Skip to main content

NLST

The Cancer Imaging Archive

NLST | National Lung Screening Trial

DOI: 10.7937/TCIA.HMQ8-J677 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Chest Human 26,254 CT, Other, Histopathology, Demographic, Diagnosis, Exposure, Measurement, Follow-Up Lung Cancer 11.92TB Clinical Public, Complete 2021/09/24

Summary

Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.

Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009.  This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.

Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).

Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385).

Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data.  The full clinical data set from NLST is available through CDAS.  Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial.  (These previously were restricted.)

Data Access

Version 3: Updated 2021/09/24

Data embargo of limited access is lifted September 2021, with the addition of downloadable pathology slide data and clinical data spreadsheet & dictionaries.

Title Data Type Format Access Points Subjects Studies Series Images License
Radiology CT Images CT DICOM
Download requires NBIA Data Retriever
 26,254 73,116 203,099 21,082,265 CC BY 4.0
Tissue Slide Images - Primary Tumor Histopathology SVS
Download requires IBM-Aspera-Connect plugin
 451 1,225 CC BY 4.0
Clinical data including data dictionaries Demographic, Diagnosis, Exposure, Measurement, Follow-Up SAS, ZIP, and DOC CC BY 4.0
Additional histopathology slide images Table 1 for which the participants have no Baseline Questionnaire data Other DOCX 2 CC BY 4.0
Histopathology additional slide images for which the participants have no Baseline Questionnaire data Histopathology SVS
Download requires IBM-Aspera-Connect plugin
 2 4 CC BY 4.0
Additional histopathology slide images Table 2 for participants with Second Primary Tumors as well as those included in the standard package Other DOCX 10 23 CC BY 4.0
Histopathology additional slide images for participants with Second Primary Tumors as well as those included in the standard package Histopathology SVS
Download requires IBM-Aspera-Connect plugin
 10 23 CC BY 4.0
Related Datasets
No related Analysis Results found: Submit your proposal! No related Collections found
Legend: Analysis Results| Collections

Additional Resources for this Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

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 the researchers utilizing this collection

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

 National Lung Screening Trial Research Team. (2013). Data from the National Lung Screening Trial (NLST) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.HMQ8-J677

Detailed Description

The full CT data (manifest-NLST_allCT.tcia) occupy 11.3 terabytes when downloaded. For convenience, you can use the “Search” feature to filter for subsets and download in chunks.

The pathology slide data:

  1. Primary Tumor slides (faspex) Primary Tumor slides (the standard package), 1225 files.
  2. Additional slides (faspex) Additional histopathology slide images for which the participants have no Baseline Questionnaire data (4 slides) Detail in Table 1.
  3. Second Primary-Tumor slides (faspex) Additional histopathology slide images for participants with Second Primary Tumors as well as those included in the “standard” package (23 slides) Detail in Table 2.

NLST Design & Process, Protocol Documents, and Results: https://cdas.cancer.gov/learn/nlst/main-findings/

NLST Data Collected: https://biometry.nci.nih.gov/cdas/learn/nlst/data-collected/

  • Questionnaires, screening, diagnostic procedures, cancer diagnosis, treatment, progression, mortality, contamination.
  • Further detail about the CT can be found here 

Biospecimens Collected

Formalin-fixed paraffin embedded (FFPE) tissue specimens are available for a subset of the NLST participants who developed lung cancer during the trial. Donor blocks were obtained from local pathology laboratories and tissue cores (0.6mm) were extracted from them to construct tissue microarrays (TMA). Tissue cores were sampled from primary main invasive tumor histology, secondary tumor histology, carcinoma in situ, adjacent normal lung tissue, metastatic lesion from lymph node(s) and/or distant sites, benign (un-involved) lymph node, proximal and/or distal bronchi.

In total, tissue materials were collected from 438 lung cancer cases. All have cores arrayed across nine TMAs, one of which only contains tissue collected after neoadjuvant treatment. 434 of these also have loose cores available for nucleic acid extraction. On average, each TMA contains 504 cores from 48 subjects.

Applications for access to these specimens can be submitted under the PLCO Etiologic and Early Marker Studies Program (EEMS). The application review process opens twice a year, once in the winter and once in the summer. For more information about EEMS and to initiate an application visit the PLCO EEMS Application page. When filling out the application, specify “NLST Tissue” under the case definition.

