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  • Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting (Crowds-Cure-2017)

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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.

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Localtab Group



Localtab
activetrue
titleData Access

Data Access

Click the theDownload   button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever the data.

Data TypeDownload all or Query/Filter
Images (DICOM)

 (Open this *.tcia manifest with NBIA Data Retriever)

Image Annotations (CSV)

DICOM-SR files (ZIP, 3.7 Mb) *

DICOM SR files

Clinical Data (CSV, 53kb) **


* The conversion XSLT and Makefile depends on pixelmed.jar as a DICOM toolkit,  and dicom3tools, dcsrdump and dciodvfy for validation.

** 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.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Collections Used in this Third Party Analyses
Below is a list of the Collections used in these analyses:




Localtab
titleDetailed Description

Detailed Description

Booth posters






Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Public collection license

Info
titleData Citation
Jayashree Kalpathy-Cramer, Andrew Beers, Artem Mamonov, Erik Ziegler, Rob Lewis, Andre Botelho Almeida, Gordon Harris, Steve Pieper, David Clunie, Ashish Sharma, Lawrence Tarbox, Jeff Tobler, Fred Prior, Adam Flanders, Jamie Dulkowski,  Brenda Fevrier-Sullivan,  Carl Jaffe, John Freymann, Justin Kirby. Crowds Cure Cancer: Data collected at the RSNA 2017 annual meetingJ., 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. DOI: https://doi.org/10.7937/K9/TCIA.2018.OW73VLO2


Info
titleTCIA 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, Volume 26, Number (6), pp 1045-1057. DOI: 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 the TCIA Helpdesk.




Localtab
titleVersions

Version 1 (Current): 2018/05/17


Data TypeDownload all or Query/Filter
Images (DICOM)


 (Open this *.tcia manifest with NBIA Data Retriever)

Image Annotations (CSV)

DICOM-SR files (ZIP) *

DICOM SR files

Clinical Data (CSV) **


* The conversion XSLT and Makefile depends on pixelmed.jar as a DICOM toolkit,  and dicom3tools, dcsrdump and dciodvfy for validation.

** 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.



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