under the curve of our proposed method based on the actual ground truth of their dataset. RESULTS: Based on the surrogate ground truth, the areas under the curve were as follows: histogram of CT density, 0.640; local binary pattern on three orthogonal planes, 0.688; three-dimensional random local binary pattern, 0.725; and the proposed method, 0.837. Based on the actual ground truth, the area under the curve of the proposed method was 0.81. CONCLUSIONS: The proposed method could capture discriminative characteristics of lung nodules and was useful for the differentiation between malignant and benign nodules.

Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree

  • Nowaková, Jana
  • Prílepok, Michal
  • Snášel, Václav
Journal of Medical Systems 2017 Journal Article, cited 33 times
Website
The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area - in mammography, in addition to the creation of the list of similar images - cases. The created list is used for assessing the nature of the finding - whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.

Memory-efficient 3D connected component labeling with parallel computing

  • Ohira, Norihiro
Signal, Image and Video Processing 2017 Journal Article, cited 0 times
Website

Application of Sparse-Coding Super-Resolution to 16-Bit DICOM Images for Improving the Image Resolution in MRI

  • Ota, Junko
  • Umehara, Kensuke
  • Ishimaru, Naoki
  • Ishida, Takayuki
Open Journal of Medical Imaging 2017 Journal Article, cited 1 times
Website

3D PULMONARY NODULES DETECTION USING FAST MARCHING SEGMENTATION

  • Paing, MP
  • Choomchuay, S
Journal of Fundamental and Applied Sciences 2017 Journal Article, cited 1 times
Website

A Novel End-to-End Classifier Using Domain Transferred Deep Convolutional Neural Networks for Biomedical Images

  • Pang, Shuchao
  • Yu, Zhezhou
  • Orgun, Mehmet A
Computer methods and programs in biomedicine 2017 Journal Article, cited 21 times
Website
BACKGROUND AND OBJECTIVES: Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. METHODS: We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. RESULTS: With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. CONCLUSIONS: We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.

Deep learning for segmentation of brain tumors: Can we train with images from different institutions?

  • Paredes, David
  • Saha, Ashirbani
  • Mazurowski, Maciej A
2017 Conference Proceedings, cited 2 times
Website

Tumor Propagation Model using Generalized Hidden Markov Model

  • Park, Sun Young
  • Sargent, Dustin
2017 Conference Proceedings, cited 0 times
Website

Machine learning applications for Radiomics: towards robust non-invasive predictors in clinical oncology

  • Parmar, Chintan
2017 Thesis, cited 1 times
Website

Multimodal Retrieval Framework for Brain Volumes in 3D MR Volumes

  • ParthaSarathi, Mangipudi
  • Ansari, Mohammad Ahmad
Journal of Medical and Biological Engineering 2017 Journal Article, cited 1 times
Website
The paper presents retrieval framework for extracting similar 3D tumor volumes in magnetic resonance brain volumes in response to a query tumor volume. Similar volumes correspond to closeness in spatial location of the brain structures. Query slice pertains to a new tumor volume of a patient and the output slices belong to the tumor volumes related to previous case histories stored in the database. The framework could be of immense help to the medical practitioners. It might prove to be a useful diagnostic aid for the medical expert and also serve as a teaching aid for researchers.

A versatile method for bladder segmentation in computed tomography two-dimensional images under adverse conditions

  • Pinto, João Ribeiro
  • Tavares, João Manuel RS
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2017 Journal Article, cited 1 times
Website

Fully Automated Multi-Modal Anatomic Atlas Generation Using 3D-Slicer

  • Rackerseder, Julia
  • González, Antonio Miguel Luque
  • Düwel, Charlotte
  • Navab, Nassir
  • Frisch, Benjamin
2017 Conference Paper, cited 2 times
Website
Atlases of the human body have many applications, includ-ing for instance the analysis of information from patient cohorts to eval-uate the distribution of tumours and metastases. We present a 3D Slicer module that simplifies the task of generating a multi-modal atlas from anatomical and functional data. It provides for a simpler evaluation of existing image and verbose patient data by integrating a database that isautomatically generated from text files and accompanies the visualization of the atlas volume. The computation of the atlas is a two step process. First, anatomical data is pairwise registered to a reference dataset withan affine initialization and a B-Spline based deformable approach. Sec-ond, the computed transformations are applied to anatomical as well as the corresponding functional data to generate both atlases. The moduleis validated with a publicly available soft tissue sarcoma dataset fromThe Cancer Imaging Archive. We show that functional data in the atlasvolume correlates with the findings from the patient database.