Related Publications

Publications by the Dataset Authors

The authors recommended the following as the best source of additional information about this dataset:

Publication Citation

National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD (2011). Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. New England Journal of Medicine, 365(5), 395–409. https://doi.org/10.1056/nejmoa1102873

No other publications were recommended by dataset authors.

Publication Citation

National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD (2011). Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. New England Journal of Medicine, 365(5), 395–409. https://doi.org/10.1056/nejmoa1102873

Research Community Publications

The Collection authors suggest the below will give context to this dataset:

  • National Lung Screening Trial Research Team. (2011). The national lung screening trial: overview and study design. Radiology, 258(1), 243-253. doi:https://doi.org/10.1148/radiol.10091808
  • Pinsky, P. F., Gierada, D. S., Nath, H., Kazerooni, E. A., & Amorosa, J. (2013). ROC curves for low-dose CT in the National Lung Screening Trial. J Med Screen, 20(3), 165-168. doi:10.1177/0969141313500666
  • Pinsky, P. F., Gierada, D. S., Nath, P. H., Kazerooni, E., & Amorosa, J. (2013). National lung screening trial: variability in nodule detection rates in chest CT studies. Radiology, 268(3), 865-873. doi:10.1148/radiol.13121530
  • National Lung Screening Trial Research Team, Aberle, D. R., Adams, A. M., Berg, C. D., Black, W. C., Clapp, J. D., . . . Sicks, J. D. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine, 365(5), 395-409. doi: https://doi.org/10.1056/NEJMoa1102873

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Note: IMS/CDAS maintains a separate list of publications related to NLST data: https://cdas.cancer.gov/publications/?study=nlst

