Summary
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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) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, segmentation maps of tumors in the CT scans, and quantitative values obtained from the PET/CT scans. Imaging data are also paired with gene mutation, RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between genomic and medical image features, as well as the development and evaluation of prognostic medical image biomarkers. |
Further details regarding this data-set may be found in Bakr, et. al, Sci Data. 2018 Oct 16;5:180202. doi: 10.1038/sdata.2018.202, https://www.ncbi.nlm.nih.gov/pubmed/30325352.
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scientific and other inquiries about this dataset, please contact TCIA's Helpdesk.
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Click 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 Data Portal, where you can browse the data collection and/or download a subset of its contents.
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RNA sequence data (web)
Note: 130 subject subset
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Click the Versions tab for more info about data releases.
Third Party Analyses of this Dataset
TCIA encourages the community to publish your analyses of our datasets . Below is a list of such third party analyses published using this Collection:
- Crowds Cure Cancer: Data collected at the RSNA 2018 annual meeting (Crowds-Cure-2018)
- QIN multi-site collection of Lung CT data with Nodule Segmentations (QIN-LungCT-Seg)
- NSCLC Radiogenomics: Initial Stanford Study of 26 Cases
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title | Detailed Description |
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Detailed Description
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Collection Statistics
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Modalities
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CT, PT, SEG
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Number of Participants
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211
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Number of Studies
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303
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Number of Series
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1355
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Number of Images
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285,411
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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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Napel, Sandy, & Plevritis, Sylvia K. (2014). NSCLC Radiogenomics: Initial Stanford Study of 26 Cases. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014.X7ONY6B1 |
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title | Citations & Data Usage Policy |
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Citations & Data Usage Policy
Public collection license |
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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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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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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 Data
If you have a publication you'd like to add, please contact the TCIA Helpdesk.
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title | Versions |
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Version 4: Updated 2021/06/01
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Data Type
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Download all or Query/Filter
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(Requires the NBIA Data Retriever .)
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AIM Annotations (XML, zip)
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- 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
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Data Type
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(Requires the NBIA Data Retriever .)
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AIM Annotations (XML, zip)
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