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

  • Rezaie, Afsaneh Abdollahzadeh
  • Habiboghli, Ali
International Journal of Interactive Multimedia and Artificial Intelligence 2017 Journal Article, cited 0 times
Website
In the present paper, a method for the detection of malignant and benign tumors on the CT scan images has been proposed. In the proposed method, firstly the area of interest in which the tumor may exist is selected on the original image and by the use of image segmentation and determination of the image's threshold limit, the tumor's area is specified and then edge detection filters are used for detection of the tumor's edge. After detection of area and by calculating the fractal dimensions with less percent of errors and better resolution, the areas where contain the tumor are determined. The images used in the proposed method have been extracted from cancer imaging archive database that is made available for public. Compared to other methods, our proposed method recognizes successfully benign and malignant tumors in all cases that have been clinically approved and belong to the database.

Computer Simulation of Low-dose CT with Clinical Lung Image Database: a preliminary study

  • Ronga, Junyan
  • Gaoa, Peng
  • Liua, Wenlei
  • Zhanga, Yuanke
  • Liua, Tianshuai
  • Lu, Hongbing
2017 Conference Proceedings, cited 1 times
Website

Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation

  • Ryalat, Mohammad Hashem
  • Laycock, Stephen
  • Fisher, Mark
2017 Conference Proceedings, cited 0 times
Website

Deciphering unclassified tumors of non-small-cell lung cancer through radiomics

  • Saad, Maliazurina
  • Choi, Tae-Sun
Computers in biology and medicine 2017 Journal Article, cited 8 times
Website

High Level Mammographic Information Fusion For Real World Ontology Population

  • Salem, Yosra Ben
  • Idodi, Rihab
  • Ettabaa, Karim Saheb
  • Hamrouni, Kamel
  • Solaiman, Basel
Journal of Digital Information Management 2017 Journal Article, cited 1 times
Website
In this paper, we propose a novel approach for ontology instantiating from real data related to the mammographic domain. In our study, we are interested in handling two modalities of mammographic images:mammography and Breast MRI. Firstly, we propose to model both images content in ontological representations since ontologies allow the description of the objects from a common perspective. In order, to overcome the ambiguity problem of representation of image’s entities, we propose to take advantage of the possibility theory applied to the ontological representation. Second, both local generated ontologies are merged in a unique formal representation with the use of two similarity measures: syntactic measure and possibilistic measure. The candidate instances are, finally, used for the global domain ontology populating in order to empower the mammographic knowledge base. The approach was validated on real world domain and the results were evaluated in terms of precision and recall by an expert.

Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research

  • Saltz, Joel
  • Almeida, Jonas
  • Gao, Yi
  • Sharma, Ashish
  • Bremer, Erich
  • DiPrima, Tammy
  • Saltz, Mary
  • Kalpathy-Cramer, Jayashree
  • Kurc, Tahsin
AMIA Summits on Translational Science Proceedings 2017 Journal Article, cited 4 times
Website
Cancer is a complex multifactorial disease state and the ability to anticipate and steer treatment results will require information synthesis across multiple scales from the host to the molecular level. Radiomics and Pathomics, where image features are extracted from routine diagnostic Radiology and Pathology studies, are also evolving as valuable diagnostic and prognostic indicators in cancer. This information explosion provides new opportunities for integrated, multi-scale investigation of cancer, but also mandates a need to build systematic and integrated approaches to manage, query and mine combined Radiomics and Pathomics data. In this paper, we describe a suite of tools and web-based applications towards building a comprehensive framework to support the generation, management and interrogation of large volumes of Radiomics and Pathomics feature sets and the investigation of correlations between image features, molecular data, and clinical outcome.

Identifying key radiogenomic associations between DCE-MRI and micro-RNA expressions for breast cancer

  • Samala, Ravi K
  • Chan, Heang-Ping
  • Hadjiiski, Lubomir
  • Helvie, Mark A
  • Kim, Renaid
2017 Conference Proceedings, cited 1 times
Website

Semi-automatic 3D lung nodule segmentation in CT using dynamic programming

  • Sargent, Dustin
  • Park, Sun Young
2017 Conference Proceedings, cited 0 times
Website

Wwox deficiency in human cancers: Role in treatment resistance

  • Schrock, Morgan S
2017 Thesis, cited 0 times
Website

A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials

  • Semmineh, Natenael B
  • Stokes, Ashley M
  • Bell, Laura C
  • Boxerman, Jerrold L
  • Quarles, C Chad
Tomography 2017 Journal Article, cited 5 times
Website
The standardization and broad-scale integration of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, we developed a digital reference object (DRO), using physiological and kinetic parameters derived from in vivo data, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach, aimed at validating image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas. To achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and changes observed across multiple glioblastomas, the DRO's input parameters were trained using DSC-MRI data from 23 glioblastomas (>40 000 voxels). The DRO's ability to produce reliable signals for combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. Representative applications of the DRO are presented, including the selection of DSC-MRI acquisition and postprocessing methods that optimize CBV accuracy, determination of the impact of DSC-MRI methodology choices on sample size requirements, and the assessment of treatment response in clinical glioblastoma trials.