  1. Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., . . . Shetty, S. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. doi:10.1038/s41591-019-0447-x
  2. Balagurunathan, Y., Schabath, M. B., Wang, H., Liu, Y., & Gillies, R. J. (2019). Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules. Sci Rep, 9(1). doi:10.1038/s41598-019-44562-z
  3. Bartel, S. T., Bierhals, A. J., Pilgram, T. K., Hong, C., Schechtman, K. B., Conradi, S. H., & Gierada, D. S. (2011). Equating quantitative emphysema measurements on different CT image reconstructions. Medical Physics, 38(8), 4894-4902. doi:10.1118/1.3615624
  4. Cherezov, D., Goldgof, D., Hall, L., Gillies, R., Schabath, M., Müller, H., & Depeursinge, A. (2019). Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness. Sci Rep, 9(1), 4500. doi:10.1038/s41598-019-38831-0
  5. Church, T. R., Black, W. C., Aberle, D. R., Berg, C. D., Clingan, K. L., Duan, F., . . . Baum, S. (2013). Results of initial low-dose computed tomographic screening for lung cancer. N Engl J Med, 368(21), 1980-1991. doi:10.1056/NEJMoa1209120
  6. Foley, F., Rajagopalan, S., Raghunath, S. M., Boland, J. M., Karwoski, R. A., Maldonado, F., . . . Peikert, T. (2016). Computer-aided nodule assessment and risk yield risk management of adenocarcinoma: the future of imaging? Paper presented at the Seminars in thoracic and cardiovascular surgery.
  7. Gierada, D. S., Guniganti, P., Newman, B. J., Dransfield, M. T., Kvale, P. A., Lynch, D. A., & Pilgram, T. K. (2011). Quantitative CT assessment of emphysema and airways in relation to lung cancer risk. Radiology, 261(3), 950-959. doi:https://doi.org/10.1148/radiol.11110542
  8. Gierada, D. S., Pinsky, P., Nath, H., Chiles, C., Duan, F., & Aberle, D. R. (2014). Projected outcomes using different nodule sizes to define a positive CT lung cancer screening examination. Journal of the National Cancer Institute, 106(11), dju284. doi:10.1093/jnci/dju284
  9. Gunawan, R., Tran, Y., Zheng, J., Nguyen, H., & Chai, R. (2022). Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net. Sensors (Basel), 22(18). doi:https://doi.org/10.3390/s22187031
  10. Jamdade, V. A. (2022). Explainable Lung Nodule Malignancy Classification from CT Scans. (M.S. Thesis). University of Maryland, Baltimore County, USA, University of Maryland, Baltimore County ProQuest Dissertations Publishing. Retrieved from https://dissexpress.proquest.com/dxweb/results.html?QryTxt=&pubnum=29997250
  11. Jeon, K. N., Goo, J. M., Lee, C. H., Lee, Y., Choo, J. Y., Lee, N. K., . . . Gierada, D. S. (2012). Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening CT. Investigative radiology, 47(8), 457. doi:10.1097/RLI.0b013e318250a5aa
  12. Lo, S. B., Freedman, M. T., Gillis, L. B., White, C. S., & Mun, S. K. (2018). JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function. American Journal of Roentgenology, 210(3), 480-488. doi:10.2214/AJR.17.18718
  13. Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., . . . Barzilay, R. (2023). Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. J Clin Oncol, JCO2201345. doi:https://doi.org/10.1200/JCO.22.01345
  14. Patz Jr, E. F., Greco, E., Gatsonis, C., Pinsky, P., Kramer, B. S., & Aberle, D. R. (2016). 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. The Lancet Oncology, 17(5), 590-599. doi:https://doi.org/10.1016/S1470-2045(15)00621-X
  15. Perez-Morales, J., Tunali, I., Stringfield, O., Eschrich, S. A., Balagurunathan, Y., Gillies, R. J., & Schabath, M. B. (2020). Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening. Sci Rep, 10(1), 10528. doi:https://doi.org/10.1038/s41598-020-67378-8
  16. Petousis, P., Han, S. X., Aberle, D., & Bui, A. A. (2016). Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artificial intelligence in medicine, 72, 42-55. doi:https://doi.org/10.1016/j.artmed.2016.07.001
  17. Pilgram, T. K., Quirk, J. D., Bierhals, A. J., Yusen, R. D., Lefrak, S. S., Cooper, J. D., & Gierada, D. S. (2010). Accuracy of emphysema quantification performed with reduced numbers of CT sections. American Journal of Roentgenology, 194(3), 585-591. doi:10.2214/AJR.09.2709
