PROSTATE-DIAGNOSIS

DOI: 10.7937/K9/TCIA.2015.FOQEUJVT | Image Collection

Prostate cancer T1- and T2-weighted magnetic resonance images (MRIs) were acquired on a 1.5 T Philips Achieva by combined surface and endorectal coil, including dynamic contrast-enhanced images obtained prior to, during and after I.V. administration of 0.1 mmol/kg body weight of Gadolinium-DTPA (pentetic acid). Corresponding clinical metadata (XLS format) and 3D segmentation files (NRRD format) are offered as a supplement...

[…] as portions of his forthcoming online prostate cancer image atlas. […]

LUNGCT-DIAGNOSIS

DOI: 10.7937/K9/TCIA.2015.A6V7JIWX | Image Collection

All the images are diagnostic contrast enhanced CT scans. The images were retrospectively acquired, to ensure sufficient patient follow-up. Slice thickness is variable : between 3 and 6 mm. All images were done at diagnosis and prior to surgery. The objective of the study was to extract prognostic image features that will describe lung adenocarcinomas and will associate with overall survival.  

Two CT features...

[…] scoring tumor shape complexity and intratumor density variation using routinely […]

NSCLC-RADIOGENOMICS

DOI: 10.7937/K9/TCIA.2017.7hs46erv | Image Collection

Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being a non-invasive procedure, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available for biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC)...

[…] biomarkers of cancer promise improvements in patient care through advances in […]

RIDER-PHANTOM-PET-CT

DOI: 10.7937/K9/TCIA.2015.8WG2KN4W | Image Collection

The RIDER PHANTOM PET-CT collection consists of repeat measurement PET/CT phantom scan collections carried out under the aegis of the Society of Nuclear Medicine (SNM) to discern the uniformity of clinical imaging instrumentation at various sites. They were obtained in cooperation with SNM as a resource for increased quantitative understanding of machine acquisition, analytic reproducibility and image processing.

The...

[…] the uniformity of clinical imaging instrumentation at various sites. They […]

RHUH-GBM

DOI: 10.7937/4545-c905 | Image Collection

 

This data collection consists of multiparametric MRI scans of 40 adult patients with histopathologically confirmed WHO grade 4 astrocytoma, who underwent surgery at the Río Hortega University Hospital in Valladolid, Spain, between January 2018 and December 2022. The dataset encompasses 600 MRI series, covering three time points: preoperative, early post-operative (less than 72 hours after surgery), and...

