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Description

Many Cancers routinely identified by imaging haven’t yet benefited from recent advances in computer science. Approaches such as machine learning and deep learning can generate quantitative tumor 3D volumes, complex features and therapy-tracking temporal dynamics. However, cross-disciplinary researchers striving to develop new approaches often lack disease understanding or sufficient contacts within the medical community. Their research can greatly benefit from labeling and annotating basic information in the images such as tumor locations, which are obvious to radiologists.

Crowd-sourcing the creation of publicly-accessible reference data sets could address this challenge. In 2011 the National Cancer Institute funded development of The Cancer Imaging Archive (TCIA), a free and open-access database of medical images. However, most of these collections lack the labeling and annotations needed by image processing researchers for progress in deep learning and radiomics. As a result, TCIA has partnered with the Radiological Society of North America (RSNA) and numerous academic centers to harness the vast knowledge of RSNA meeting attendees to generate these tumor markups.  Data sets annotated included CT scans from 352 subjects from the The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD), The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC), The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC), and The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV) collections on TCIA.

A full explanation of the project can be seen in the booth posters: 

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Download

Data resulting from this experiment is available in the following formats:

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• RSNA2017CCC-doiJNLP-e8nBWDCC.jnlp (Download Manager software requires Java to launch)

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• CrowdsCureCancer2017Annotations.csv

Detailed Description tab.

Localtab Group

Localtab active true title Data Access Data Access Data Type Download all or Query/Filter License Image Annotations (CSV, 925 kb) Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/33948774/CrowdsCureCancer2017Annotations.csv?api=v2 Tcia cc by 3 DICOM-SR files see note (ZIP, 3.7 Mb) Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/33948774/CrowdsCureCancer-dicomsrfiles_20180830.zip?api=v2 Tcia cc by 3 Clinical Data snapshot see note (CSV, 53kb) Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/33948774/ccc2017clinical.csv?api=v2 Tcia cc by 3 Collections Used in this Third Party Analysis Below is a list of the Collections used in these analyses: Source Data Type Download all or Query/Filter License Corresponding original source Images from TCGA-LUAD, TCGA-KIRC, TCGA-LIHC, TCGA-OV (DICOM, 24.2 GB) Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/33948774/RSNA2017CCC-doiJNLP-e8nBWDCC.tcia?api=v2  (Requires NBIA Data Retriever) Tcia cc by 3 The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD) The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC) The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV)  Localtab title Detailed Description Detailed Description DICOM-SR note:  The conversion XSLT and Makefile depends on pixelmed.jar as a DICOM toolkit,  and dicom3tools, dcsrdump and dciodvfy for validation. Clinical data note:  Because all subjects were pulled from The Cancer Genome Atlas cohorts, clinical data was available through the NCI Genomic Data Commons.  A CSV dump of that data is provided here for convenience. Booth posters Image Added Localtab title Citations & Data Usage Policy Citations & Data Usage Policy  Public collection license   Info title Data Citation Kalpathy-Cramer, J., Beers, A., Mamonov, A., Ziegler, E., Lewis, R., Almeida, A. B., Harris, G., Pieper, S., Sharma, A., Tarbox, L., Tobler, J., Prior, F., Flanders, A., Dulkowski, J., Fevrier-Sullivan, B., Jaffe, C., Freymann, J., & Kirby, J. (2019). Crowds Cure Cancer: Crowdsourced data collected at the RSNA 2017 annual meeting [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.OW73VLO2 Info title TCIA Citation Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7  Other Publications Using This Data TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact TCIA's Helpdesk. Localtab title Versions Version 1 (Current): 2018/05/17 Data Type Download all or Query/Filter Images (DICOM, 24.2 GB) Image Added  (Open this *.tcia manifest with NBIA Data Retriever) Image Annotations (CSV) Image Added DICOM-SR files (ZIP) * Image Added Clinical Data (CSV) ** Image Added *

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