  18. Pinsky, P. F., Nath, P. H., Gierada, D. S., Sonavane, S., & Szabo, E. (2014). Short-and long-term lung cancer risk associated with noncalcified nodules observed on low-dose CT. Cancer prevention research, 7(12), 1179-1185. doi:10.1158/1940-6207.CAPR-13-0438
  19. Pu, L., Gezer, N. S., Ashraf, S. F., Ocak, I., Dresser, D. E., & Dhupar, R. (2022). Automated segmentation of five different body tissues on computed tomography using deep learning. Med Phys. doi:https://doi.org/10.1002/mp.15932
  20. Reeves, A. P., Xie, Y., & Jirapatnakul, A. (2016). Automated pulmonary nodule CT image characterization in lung cancer screening. International Journal of Computer Assisted Radiology and Surgery, 11(1), 73-88. doi: 10.1007/s11548-015-1245-7
  21. Salama, W. M., Aly, M. H., & Elbagoury, A. M. (2021). Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach. Journal of Physics: Conference Series, 2128(1). doi:10.1088/1742-6596/2128/1/012011
  22. Schreuder, A., Jacobs, C., Gallardo-Estrella, L., Prokop, M., Schaefer-Prokop, C. M., & van Ginneken, B. (2019). Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances. PLoS One, 14(2), e0212756. doi:https://doi.org/10.1371/journal.pone.0212756
  23. Schreuder, A., van Ginneken, B., Scholten, E. T., Jacobs, C., Prokop, M., Sverzellati, N., . . . Schaefer-Prokop, C. M. (2018). Classification of CT pulmonary opacities as perifissural nodules: reader variability. Radiology, 288(3), 867-875. doi:https://doi.org/10.1148/radiol.2018172771
  24. Shields, B., & Ramachandran, P. (2023). Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept. Phys Eng Sci Med. doi:https://doi.org/10.1007/s13246-023-01302-y
  25. Singh, S., Gierada, D. S., Pinsky, P., Sanders, C., Fineberg, N., Sun, Y., . . . Nath, H. (2012). Reader variability in identifying pulmonary nodules on chest radiographs from the national lung screening trial. Journal of thoracic imaging, 27(4), 249. doi:10.1097/RTI.0b013e318256951e
  26. Singh, S., Pinsky, P., Fineberg, N. S., Gierada, D. S., Garg, K., Sun, Y., & Nath, P. H. (2011). Evaluation of reader variability in the interpretation of follow-up CT scans at lung cancer screening. Radiology, 259(1), 263-270. doi:10.1148/radiol.10101254
  27. Torres, F. S., Akbar, S., Raman, S., Yasufuku, K., Schmidt, C., Hosny, A., . . . Leighl, N. B. (2021). End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography. JCO Clin Cancer Inform, 5, 1141-1150. doi:10.1200/cci.21.00096
  28. Uthoff, J. M. (2019). Cancer Risk Assessment Using Quantitative Imaging Features from Solid Tumors and Surrounding Structures. (Ph.D. Dissertation). The University of Iowa, Ann Arbor, United States. Retrieved from https://www.proquest.com/dissertations-theses/cancer-risk-assessment-using-quantitative-imaging/docview/2306303717/se-2?accountid=142023 (2306303717, 13858412)
  29. Wu, D., Liu, R., Levitt, B., Riley, T., & Baumgartner, K. (2016). Evaluating long-term outcomes via computed tomography in lung cancer screening. J Biom Biostat, 7(313), 2. doi:10.4172/2155-6180.1000313
  30. Yip, R., Henschke, C. I., Xu, D. M., Li, K., Jirapatnakul, A., & Yankelevitz, D. F. (2017). Lung Cancers Manifesting as Part-Solid Nodules in the National Lung Screening Trial. American Journal of Roentgenology, 208(5), 1011-1021. doi:10.2214/Ajr.16.16930
  31. Yip, R., Yankelevitz, D. F., Hu, M., Li, K., Xu, D. M., Jirapatnakul, A., & Henschke, C. I. (2016). Lung cancer deaths in the National Lung Screening Trial attributed to nonsolid nodules. Radiology, 281(2), 589-596. doi:https://doi.org/10.1148/radiol.2016152333
  32. Zhao, T., & Yin, Z. (2021). Airway Anomaly Detection by Prototype-Based Graph Neural Network. Paper presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France.
  33. Zhu, C. S., Pinsky, P. F., Moler, J. E., Kukwa, A., Mabie, J., Rathmell, J. M., . . . Berg, C. D. (2017). Data sharing in clinical trials: An experience with two large cancer screening trials. PLoS medicine, 14(5), e1002304. doi:10.1371/journal.pmed.1002304