[…] the Río Hortega University Hospital in Valladolid, Spain, between January 2018 and December 2022. The dataset encompasses 600 MRI series, covering three time points: preoperative, early post-operative (less than 72 hours after surgery), and the follow-up scan, at which recurrence is diagnosed. Patients included in the sample underwent gross total resection (GTR) or near total resection (NTR), defined as having no residual tumor enhancement and an extent of resection of more than 95% of the initial enhancing volume, respectively. The modified Response Assessment in Neuro-Oncology criteria (RANO) were used to define tumor progression. The dataset contains T1-weighted (T1w), T2-weighted (T2w), Fluid Attenuated Inversion Recovery (FLAIR), T1w contrast-enhanced (T1ce) sequences, and diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) maps. It also includes clinical and demographic data, IDH status, treatment information, and volumetric assessment of the extent of the resection. Moreover, the dataset comprises expert-validated segmentations of tumor subregions (e.g., enhancing tumor, necrosis, peritumoral region), generated through computer-aided methods from preoperative, postoperative, and follow-up scans. This dataset is unique in its inclusion of patients who underwent extensive resection of > 95% of the enhancing tumor. It also stands out from other publicly available datasets by providing early postoperative studies and segmentations, filling the gap in preoperative-focused datasets. By making these data publicly available, the scientific community can analyze recurrence patterns in patients who underwent total or near-total resection and develop new registration and segmentation algorithms focused on post-surgical and follow-up studies. Acknowledgements This work was partially funded by a grant awarded by the “Instituto Carlos III, Proyectos I-D-i, Acción Estratégica en Salud 2022” under the project titled “Prediction of tumor recurrence in glioblastomas using magnetic resonance imaging, machine learning, and transcriptomic analysis: A supratotal resection guided by artificial intelligence,” reference PI22/01680. Data Access Detailed Description Citations & Data Usage Policy Versions Data Access Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data. Data Type Download all or Query/Filter License Images  (37425 files, DICOM, 16 GB) Download  Search  (Download requires NBIA Data Retriever) TCIA Restricted Brain-extracted Images, Segmentations (720 images , NIfTI, 2.9 GB)  Download  (Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)  CC BY 4.0 Clinical data (CSV, 7 kB) Download  CC BY 4.0 Click the Versions tab for more info about data releases. Additional Resources for this Dataset The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection. The source code used for image preprocessing in this collection can be found at https://github.com/smcch/RHUH-GBM-dataset-MRI-preprocessing Detailed Description Image Statistics Modalities MR Number of Patients 40 Number of Studies 120 Number of Series 600 Number of Images 38145 Images Size (GB) 18.9 The inclusion criteria were: Primary newly diagnosed WHO grade 4 astrocytoma adult patients (age over 18 years) who underwent surgery. Gross total resections (GTR) and Near Total Resection (NTR) were defined as no residual tumor enhancement and an extent of resection of more than 95% of the initial enhancing volume, respectively. Patients were treated with systemic temozolomide according to the Stupp protocol. Tumor progression was defined according to the modified Response assessment in neuro-oncology criteria (RANO). All the patients in our collection had primary glioblastomas. They were all newly diagnosed, with the exception of two patients who had undergone previous surgery and chemo/radiotherapy. The exclusion criteria were: Other histopathological diagnoses, patients in which it was impossible to establish the diagnosis of progression vs. pseudo-progression, missing MRI sequences, and poor-quality MRI scans due to the presence of artifacts. The dataset includes clinical and pathological information: Age, Sex, preoperative and postoperative Karnofsky performance score, Overall survival, Progression-free survival, percentage of the extent of resection of enhancing tumor, systemic therapy received, details of RT received (dose, technique, number of fractions, isodose), IDH status, ATRX mutation, and Ki-67 index, size of enhancing tumor recurrence. Note:  in the Clinical data file, In this dataset, some patients were still alive at the end of the data collection period, and their survival times are not yet known. These patients are considered right-censored = yes. In survival analysis, ‘right censoring’ occurs when the event of interest has not occurred for some study participants by the end of the study period or by the time the data was collected. This means that the survival time of censored participants is not fully observed or known.  The dataset includes the segmentations of the enhancing tumor, necrosis, and peritumoral region from the pre-postoperative and follow-up studies that experts have manually corrected. The dataset represents a sample of unique characteristics by including patients with an extent of resection of > 95 % of the enhancing […]

BRAIN-TR-GAMMAKNIFE

DOI: 10.7937/xb6d-py67 | Image Collection

Here we release a brain cancer MRI dataset with the companion Gamma Knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. The dataset consists of 47 subjects. A total of 244 lesions were collected with annotations. The dataset contains original patient MRI images (in DICOM format), radiation therapy structure data (in DICOM and NRRD format), code, and clinical information. First,...

[…] Here we release a bra in cancer MRI dataset with […]

Spine-Mets-CT-SEG

DOI: 10.7937/kh36-ds04 | Image Collection

We provide an annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with a large range of primary cancers and corresponding bone metastatic lesions obtained for patients with metastatic spine disease. The subjects of the study planned for radiotherapy...

[…] and a cloud-based data science infrastructure that connects data sets […]

DFCI-BCH-BWH-PEDs-HGG

DOI: 10.7937/v8h6-bg25 | Image Collection

Background: Pediatric tumors of the central nervous system are the leading cause of cancer-related death among children. High-grade gliomas (HGGs) in children have a five-year survival rate of less than 20%. Due to their rarity, diagnosis is often delayed, treatment strategies rely on historical protocols, and clinical trials necessitate collaboration across multiple institutions.

Summary: The...

The follow ing external resources have been made available by […]