Crowds-Cure-2017 | Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting
DOI: 10.7937/K9/TCIA.2018.OW73VLO2 | Data Citation Required | 48 Views | 2 Citations | Analysis Result
| Location | Subjects | Size | Updated | |||
|---|---|---|---|---|---|---|
| Lung Adenocarcinoma, Renal Clear Cell, Liver, Ovarian | Chest, Kidney, Liver, Ovary | 352 | 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 | License | |||
|---|---|---|---|---|---|---|---|---|
| Image Annotations | Measurement | CSV | 352 | CC BY 3.0 | ||||
| DICOM-SR files see note | SR, Measurement | ZIP and DICOM | CC BY 3.0 | |||||
| Clinical Data snapshot see note | Demographic, Follow-Up | CSV | CC BY 3.0 |
Collections Used In This Analysis Result
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Corresponding original source Images from TCGA-LUAD, TCGA-KIRC, TCGA-LIHC, TCGA-OV | CT | DICOM | Requires NBIA Data Retriever |
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 |
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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
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Detailed Description
- DICOM-SR note: The conversion XSLT and Makefile depends on pixelmed.jar as a DICOM toolkit (https://www.pixelmed.com/dicomtoolkit.html) , 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
Related Publications
Publications by the Dataset Authors
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Research Community Publications
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