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Other Publications Using this Data

The Collection authors suggest the below will give context to this dataset:

  • National Lung Screening Trial Research Team. (2011). The national lung screening trial: overview and study design. Radiology, 258(1), 243-253. doi:https://doi.org/10.1148/radiol.10091808
  • Pinsky, P. F., Gierada, D. S., Nath, H., Kazerooni, E. A., & Amorosa, J. (2013). ROC curves for low-dose CT in the National Lung Screening Trial. J Med Screen, 20(3), 165-168. doi:10.1177/0969141313500666
  • Pinsky, P. F., Gierada, D. S., Nath, P. H., Kazerooni, E., & Amorosa, J. (2013). National lung screening trial: variability in nodule detection rates in chest CT studies. Radiology, 268(3), 865-873. doi:10.1148/radiol.13121530
  • National Lung Screening Trial Research Team, Aberle, D. R., Adams, A. M., Berg, C. D., Black, W. C., Clapp, J. D., . . . Sicks, J. D. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine, 365(5), 395-409. doi: https://doi.org/10.1056/NEJMoa1102873

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Note: IMS/CDAS maintains a separate list of publications related to NLST data: https://cdas.cancer.gov/publications/?study=nlst

  1. Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., . . . Shetty, S. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. doi:10.1038/s41591-019-0447-x
  2. Balagurunathan, Y., Schabath, M. B., Wang, H., Liu, Y., & Gillies, R. J. (2019). Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules. Sci Rep, 9(1). doi:10.1038/s41598-019-44562-z
  3. Bartel, S. T., Bierhals, A. J., Pilgram, T. K., Hong, C., Schechtman, K. B., Conradi, S. H., & Gierada, D. S. (2011). Equating quantitative emphysema measurements on different CT image reconstructions. Medical Physics, 38(8), 4894-4902. doi:10.1118/1.3615624
  4. Cherezov, D., Goldgof, D., Hall, L., Gillies, R., Schabath, M., Müller, H., & Depeursinge, A. (2019). Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness. Sci Rep, 9(1), 4500. doi:10.1038/s41598-019-38831-0
  5. Church, T. R., Black, W. C., Aberle, D. R., Berg, C. D., Clingan, K. L., Duan, F., . . . Baum, S. (2013). Results of initial low-dose computed tomographic screening for lung cancer. N Engl J Med, 368(21), 1980-1991. doi:10.1056/NEJMoa1209120
  6. Foley, F., Rajagopalan, S., Raghunath, S. M., Boland, J. M., Karwoski, R. A., Maldonado, F., . . . Peikert, T. (2016). Computer-aided nodule assessment and risk yield risk management of adenocarcinoma: the future of imaging? Paper presented at the Seminars in thoracic and cardiovascular surgery.
  7. Gierada, D. S., Guniganti, P., Newman, B. J., Dransfield, M. T., Kvale, P. A., Lynch, D. A., & Pilgram, T. K. (2011). Quantitative CT assessment of emphysema and airways in relation to lung cancer risk. Radiology, 261(3), 950-959. doi:https://doi.org/10.1148/radiol.11110542
  8. Gierada, D. S., Pinsky, P., Nath, H., Chiles, C., Duan, F., & Aberle, D. R. (2014). Projected outcomes using different nodule sizes to define a positive CT lung cancer screening examination. Journal of the National Cancer Institute, 106(11), dju284. doi:10.1093/jnci/dju284
  9. Gunawan, R., Tran, Y., Zheng, J., Nguyen, H., & Chai, R. (2022). Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net. Sensors (Basel), 22(18). doi:https://doi.org/10.3390/s22187031
  10. Jamdade, V. A. (2022). Explainable Lung Nodule Malignancy Classification from CT Scans. (M.S. Thesis). University of Maryland, Baltimore County, USA, University of Maryland, Baltimore County ProQuest Dissertations Publishing. Retrieved from https://dissexpress.proquest.com/dxweb/results.html?QryTxt=&pubnum=29997250
  11. Jeon, K. N., Goo, J. M., Lee, C. H., Lee, Y., Choo, J. Y., Lee, N. K., . . . Gierada, D. S. (2012). Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening CT. Investigative radiology, 47(8), 457. doi:10.1097/RLI.0b013e318250a5aa
  12. Lo, S. B., Freedman, M. T., Gillis, L. B., White, C. S., & Mun, S. K. (2018). JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function. American Journal of Roentgenology, 210(3), 480-488. doi:10.2214/AJR.17.18718
  13. Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., . . . Barzilay, R. (2023). Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. J Clin Oncol, JCO2201345. doi:https://doi.org/10.1200/JCO.22.01345
  14. Patz Jr, E. F., Greco, E., Gatsonis, C., Pinsky, P., Kramer, B. S., & Aberle, D. R. (2016). 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. The Lancet Oncology, 17(5), 590-599. doi:https://doi.org/10.1016/S1470-2045(15)00621-X
  15. Perez-Morales, J., Tunali, I., Stringfield, O., Eschrich, S. A., Balagurunathan, Y., Gillies, R. J., & Schabath, M. B. (2020). Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening. Sci Rep, 10(1), 10528. doi:https://doi.org/10.1038/s41598-020-67378-8
