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CROWDS-CURE-2017

Crowds-Cure-2017 | Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting

DOI: 10.7937/K9/TCIA.2018.OW73VLO2 | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Size Supporting Data Updated
Lung Adenocarcinoma, Renal Clear Cell, Liver, Ovarian Chest, Kidney, Liver, Ovary 352 4.64MB Lesion measurements 2018/05/17

Summary

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 Detailed Description.

Data Access

Version 1: Updated 2018/05/17

Title Data Type Format Access Points Subjects Studies Series Images License
Image Annotations CSV CC BY 3.0
DICOM-SR files see note SR ZIP and DICOM CC BY 3.0
Clinical Data snapshot see note CSV CC BY 3.0

Collections Used In This Analysis Result

Title Data Type Format Access Points Subjects Studies Series Images License
Corresponding original source Images from TCGA-LUAD, TCGA-KIRC, TCGA-LIHC, TCGA-OV CT DICOM 352 352 443 49,211 CC BY 3.0

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
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

Detailed Description

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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.

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. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Collections Used In This Analysis Result

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