Localtab |
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title | Data Access |
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| Data AccessClick the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search
button to open our Portal, where you can browse the data collection and/or download a subset of its contents.Data Type | Download all or Query/Filter | License |
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Images and Segmentations (DICOM, 97.6 GB) |
| cc-by-3AIM Annotations (XML, zip) | | Clinical Data (csv) | button-generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/28672347/NSCLC_Radiogenomics-6-1-21%20Version%204.tcia?version=1&modificationDate=1622561925765&api=v2 |
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Tcia button generator |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=NSCLC%20Radiogenomics |
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(Download requires the NBIA Data Retriever) | | AIM Annotations (XML, zip) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/28672347/AIM_files_updated-11-10-2020.zip?version=1&modificationDate=1605631114167&api=v2 |
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Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/28672347/NSCLCR01Radiogenomic_DATA_LABELS_2018-05-22_1500-shifted.csv?version=1&modificationDate=1531967714295&api=v2 |
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| Image Removed Click the Versions tab for more info about data releases. Additional Resources for this DatasetThe 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. Third Party Analyses of this DatasetTCIA encourages the community to publish your analyses of our datasets . Below is a list of such third party analyses published using this Collection: |
Localtab |
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title | Detailed Description |
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| Detailed Description
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Modalities | CT, PT, SEG | Number of Participants | 211 | Number of Studies | 303 | Number of Series | 1355 | Number of Images | 285,411 | Image Size (GB) | 97.6 |
This collection was originally submitted to TCIA as a 26 subject pilot data set. You can learn more about that subset of the collection in the following Analysis Results publication:
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Localtab |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia limited license policy |
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Info |
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| Bakr, Shaimaa; Gevaert, Olivier; Echegaray, Sebastian; Ayers, Kelsey; Zhou, Mu; Shafiq, Majid; Zheng, Hong; Zhang, Weiruo; Leung, Ann; Kadoch, Michael; Shrager, Joseph; Quon, Andrew; Rubin, Daniel; Plevritis, Sylvia; Napel, Sandy.(2017). Data for NSCLC Radiogenomics Collection. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2017.7hs46erv |
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title | Publication Citation |
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| Bakr, S., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Benson, J. A., Zhang, W., Leung, A., Kadoch, M., Hoang, C. D., Shrager, J., Quon, A., Rubin, D. L., Plevritis, S. K., & Napel, S. (2018). A radiogenomic dataset of non-small cell lung cancer. Scientific data, 5, 180202. https://doi.org/10.1038/sdata.2018.202 |
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title | Publication Citation |
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| Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N., Xu, Y., Quon, A., … Plevritis, S. K. (2012, August). Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results. Radiology. Radiological Society of North America (RSNA). http://doi.org/10.1148/radiol.12111607 |
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| 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. (paper) |
Other Publications Using This DataIf you have a publication you'd like to add, please contact the TCIA Helpdesk. - Aonpong, P., Iwamoto, Y., Wang, W., Lin, L., & Chen, Y.-W. (2020). Hand-Crafted and Deep Learning-Based Radiomics Models for Recurrence Prediction of Non-Small Cells Lung Cancers. Innovation in Medicine and Healthcare, 192, 135-144. doi:https://doi.org/10.1007/978-981-15-5852-8_13
- Aonpong, P., Iwamoto, Y., Wang, W., Lin, L., & Chen, Y.-W. (2021). Genomics-Based Models for Recurrence Prediction of Non-small Cells Lung Cancers. Paper presented at the KES International Conferences on Innovation in Medicine and Healthcare (KES-InMed-21), Online only.
- Choi, J., Cho, H. H., Kwon, J., Lee, H. Y., & Park, H. (2021). A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT. Diagnostics (Basel), 11(6). doi:10.3390/diagnostics11061047
- Großmann, P. B. H. J., & Grossmann, P. B. H. J. (2018). Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers. (PhD Ph.D. Thesis). Universitaire Pers Maastricht Maastricht. Retrieved from https://cris.maastrichtuniversity.nl/portal/files/24774850/c5959.pdf
- Kadoya, N., Tanaka, S., Kajikawa, T., Tanabe, S., Abe, K., Nakajima, Y., . . . Jingu, K. (2020). Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics. Med Phys. doi:10.1002/mp.14104
- Kalpathy-Cramer, J., Mamomov, A., Zhao, B., Lu, L., Cherezov, D., Napel, S., . . . Goldgof, D. (2016). Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography: a journal for imaging research, 2(4), 430-437. doi:10.18383/j.tom.2016.00235
- Koyasu, S., Nishio, M., Isoda, H., Nakamoto, Y., & Togashi, K. (2020). Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on (18)F FDG-PET/CT. Ann Nucl Med, 34(1), 49-57. doi:https://doi.org/10.1007/s12149-019-01414-0
- Leitner, B. P., & Perry, R. J. (2020). The Impact of Obesity on Tumor Glucose Uptake in Breast and Lung Cancer. JNCI Cancer Spectrum. doi:10.1093/jncics/pkaa007
- Mattonen, S. A., Davidzon, G. A., Benson, J., Leung, A. N. C., Vasanawala, M., Horng, G., . . . Nair, V. S. (2019). Bone Marrow and Tumor Radiomics at (18)F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology, 190357. doi:10.1148/radiol.2019190357
- Mienye, I. D. (2021). Improved Machine Learning Algorithms with Application to Medical Diagnosis. (Ph. D. Dissertation
Ph.D.). University of Johannesburg, Retrieved from https://www.researchgate.net/profile/Domor-Mienye/publication/350788174_Improved_Machine_Learning_Algorithms_with_Application_to_Medical_Diagnosis/links/6071d56092851c8a7bba864f/Improved-Machine-Learning-Algorithms-with-Application-to-Medical-Diagnosis.pdf Available from TCIA 10.7937/K9/TCIA.2017.7hs46erv database. - Moitra, D., & Kr. Mandal, R. (2020). Classification of non-small cell lung cancer using one-dimensional convolutional neural network. Expert Systems with Applications, 159, 113564. doi:https://doi.org/10.1016/j.eswa.2020.113564
- Moitra, D., & Mandal, R. K. (2019). Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN). Health Inf Sci Syst, 7(1), 14. doi:10.1007/s13755-019-0077-1
- Moitra, D., & Mandal, R. K. (2019). Automated grading of non-small cell lung cancer by fuzzy rough nearest neighbour method. Network Modeling Analysis in Health Informatics and Bioinformatics, 8(1). doi:10.1007/s13721-019-0204-6
- Moitra, D., & Mandal, R. K. (2020). Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network. Journal of Digital Imaging, 1-8. doi:10.1007/s10278-020-00337-x
- Morgado, J., Pereira, T., Silva, F., Freitas, C., Negrão, E., de Lima, B. F., . . . Oliveira, H. P. (2021). Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer. Applied Sciences, 11(7), 3273. doi:10.3390/app11073273
- Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., . . . Gevaert, O. (2020). A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. Nature Machine Intelligence, 2, 274-282. doi:https://doi.org/10.1038/s42256-020-0173-6
- Nishio, M., Nishizawa, M., Sugiyama, O., Kojima, R., Yakami, M., Kuroda, T., & Togashi, K. (2018). Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. PLoS One, 13(4), e0195875. doi:10.1371/journal.pone.0195875
- Saad, M., & Choi, T.-S. (2018). Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Computerized Medical Imaging and Graphics, 67, 1-8. doi:10.1016/j.compmedimag.2018.04.003
- Thomas, R., Schalck, E., Fourure, D., Bonnefoy, A., & Cervera-Marzal, I. (2021). 2Be3-Net: Combining 2D and 3D Convolutional Neural Networks for 3D PET Scans Predictions. Paper presented at the International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021).
- 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
- Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi:10.3389/fonc.2021.637804
- Yousefi, B., Jahani, N., LaRiviere, M. J., Cohen, E., Hsieh, M.-K., Luna, J. M., . . . Kontos, D. (2019). Correlative hierarchical clustering-based low-rank dimensionality reduction of radiomics-driven phenotype in non-small cell lung cancer. Paper presented at the SPIE Medical Imaging, San Diego, California, United States.
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Localtab |
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| Version 4: Updated 2021/06/01 Data Type | Download all or Query/Filter |
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Images (DICOM, 97.6 GB) |
(Requires the Download requires the NBIA Data Retriever .)
| AIM Annotations (XML, zip) | | Clinical Data (csv) | | - Added missing image studies for the following cases: R01-009 (CT), R01-100 (PET/CT), and R01-111 (PET/CT).
- SUV conversion factor DICOM tag (7053,1000) was added for the following Philips PET images: R01-074, R01-077, R01-079, R01-089, R01-98 and R01-137.
Version 3: Updated 2020/11/10 Data Type | Download all or Query/Filter |
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Images (DICOM, 97.6 GB) |
(Requires the Download requires the NBIA Data Retriever .)
| AIM Annotations (XML, zip) | | Clinical Data (csv) | | - A new version of RO1-023 was created to correct a cranial-caudal flip of the segmentation of the CT volume (483 images) and associated Segmentation object. The UIDs of the other scans were updated to preserve Study level consistency but were otherwise unmodified. The referenced UIDs within the AIM object for RO1-023 were updated and renamed to RO1-023v1.
- RO1-038 was updated to remove a coronal slice at the start of the of the CT volume. This created difficulty for some software to determine slice spacing.
Version 2 (Current): Updated 2017/02/28Data Type | Download all or Query/Filter |
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Images (DICOM, 97.6 GB) | | AIM Annotations (XML, zip) | | Clinical Data (csv) | |
Version 1: Updated 2015/12/22
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