  16. Petousis, P., Han, S. X., Aberle, D., & Bui, A. A. (2016). Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artificial intelligence in medicine, 72, 42-55. doi:https://doi.org/10.1016/j.artmed.2016.07.001
  17. Pilgram, T. K., Quirk, J. D., Bierhals, A. J., Yusen, R. D., Lefrak, S. S., Cooper, J. D., & Gierada, D. S. (2010). Accuracy of emphysema quantification performed with reduced numbers of CT sections. American Journal of Roentgenology, 194(3), 585-591. doi:10.2214/AJR.09.2709
  18. Pinsky, P. F., Nath, P. H., Gierada, D. S., Sonavane, S., & Szabo, E. (2014). Short-and long-term lung cancer risk associated with noncalcified nodules observed on low-dose CT. Cancer prevention research, 7(12), 1179-1185. doi:10.1158/1940-6207.CAPR-13-0438
  19. Pu, L., Gezer, N. S., Ashraf, S. F., Ocak, I., Dresser, D. E., & Dhupar, R. (2022). Automated segmentation of five different body tissues on computed tomography using deep learning. Med Phys. doi:https://doi.org/10.1002/mp.15932
  20. Reeves, A. P., Xie, Y., & Jirapatnakul, A. (2016). Automated pulmonary nodule CT image characterization in lung cancer screening. International Journal of Computer Assisted Radiology and Surgery, 11(1), 73-88. doi: 10.1007/s11548-015-1245-7
  21. Salama, W. M., Aly, M. H., & Elbagoury, A. M. (2021). Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach. Journal of Physics: Conference Series, 2128(1). doi:10.1088/1742-6596/2128/1/012011
  22. Schreuder, A., Jacobs, C., Gallardo-Estrella, L., Prokop, M., Schaefer-Prokop, C. M., & van Ginneken, B. (2019). Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances. PLoS One, 14(2), e0212756. doi:https://doi.org/10.1371/journal.pone.0212756
  23. Schreuder, A., van Ginneken, B., Scholten, E. T., Jacobs, C., Prokop, M., Sverzellati, N., . . . Schaefer-Prokop, C. M. (2018). Classification of CT pulmonary opacities as perifissural nodules: reader variability. Radiology, 288(3), 867-875. doi:https://doi.org/10.1148/radiol.2018172771
  24. Shields, B., & Ramachandran, P. (2023). Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept. Phys Eng Sci Med. doi:https://doi.org/10.1007/s13246-023-01302-y
  25. Singh, S., Gierada, D. S., Pinsky, P., Sanders, C., Fineberg, N., Sun, Y., . . . Nath, H. (2012). Reader variability in identifying pulmonary nodules on chest radiographs from the national lung screening trial. Journal of thoracic imaging, 27(4), 249. doi:10.1097/RTI.0b013e318256951e
  26. Singh, S., Pinsky, P., Fineberg, N. S., Gierada, D. S., Garg, K., Sun, Y., & Nath, P. H. (2011). Evaluation of reader variability in the interpretation of follow-up CT scans at lung cancer screening. Radiology, 259(1), 263-270. doi:10.1148/radiol.10101254
  27. Torres, F. S., Akbar, S., Raman, S., Yasufuku, K., Schmidt, C., Hosny, A., . . . Leighl, N. B. (2021). End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography. JCO Clin Cancer Inform, 5, 1141-1150. doi:10.1200/cci.21.00096
  28. Uthoff, J. M. (2019). Cancer Risk Assessment Using Quantitative Imaging Features from Solid Tumors and Surrounding Structures. (Ph.D. Dissertation). The University of Iowa, Ann Arbor, United States. Retrieved from https://www.proquest.com/dissertations-theses/cancer-risk-assessment-using-quantitative-imaging/docview/2306303717/se-2?accountid=142023 (2306303717, 13858412)
  29. Wu, D., Liu, R., Levitt, B., Riley, T., & Baumgartner, K. (2016). Evaluating long-term outcomes via computed tomography in lung cancer screening. J Biom Biostat, 7(313), 2. doi:10.4172/2155-6180.1000313
  30. Yip, R., Henschke, C. I., Xu, D. M., Li, K., Jirapatnakul, A., & Yankelevitz, D. F. (2017). Lung Cancers Manifesting as Part-Solid Nodules in the National Lung Screening Trial. American Journal of Roentgenology, 208(5), 1011-1021. doi:10.2214/Ajr.16.16930
  31. Yip, R., Yankelevitz, D. F., Hu, M., Li, K., Xu, D. M., Jirapatnakul, A., & Henschke, C. I. (2016). Lung cancer deaths in the National Lung Screening Trial attributed to nonsolid nodules. Radiology, 281(2), 589-596. doi:https://doi.org/10.1148/radiol.2016152333
  32. Zhao, T., & Yin, Z. (2021). Airway Anomaly Detection by Prototype-Based Graph Neural Network. Paper presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France.
  33. Zhu, C. S., Pinsky, P. F., Moler, J. E., Kukwa, A., Mabie, J., Rathmell, J. M., . . . Berg, C. D. (2017). Data sharing in clinical trials: An experience with two large cancer screening trials. PLoS medicine, 14(5), e1002304. doi:10.1371/journal.pmed.1002304

Previous Versions

Version 2: Updated 2015/12/14

Change: restoration of images that had become corrupted/missing during a storage transfer.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM

Version 1: Updated 2013/03/01

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM