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  • RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

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Localtab Group


Localtab
activetrue
titleData Access

Data Access

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

Data TypeDownload all or Query/Filter
Gross Tumor Volume Segmentation - (DICOM RTSTRUCT and SEG,  912 MB)

Corresponding Original CT Images from RIDER Lung CT - (DICOM, 7 GB)

Click the Versions tab for more info about data releases.


Localtab
titleDetailed Description

Detailed Description

Image Statistics


Modalities (DICOM)

RTSTRUCT, SEG

Number of Patients

31

Number of Studies

31

Number of Series

118

Number of Images

118

Images Size (GB)912 MB
  • (RIDER-2283289298) only has segmentations associated with the retest.

  • (RIDER-5195703382) only has segmentations associated with the test.

  • (RIDER-8509201188) only has segmentations associated with the test.

  • (RIDER-9762593735) not included in the data set due to missing delineations.


Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

This analysis set may not be used for commercial purposes. It is available to browse, download, as outlined in the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0https://creativecommons.org/licenses/by-nc/3.0/.  See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.Users of this data must abide by the Creative Commons Attribution-NonCommercial 3.0 Unported License under which it has been published. Attribution should include references to the following citations:

Info
titleData Citation

Leonard Wee, Hugo Aerts, Petros Kalendralis and Andre Dekker. (2018) RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2020.jit9grk8


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. 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)


Info
titlePublication Citation

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Cavalho, S., … Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group. http://doi.org/10.1038/ncomms5006

Questions may be directed to help@cancerimagingarchive.net.

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): Updated 2020/02/13

Data TypeDownload all or Query/Filter
Gross Tumor Volume Segmentation - (DICOM RTSTRUCT and SEG,  912 MB)

Corresponding Original CT Images from RIDER Lung CT - (DICOM, 7 GB)



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