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Other data sets in the Cancer Imaging Archive that were used in the same study published in Nature Communications: NSCLC-Radiomics, NSCLC-Radiomics-GenomicsHead-Neck-Radiomics-HN1RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (RIDER-LungCT-Seg).

For scientific or other inquiries about this dataset, please contact the TCIA Helpdesk.

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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. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Data TypeDownload all or Query/Filter
Images (DICOM, 3.2 GB)
Clinical Data (CSV)

Click the Versions tab for more info about data releases.


Localtab
titleDetailed Description

Detailed Description

Image Statistics


Modalities

CT, RTSTRUCT, SEG

Number of Participants

22

Number of Studies

22

Number of Series

64

Number of Images

3886

Images Size (GB)3.2



Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

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

Wee, L., Aerts, H. J.L., Kalendralis, P., & Dekker, A. (2019). Data from NSCLC-Radiomics-Interobserver1 [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.cwvlpd26.


Info
titlePublication Citation

Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach, Nature Communications, Volume 5, Article Number 4006, June 03, 2014. DOI: http://doi.org/10.1038/ncomms5006

Kalendralis, P., Shi, Z., Traverso, A., Choudhury, A., Sloep, M., Zhovannik, I., Starmans, M.P., Grittner, D., Feltens, P., Monshouwer, R., Klein, S., Fijten, R., Aerts, H., Dekker, A., van Soest, J. and Wee, L. (2020). FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, Interobserver, Lung1 and Head‐Neck1 TCIA collections. Medical Physics. DOI: http://doi.org/10.1002/mp.14322.


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. DOI: 10.1007/s10278-013-9622-7

Questions may be directed to help@cancerimagingarchive.net

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.


Localtab
titleVersions


Localtab
titleVersions

Version 3 (Current): Updated 2020/08/31

Data TypeDownload all or Query/Filter
Images (DICOM, 3.2 GB)

(Requires NBIA Data Retriever.)

Clinical Data (CSV)

Resolved the inadvertent mismatch of the labels between the DICOM Segmentations and the RTSTRUCT annotations. Version 2 was replaced.

Version 2: Updated 2019/10/18

Data TypeDownload all or Query/Filter
Images (DICOM, 3.2 GB)

(Requires NBIA Data Retriever.)

Clinical Data (CSV)

Added DICOM Segmentations for the primary tumor only, the ROI (GTV-1) for the RTSTRUCTs and DICOM Segs are the same.

Version 1: Updated 2019/06/02

Data TypeDownload all or Query/Filter
Images (DICOM, 2.0 GB)

(Requires NBIA Data Retriever.)

Clinical Data (CSV)




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