Localtab |
---|
active | true |
---|
title | Data Access |
---|
| Data Access
Data Type | Download all or Query/Filter | License |
---|
Radiology CT Images (26254 subjects, DICOM, 11.3 TB) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/5800702/manifest-NLST_allCT.tcia?version=1&modificationDate=1633563007179&api=v2 |
---|
|
|
Tcia button generator |
---|
label | Search |
---|
url | https://nlst.cancerimagingarchive.net/nbia-search/ |
---|
|
|
This link downloads the entire collection, which is quite large. See the Detailed Description tab for options to download the collection in smaller chunks. (Download requires the NBIA Data Retriever)
| | Tissue Slide Images - Primary Tumor Tissue Slide Images (451 subjects, 1225 files, SVS, 775 GB) |
Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU1MCIsInBhc3Njb2RlIjoiNjI5NzNjMmExYTFlNGNlZDZhNzhlOWI3Mzc1MmJmMWE3MGQ2ZWEzOCIsInBhY2thZ2VfaWQiOiI1NTAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
---|
|
|
Tcia button generator |
---|
label | Search |
---|
url | https://pathdb.cancerimagingarchive.net/imagesearch?f[0]=collection:nlst |
---|
|
|
Additional images are available: See Detailed Description. (Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)
| | Clinical data including data dictionaries (SAS, ZIP, 25 MB) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/5800702/package-nlst-780.2021-05-28.zip?version=1&modificationDate=1633562878492&api=v2 |
---|
|
|
Provided in SAS format in one compressed file (.zip); includes data and dictionaries. | | Additional histopathology slide images Table 1 for which the participants have no Baseline Questionnaire data (2 subjects, DOCX, 13 KB) | Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/10519121/Table%201-%20Participants%20without%20a%20Baseline%20Questionairre.docx?version=1&modificationDate=1633631313724&api=v2 |
---|
|
|
| | Additional histopathology Histopathology additional slide images images for which the participants have no Baseline Questionnaire data (2 subjects, 4 files, SVS) | Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU3MCIsInBhc3Njb2RlIjoiMTMyZmU3OTIwYWQwYTM1YmQwMjkxNzIzYzk2NzRkNDRhNDc1Yjk3NiIsInBhY2thZ2VfaWQiOiI1NzAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
---|
|
|
(Download and apply the IBM-Aspera-Connect plugin to your browser) | | Additional histopathology slide images Table 2 for participants with Second Primary Tumors as well as those included in the "standard" package (10 subjects, 23 images, DOCX, 23 KB) | Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/10519121/Table%202-%20Participants%20with%20a%20Second%20Primary%20Lung%20Cancer%2023%20additional%20pathology%20slide%20images.docx?version=1&modificationDate=1633631331226&api=v2 |
---|
|
|
| | Additional histopathology Histopathology additional slide images for participants with Second Primary Tumors as well as those included in the "standard" package (10 subjects, 23 files, SVS, 18.7 GB) | Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU0NSIsInBhc3Njb2RlIjoiOTEwMWE5N2NkYWY5MmIyN2RiMTczNTg0Y2Q5MTBmMzZiNjVlMTQxNCIsInBhY2thZ2VfaWQiOiI1NDUiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
---|
|
|
(Download and apply the IBM-Aspera-Connect plugin to your browser) | |
Click the Versions tab for more info about data releases. Please contact help@cancerimagingarchive.net with any questions regarding usage.
Nci_crdc additional resources |
---|
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 This is a subset of the full clinical data. If you need the full clinical data, please visit the Cancer Data Access System (CDAS) system. |
Localtab |
---|
title | Detailed Description |
---|
| Detailed DescriptionCollection Statistics | Radiology | Pathology |
---|
Modalities | CT | Aperio | Number of Patients | 26,254 | 451 | Number of Studies | 73,118 |
| Number of Series | 203,099 |
| Number of Images | 21,082,502 | 1,225 (optionally + 4 + 23) | Images Size (TB) | 11.3 TB | 775 GB |
The full CT data (manifest-NLST_allCT.tcia) occupy 11.3 terabytes when downloaded. For convenience, you can "Search" to access all the files, or you can download in chunks. The pathology slide data: Primary Tumor slides (faspex) Primary Tumor slides (the standard package), 1225 files. Additional slides (faspex) Additional histopathology slide images for which the participants have no Baseline Questionnaire data (4 slides) Detail in Table 1. 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.
- Questionnaires, screening, diagnostic procedures, cancer diagnosis, treatment, progression, mortality, contamination.
Biospecimens CollectedFormalin-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.
|
Localtab |
---|
title | Citations & Data Usage Policy |
---|
| Citations & Data Usage Policy Tcia limited license policy |
---|
Info |
---|
title | 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 |
Info |
---|
| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: https://doi.org/10.1007/s10278-013-9622-7 |
Other Publications Using This DataThe 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 - 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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)
- 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
- 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
- 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
- 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.
- 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
|
Localtab |
---|
| Version 3 (Current) : Updated 2021/09/24Data Type | Download all or Query/Filter |
---|
CT Images (DICOM, 11.3 TB) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/5800702/manifest-NLST_allCT.tcia?version=1&modificationDate=1633563007179&api=v2 |
---|
|
|
Tcia button generator |
---|
label | Search |
---|
url | https://nlst.cancerimagingarchive.net/nbia-search/ |
---|
|
|
This link downloads the entire collection, which is quite large, as legacy single frame images. See the Detailed Description tab for options to download the collection in smaller chunks.
(Download requires the NBIA Data Retriever) | Primary Tumor Tissue Slide Images (SVS, 775 GB) |
Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU1MCIsInBhc3Njb2RlIjoiNjI5NzNjMmExYTFlNGNlZDZhNzhlOWI3Mzc1MmJmMWE3MGQ2ZWEzOCIsInBhY2thZ2VfaWQiOiI1NTAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
---|
|
|
Tcia button generator |
---|
label | Search |
---|
url | https://pathdb.cancerimagingarchive.net/imagesearch?f[0]=collection:nlst |
---|
|
|
Additional images are available: See Detailed Description. (Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | Clinical data (ZIP, 25 MB) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/5800702/package-nlst-780.2021-05-28.zip?version=1&modificationDate=1633562878492&api=v2 |
---|
|
|
(more info) Provided in SAS format in one compressed file (.zip); includes data and dictionaries. This is a subset of the full clinical data. If you need the full clinical data, please visit the Cancer Data Access System (CDAS) system. |
|
|
Data embargo of limited access is lifted September 2021, with the addition of downloadable pathology slide data and clinical data spreadsheet & dictionaries. Version 2: Updated 2015/12/14Data Type | Download all or Query/Filter |
---|
Images (DICOM, 11.3TB) |
Tcia button generator |
---|
label | Search |
---|
url | https://queries.cancerimagingarchive.net/NLSTQueryTool/faces/main.xhtml |
---|
|
|
|
Change: restoration of images that had become corrupted/missing during a storage transfer. Version 1: Updated 2013/03/01Data Type | Download all or Query/Filter |
---|
Images (DICOM, 11.3TB) |
Tcia button generator |
---|
label | Search |
---|
url | https://queries.cancerimagingarchive.net/NLSTQueryTool/faces/main.xhtml |
---|
|
|
|
|
|