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

  • Setio, A. A. A.
  • Traverso, A.
  • de Bel, T.
  • Berens, M. S. N.
  • Bogaard, C. V. D.
  • Cerello, P.
  • Chen, H.
  • Dou, Q.
  • Fantacci, M. E.
  • Geurts, B.
  • Gugten, R. V.
  • Heng, P. A.
  • Jansen, B.
  • de Kaste, M. M. J.
  • Kotov, V.
  • Lin, J. Y.
  • Manders, Jtmc
  • Sonora-Mengana, A.
  • Garcia-Naranjo, J. C.
  • Papavasileiou, E.
  • Prokop, M.
  • Saletta, M.
  • Schaefer-Prokop, C. M.
  • Scholten, E. T.
  • Scholten, L.
  • Snoeren, M. M.
  • Torres, E. L.
  • Vandemeulebroucke, J.
  • Walasek, N.
  • Zuidhof, G. C. A.
  • Ginneken, B. V.
  • Jacobs, C.
Med Image Anal 2017 Journal Article, cited 87 times
Website
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.

Fully automatic and accurate detection of lung nodules in CT images using a hybrid feature set

  • Shaukat, Furqan
  • Raja, Gulistan
  • Gooya, Ali
  • Frangi, Alejandro F
Medical physics 2017 Journal Article, cited 2 times
Website

2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer

  • Shen, Chen
  • Liu, Zhenyu
  • Guan, Min
  • Song, Jiangdian
  • Lian, Yucheng
  • Wang, Shuo
  • Tang, Zhenchao
  • Dong, Di
  • Kong, Lingfei
  • Wang, Meiyun
Translational oncology 2017 Journal Article, cited 10 times
Website

Combination of fuzzy c-means clustering and texture pattern matrix for brain MRI segmentation

  • Shijin Kumar, P.S.
  • Dharun, V.S.
Biomedical Research 2017 Journal Article, cited 0 times
The process of image segmentation can be defined as splitting an image into different regions. It is an important step in medical image analysis. We introduce a hybrid tumor tracking and segmentation algorithm for Magnetic Resonance Images (MRI). This method is based on Fuzzy C-means clustering algorithm (FCM) and Texture Pattern Matrix (TPM). The key idea is to use texture features along with intensity while performing segmentation. The performance parameters can be improved by using Texture Pattern Matrix (TPM). FCM is capable of predicting tumor cells with high accuracy. In FCM homogeneous regions in an image are obtained based on intensity. Texture Pattern Matrix (TPM) provides details about spatial distribution of pixels in an image. Experimental results obtained by applying proposed segmentation method for tracking tumors are presented. Various performance parameters are evaluated by comparing the outputs of proposed method and Fuzzy C-means algorithm. The computational complexity and computation time can be reduced by using this hybrid segmentation method.

Brain Tumor Segmentation Using Deep Learning Technique

  • Singh, Oyesh Mann
2017 Thesis, cited 0 times
Website

Simultaneous segmentation and correspondence improvement using statistical modes

  • Sinhaa, Ayushi
  • Reitera, Austin
  • Leonarda, Simon
  • Ishiib, Masaru
  • Hagera, Gregory D
  • Taylora, Russell H
2017 Conference Proceedings, cited 3 times
Website

Using Deep Learning for Classification of lung nodules on Computed Tomography Images

  • Song, QingZeng
  • Zhao, Lei
  • Luo, XingKe
  • Dou, XueChen
Journal of Healthcare Engineering 2017 Journal Article, cited 19 times
Website

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

  • Sutton, Elizabeth J
  • Huang, Erich P
  • Drukker, Karen
  • Burnside, Elizabeth S
  • Li, Hui
  • Net, Jose M
  • Rao, Arvind
  • Whitman, Gary J
  • Zuley, Margarita
  • Ganott, Marie
  • Bonaccio, Ermelinda
  • Giger, Maryellen L
  • Morris, Elizabeth A
European Radiology Experimental 2017 Journal Article, cited 17 times
Website
Background: In this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. Methods: Our retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff's alpha for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman's rank correlation coefficients were used to compare HEIP and CEIP. Results: Inter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with p < 10(-12). CEIP of shape, specifically sphericity and irregularity, replicated HEIP with both p values < 0.001. CEIP did not demonstrate agreement with HEIP of tumour margin or internal enhancement. Conclusions: Quantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.

Personalized Medicine, Biomarkers of Risk and Breast MRI

  • Sutton, Elizabeth J
  • Purvis, Nina
  • Pinker-Domenig, Katja
  • Morris, Elizabeth A
2017 Book Section, cited 0 times
Website
Breast cancer is a heterogeneous disease with inter- and intra-tumor genetic variation impacting predictive and prognostic risk. This chapter discusses the use of breast MRI, the most sensitive imaging modality for high-risk screening and pre-operative assessment, to predict breast cancer risk, to define extent of disease and to monitor neoadjuvant chemotherapeutic response at the level of the individual patient. In the current clinical landscape, immunohistochemical surrogates are used to define molecular subtypes and personalized cancer treatment and care. Radiogenomics involves the correlation of genomic information with imaging features. Feature extraction from breast MRI is being pursued on a large scale as a potential non-invasive means of defining molecular subtypes and/or developing phenotypic biomarkers that can be clinically analogous to commercially available genomic assays. Neoadjuvant chemotherapy, treatment administered in operable cancers before surgery, is increasingly used, allowing for breast conservation in women who would traditionally require mastectomy. As breast cancer genetic molecular subtypes are predictive of recurrence free and overall survival, treatment based on breast cancer molecular subtype and breast MRI is critical in evaluating response though improvement in its sensitivity for pathologic complete response. Breast MRI in the neoadjuvant cohort has provided biomarkers of response and insight into the biologic basis of disease. MRI is at the forefront of technology providing prognostic indicators as well as a crucial tool in personalizing medicine. © Springer International Publishing Switzerland 2017. All rights reserved.

Classification of Benign and Malignant Tumors of Lung Using Bag of Features

  • Suzan, A Melody
  • Prathibha, G
Journal of Scientific & Engineering Research 2017 Journal Article, cited 0 times
Website
This paper presents a novel approach for feature extraction and classification of lung cancer, i.e., Benign or malignant. Classification of lung cancer is based on a code book generated by using Bag of features algorithm. In this paper 300 regions of Interest (ROI’s) of lung cancer images from The Cancer Imaging Archive (TICA) sponsored by SPIE are used. In this approach Scale-Invariant Feature Transform (SIFT) is used for feature extraction and this coefficients are quantized using a bag of features into a predefined code book. This code book is given as input to the KNN classifier. The overall performance of the system in classifying tumors of lung is evaluated by using Receiver Operating Characteristics Curve (ROC). Area under the curve (AUC) is Az=0.95.

Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma

  • Taylor, Erik N
  • Ding, Yao
  • Zhu, Shan
  • Cheah, Eric
  • Alexander, Phillip
  • Lin, Leon
  • Aninwene II, George E
  • Hoffman, Matthew P
  • Mahajan, Anita
  • Mohamed, Abdallah SR
Oncotarget 2017 Journal Article, cited 0 times
Website

Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma

  • Taylor, Erik N
  • Ding, Yao
  • Zhu, Shan
  • Cheah, Eric
  • Alexander, Phillip
  • Lin, Leon
  • Aninwene II, George E
  • Hoffman, Matthew P
  • Mahajan, Anita
  • Mohamed, Abdallah SR
  • McDannold, N.
  • Fuller, Clifton D
  • Chen, Clark C.
  • Gilbert, Richard J.
Oncotarget 2017 Journal Article, cited 0 times
Website
While it is recognized that the overall resistance of glioblastoma to treatment may be related to intra-tumor patterns of structural heterogeneity, imaging methods to assess such patterns remain rudimentary. Methods: We utilized a generalized Q-space imaging (GQI) algorithm to analyze magnetic resonance imaging (MRI) derived from a rodent model of glioblastoma and 2 clinical datasets to correlate GQI, histology, and survival. Results: In a rodent glioblastoma model, GQI demonstrated a poorly coherent core region, consisting of diffusion tracts < 5 mm, surrounded by a shell of highly coherent diffusion tracts, 6-25 mm. Histologically, the core region possessed a high degree of necrosis, whereas the shell consisted of organized sheets of anaplastic cells with elevated mitotic index. These attributes define tumor architecture as the macroscopic organization of variably aligned tumor cells. Applied to MRI data from The Cancer Imaging Atlas (TCGA), the core-shell diffusion tract-length ratio (c/s ratio) correlated linearly with necrosis, which, in turn, was inversely associated with survival (p = 0.00002). We confirmed in an independent cohort of patients (n = 62) that the c/s ratio correlated inversely with survival (p = 0.0004). Conclusions: The analysis of MR images by GQI affords insight into tumor architectural patterns in glioblastoma that correlate with biological heterogeneity and clinical outcome.

Handling images of patient postures in arms up and arms down position using a biomechanical skeleton model

  • Teske, Hendrik
  • Bartelheimer, Kathrin
  • Bendl, Rolf
  • Stoiber, Eva M
  • Giske, Kristina
Current Directions in Biomedical Engineering 2017 Journal Article, cited 0 times
Website

Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

  • Umehara, Kensuke
  • Ota, Junko
  • Ishida, Takayuki
Open Journal of Medical Imaging 2017 Journal Article, cited 0 times
Website

Brain tumor classification from multi-modality MRI using wavelets and machine learning

  • Usman, Khalid
  • Rajpoot, Kashif
Pattern Analysis and Applications 2017 Journal Article, cited 17 times
Website

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

  • Vallières, Martin
  • Kay-Rivest, Emily
  • Perrin, Léo Jean
  • Liem, Xavier
  • Furstoss, Christophe
  • Aerts, Hugo JWL
  • Khaouam, Nader
  • Nguyen-Tan, Phuc Felix
  • Wang, Chang-Shu
  • Sultanem, Khalil
arXiv preprint arXiv:1703.08516 2017 Journal Article, cited 32 times
Website
Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

  • Vallières, Martin
  • Laberge, Sébastien
  • Diamant, André
  • El Naqa, Issam
Physics in Medicine & Biology 2017 Journal Article, cited 3 times
Website
Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice ('span'). Simulated T 1-weighted and T 2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of [Formula: see text] in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters ([Formula: see text]), with an average AUC of [Formula: see text]. Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

Brain Tumor Classification using Support Vector Machine

  • Vani, N
  • Sowmya, A
  • Jayamma, N
International Research Journal of Engineering and Technology 2017 Journal Article, cited 0 times
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Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group

  • Vargas, Hebert Alberto
  • Huang, Erich P
  • Lakhman, Yulia
  • Ippolito, Joseph E
  • Bhosale, Priya
  • Mellnick, Vincent
  • Shinagare, Atul B
  • Anello, Maria
  • Kirby, Justin
  • Fevrier-Sullivan, Brenda
Radiology 2017 Journal Article, cited 3 times
Website

Classification of benign and malignant lung nodules using image processing techniques

  • Vas, Moffy Crispin
  • Dessai, Amita
International Research Journal of Engineering and Technology 2017 Journal Article, cited 0 times
Website
Lung, CT, Feature extraction, segmentation, Computer tomography; Lung cancer; malignant; Haralick features; ANN; Haar Wavelet,

Assessing the prognostic impact of 3D CT image tumour rind texture features on lung cancer survival modelling

  • Vial, A.
  • Stirling, D.
  • Field, M.
  • Ros, M.
  • Ritz, C.
  • Carolan, M
  • Holloway, L.
  • Miller, A. A.
2017 Conference Paper, cited 1 times
Website

Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT

  • Wang, Dantong
  • Fong, Simon
  • Wong, Raymond K
  • Mohammed, Sabah
  • Fiaidhi, Jinan
  • Wong, Kelvin KL
Sci RepScientific reports 2017 Journal Article, cited 3 times
Website

CT-based radiomic analysis of hepatocellular carcinoma patients to predict key genomic information

  • West, Derek L
  • Kotrotsou, Aikaterini
  • Niekamp, Andrew Scott
  • Idris, Tagwa
  • Giniebra Camejo, Dunia
  • Mazal, Nicolas James
  • Cardenas, Nicolas James
  • Goldberg, Jackson L
  • Colen, Rivka R
Journal of Clinical Oncology 2017 Journal Article, cited 1 times
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Introducing the Medical Physics Dataset Article

  • Williamson, Jeffrey F
  • Das, Shiva K
  • Goodsitt, Mitchell S
  • Deasy, Joseph O
Medical physics 2017 Journal Article, cited 7 times
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Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT

  • Woodruff, Henry C.
  • Shieh, Chun-Chien
  • Hegi-Johnson, Fiona
  • Keall, Paul J.
  • Kipritidis, John
Medical physics 2017 Journal Article, cited 2 times
Website

Unsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways

  • Wu, Jia
  • Cui, Yi
  • Sun, Xiaoli
  • Cao, Guohong
  • Li, Bailiang
  • Ikeda, Debra M
  • Kurian, Allison W
  • Li, Ruijiang
Clinical Cancer Research 2017 Journal Article, cited 14 times
Website

Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer

  • Wu, Jia
  • Li, Bailiang
  • Sun, Xiaoli
  • Cao, Guohong
  • Rubin, Daniel L
  • Napel, Sandy
  • Ikeda, Debra M
  • Kurian, Allison W
  • Li, Ruijiang
Radiology 2017 Journal Article, cited 9 times
Website

Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation

  • Wu, Jia
  • Sun, Xiaoli
  • Wang, Jeff
  • Cui, Yi
  • Kato, Fumi
  • Shirato, Hiroki
  • Ikeda, Debra M
  • Li, Ruijiang
Journal of Magnetic Resonance Imaging 2017 Journal Article, cited 17 times
Website
Purpose: To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer. Materials and methods: In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort. Results: On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively. Conclusion: DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer. Level of evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027. Keywords: breast cancer; classification; dynamic contrast enhanced MRI; imaging genomics; molecular subtype.

Volume fractions of DCE-MRI parameter as early predictor of histologic response in soft tissue sarcoma: A feasibility study

  • Xia, Wei
  • Yan, Zhuangzhi
  • Gao, Xin
European Journal of Radiology 2017 Journal Article, cited 2 times
Website

Research on the Content-Based Classification of Medical Image

  • Yang, Hui
  • Liu, Feng
  • Wang, Zhiqi
  • Tang, Han
  • Sun, Shuyang
  • Sun, Shilei
Journal of Medical Imaging and Health Informatics 2017 Journal Article, cited 1 times
Website
Medical images have increased tremendously in numbers and categories these years as the devices generating them become more and more advanced. In this paper, four classifiers for automatically identifying medical images of different body parts are explored and implemented. Classic and recognized image descriptors such as wavelet transform and SIFT are utilized and combined with SVM and proposed modified KNN to verify the validity of the traditional classification methods when applied to medical images. In the process, a novel representation of wavelet feature is advanced in combination with a proposed tuned KNN. This wavelet feature is also applied with SVM. SIFT and its variety, dense SIFT are both employed to extract image features and they are formatted by the spatial pyramid model into a concatenated histogram. All these methods are compared with one another for accuracy and efficiency. Moreover, a convolutional network (CNN) is constructed to classify medical images. We show that in regards to the various types and huge numbers of medical images, traditional methods and deep learning approach such as CNN can both achieve high accuracy results. The methods illustrated in this paper can all be reasonably applied to medical image application with variance in speed and accuracy.

Lung Cancers Manifesting as Part-Solid Nodules in the National Lung Screening Trial

  • Yip, Rowena
  • Henschke, Claudia I
  • Xu, Dong Ming
  • Li, Kunwei
  • Jirapatnakul, Artit
  • Yankelevitz, David F
American Journal of Roentgenology 2017 Journal Article, cited 13 times
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MRI features predict survival and molecular markers in diffuse lower-grade gliomas

  • Zhou, Hao
  • Vallieres, Martin
  • Bai, Harrison X
  • Su, Chang
  • Tang, Haiyun
  • Oldridge, Derek
  • Zhang, Zishu
  • Xiao, Bo
  • Liao, Weihua
  • Tao, Yongguang
  • Zhou, Jianhua
  • Zhang, Paul
  • Yang, Li
Neuro-oncology 2017 Journal Article, cited 41 times
Website
Background: Previous studies have shown that MR imaging features can be used to predict survival and molecular profile of glioblastoma. However, no study of a similar type has been performed on lower-grade gliomas (LGGs). Methods: Presurgical MRIs of 165 patients with diffuse low- and intermediate-grade gliomas (histological grades II and III) were scored according to the Visually Accessible Rembrandt Images (VASARI) annotations. Radiomic models using automated texture analysis and VASARI features were built to predict isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q codeletion status, histological grade, and tumor progression. Results: Interrater analysis showed significant agreement in all imaging features scored (k = 0.703-1.000). On multivariate Cox regression analysis, no enhancement and a smooth non-enhancing margin were associated with longer progression-free survival (PFS), while a smooth non-enhancing margin was associated with longer overall survival (OS) after taking into account age, grade, tumor location, histology, extent of resection, and IDH1 1p/19q subtype. Using logistic regression and bootstrap testing evaluations, texture models were found to possess higher prediction potential for IDH1 mutation, 1p/19q codeletion status, histological grade, and progression of LGGs than VASARI features, with areas under the receiver-operating characteristic curves of 0.86 +/- 0.01, 0.96 +/- 0.01, 0.86 +/- 0.01, and 0.80 +/- 0.01, respectively. Conclusion: No enhancement and a smooth non-enhancing margin on MRI were predictive of longer PFS, while a smooth non-enhancing margin was a significant predictor of longer OS in LGGs. Textural analyses of MR imaging data predicted IDH1 mutation, 1p/19q codeletion, histological grade, and tumor progression with high accuracy.

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

  • Zhou, M
  • Scott, J
  • Chaudhury, B
  • Hall, L
  • Goldgof, D
  • Yeom, KW
  • Iv, M
  • Ou, Y
  • Kalpathy-Cramer, J
  • Napel, S
American Journal of Neuroradiology 2017 Journal Article, cited 20 times
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Data sharing in clinical trials: An experience with two large cancer screening trials

  • Zhu, Claire S
  • Pinsky, Paul F
  • Moler, James E
  • Kukwa, Andrew
  • Mabie, Jerome
  • Rathmell, Joshua M
  • Riley, Tom
  • Prorok, Philip C
  • Berg, Christine D
PLoS medicine 2017 Journal Article, cited 1 times
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Distinct Radiomic Phenotypes Define Glioblastoma TP53-PTEN-EGFR Mutational Landscape

  • Zinn, Pascal O
  • Singh, Sanjay K
  • Kotrotsou, Aikaterini
  • Abrol, Srishti
  • Thomas, Ginu
  • Mosley, Jennifer
  • Elakkad, Ahmed
  • Hassan, Islam
  • Kumar, Ashok
  • Colen, Rivka R
Neurosurgery 2017 Journal Article, cited 3 times
Website

Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks

  • Abd-Ellah, Mahmoud Khaled
  • Awad, Ali Ismail
  • Khalaf, Ashraf AM
  • Hamed, Hesham FA
EURASIP Journal on Image and Video Processing 2018 Journal Article, cited 0 times
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Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier

  • Abraham, Bejoy
  • Nair, Madhu S
Biocybernetics and Biomedical Engineering 2018 Journal Article, cited 0 times
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Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder

  • Abraham, Bejoy
  • Nair, Madhu S
Computerized Medical Imaging and Graphics 2018 Journal Article, cited 1 times
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Automated lung tumor detection and diagnosis in CT Scans using texture feature analysis and SVM

  • Adams, Tim
  • Dörpinghaus, Jens
  • Jacobs, Marc
  • Steinhage, Volker
Communication Papers of the Federated Conference on Computer Science and Information Systems 2018 Journal Article, cited 0 times
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Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN)

  • Agnes, S Akila
  • Anitha, J
  • Peter, J Dinesh
Neural Computing and Applications 2018 Journal Article, cited 0 times
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An extended reference region model for DCE‐MRI that accounts for plasma volume

  • Ahmed, Zaki
  • Levesque, Ives R
NMR in Biomedicine 2018 Journal Article, cited 0 times
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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing

  • AlBadawy, E. A.
  • Saha, A.
  • Mazurowski, M. A.
Med Phys 2018 Journal Article, cited 5 times
Website
BACKGROUND AND PURPOSE: Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs. METHODS: We selected 44 glioblastoma (GBM) patients from two institutions in The Cancer Imaging Archive dataset. The images were manually annotated by outlining each tumor component to form ground truth. To automatically segment the tumors in each patient, we trained three CNNs: (a) one using data for patients from the same institution as the test data, (b) one using data for the patients from the other institution and (c) one using data for the patients from both of the institutions. The performance of the trained models was evaluated using Dice similarity coefficients as well as Average Hausdorff Distance between the ground truth and automatic segmentations. The 10-fold cross-validation scheme was used to compare the performance of different approaches. RESULTS: Performance of the model significantly decreased (P < 0.0001) when it was trained on data from a different institution (dice coefficients: 0.68 +/- 0.19 and 0.59 +/- 0.19) as compared to training with data from the same institution (dice coefficients: 0.72 +/- 0.17 and 0.76 +/- 0.12). This trend persisted for segmentation of the entire tumor as well as its individual components. CONCLUSIONS: There is a very strong effect of selecting data for training on performance of CNNs in a multi-institutional setting. Determination of the reasons behind this effect requires additional comprehensive investigation.

Radiogenomics in renal cell carcinoma

  • Alessandrino, Francesco
  • Shinagare, Atul B
  • Bossé, Dominick
  • Choueiri, Toni K
  • Krajewski, Katherine M
Abdominal Radiology 2018 Journal Article, cited 0 times
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Imaging Biomarker Ontology (IBO): A Biomedical Ontology to Annotate and Share Imaging Biomarker Data

  • Amdouni, Emna
  • Gibaud, Bernard
Journal on Data Semantics 2018 Journal Article, cited 0 times
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Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks

  • Antonio, Victor Andrew A
  • Ono, Naoaki
  • Saito, Akira
  • Sato, Tetsuo
  • Altaf-Ul-Amin, Md
  • Kanaya, Shigehiko
International journal of computer assisted radiology and surgery 2018 Journal Article, cited 0 times
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PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phenotypic features from wider area to consider cellular distributions. METHODS: In order to recognize the types of tumors, we need not only to detail features of cells, but also to incorporate statistical distribution of the different types of cells. Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented. A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size [Formula: see text]. We applied this model for feature extraction from pathological images of lung adenocarcinoma, which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network. Since the tumor tissue is composed of heterogeneous cell populations, recognition of tumor transcriptome subtypes requires more information than local pattern of cells. The parameters extracted using this approach will then be used in multiple reduction stages to perform classification on larger images. RESULTS: We were able to demonstrate that these networks successfully recognize morphological features of lung adenocarcinoma. We also performed classification and reconstruction experiments to compare the outputs of the variants. The results showed that the larger input image that covers a certain area of the tissue is required to recognize transcriptome subtypes. The sparse autoencoder network with [Formula: see text] input provides a 98.9% classification accuracy. CONCLUSION: This study shows the potential of autoencoders as a feature extraction paradigm and paves the way for a whole slide image analysis tool to predict molecular subtypes of tumors from pathological features.

Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis

  • Arimura, Hidetaka
  • Soufi, Mazen
  • Ninomiya, Kenta
  • Kamezawa, Hidemi
  • Yamada, Masahiro
Radiological Physics and Technology 2018 Journal Article, cited 0 times
Website
Computer-aided diagnosis (CAD) is a field that is essentially based on pattern recognition that improves the accuracy of a diagnosis made by a physician who takes into account the computer’s “opinion” derived from the quantitative analysis of radiological images. Radiomics is a field based on data science that massively and comprehensively analyzes a large number of medical images to extract a large number of phenotypic features reflecting disease traits, and explores the associations between the features and patients’ prognoses for precision medicine. According to the definitions for both, you may think that radiomics is not a paraphrase of CAD, but you may also think that these definitions are “image manipulation”. However, there are common and different features between the two fields. This review paper elaborates on these common and different features and introduces the potential of radiomics for cancer diagnosis and treatment by comparing it with CAD.

Discovery of pre-therapy 2-deoxy-2-18 F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients

  • Arshad, Mubarik A
  • Thornton, Andrew
  • Lu, Haonan
  • Tam, Henry
  • Wallitt, Kathryn
  • Rodgers, Nicola
  • Scarsbrook, Andrew
  • McDermott, Garry
  • Cook, Gary J
  • Landau, David
European journal of nuclear medicine and molecular imaging 2018 Journal Article, cited 0 times
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Computer Aided Detection Scheme To Improve The Prognosis Assessment Of Early Stage Lung Cancer Patients

  • Athira, KV
  • Nithin, SS
Computer 2018 Journal Article, cited 0 times
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To develop a computer aided detection scheme to predict the stage 1 non-small cell lung cancer recurrence risk in lung cancer patients after surgery. By using chest computed tomography images; that taken before surgery, this system automatically segment the tumor that seen on CT images and extract the tumor related morphological and texture-based image features. We trained a Naïve Bayesian network classifier using six image features and an ANN classifier using two genomic biomarkers, these biomarkers are protein expression of the excision repair cross-complementing 1 gene (ERCC1) & a regulatory subunit of ribonucleotide reductase (RRM1) to predict the cancer recurrence risk, respectively. We developed a new approach that has a high potential to assist doctors in more effectively managing first stage NSCLC patients to reduce the cancer recurrence risk.

Analysis of dual tree M‐band wavelet transform based features for brain image classification

  • Ayalapogu, Ratna Raju
  • Pabboju, Suresh
  • Ramisetty, Rajeswara Rao
Magnetic Resonance in Medicine 2018 Journal Article, cited 1 times
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A radiogenomic dataset of non-small cell lung cancer

  • Bakr, Shaimaa
  • Gevaert, Olivier
  • Echegaray, Sebastian
  • Ayers, Kelsey
  • Zhou, Mu
  • Shafiq, Majid
  • Zheng, Hong
  • Benson, Jalen Anthony
  • Zhang, Weiruo
  • Leung, Ann NC
Scientific data 2018 Journal Article, cited 1 times
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Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image

  • Bandyopadhyay, Oishila
  • Biswas, Arindam
  • Bhattacharya, Bhargab B
J Digit Imaging 2018 Journal Article, cited 0 times
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Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-cost diagnostic tool for diagnosis and visualization of bone cancer. In this paper, a novel technique for the assessment of cancer stage and grade in long bones based on X-ray image analysis has been proposed. Cancer-affected bone images usually appear with a variation in bone texture in the affected region. A fusion of different methodologies is used for the purpose of our analysis. In the proposed approach, we extract certain features from bone X-ray images and use support vector machine (SVM) to discriminate healthy and cancerous bones. A technique based on digital geometry is deployed for localizing cancer-affected regions. Characterization of the present stage and grade of the disease and identification of the underlying bone-destruction pattern are performed using a decision tree classifier. Furthermore, the method leads to the development of a computer-aided diagnostic tool that can readily be used by paramedics and doctors. Experimental results on a number of test cases reveal satisfactory diagnostic inferences when compared with ground truth known from clinical findings.

Anatomical DCE-MRI phantoms generated from glioma patient data

  • Beers, Andrew
  • Chang, Ken
  • Brown, James
  • Zhu, Xia
  • Sengupta, Dipanjan
  • Willke, Theodore L
  • Gerstner, Elizabeth
  • Rosen, Bruce
  • Kalpathy-Cramer, Jayashree
2018 Conference Proceedings, cited 0 times
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Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma

  • Beig, N.
  • Patel, J.
  • Prasanna, P.
  • Hill, V.
  • Gupta, A.
  • Correa, R.
  • Bera, K.
  • Singh, S.
  • Partovi, S.
  • Varadan, V.
  • Ahluwalia, M.
  • Madabhushi, A.
  • Tiwari, P.
Sci RepScientific reports 2018 Journal Article, cited 5 times
Website
Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naive MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS (p = 0.003).

Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations Between mRNA-Based Subtyping and CT Imaging Features

  • Bowen, Lan
  • Xiaojing, Li
Academic radiology 2018 Journal Article, cited 0 times
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A volumetric technique for fossil body mass estimation applied to Australopithecus afarensis

  • Brassey, Charlotte A
  • O'Mahoney, Thomas G
  • Chamberlain, Andrew T
  • Sellers, William I
Journal of human evolution 2018 Journal Article, cited 3 times