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  • Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (Radiomics-Tumor-Phenotypes)

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Description

This data applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer which are described in Nature Communications (http://doi.org/10.1038/ncomms5006).  The various arms of the study are represented in TCIA as distinct Collections including NSCLC-Radiomics (Lung1), NSCLC-Radiomics-Genomics (Lung3), Head-Neck-Radiomics-HN1 (H&N1), NSCLC-Radiomics-Interobserver1 (Multiple delineation), and RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (RIDER-LungCT-Seg) (RIDER test/retest).

Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

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Localtab Group


  • Gene Expression Data

Collections Used in this Analysis

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA No Commercial Limited Access License to help@cancerimagingarchive.net before accessing this portion of the data.

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
Image Data (DICOM) and Clinical Data

Please refer to each Collection page to download available images and clinical data:

NSCLC
Head
Neck
Radiomics
Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

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

Additional Resources for this Dataset

The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

 
  • (Lung3)
Gene Expression Data

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58661

Source Data Type

Download

License

Corresponding Original Images from Head-Neck-Radiomics-HN1 (H&N1) (DICOM)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/52762760/Head-Neck-Radiomics-HN1-Version%202-Sept%202019%20NBIA-manifest.tcia?version=1&modificationDate=1568995984096&api=v2



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labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=HEAD-NECK-RADIOMICS-HN1


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TCIA No Commercial Limited


Corresponding Original Images from NSCLC-Radiomics (Lung1), NSCLC-Radiomics-Genomics (Lung3), NSCLC-Radiomics-Interobserver1 (Multiple delineation) (DICOM)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/18514090/manifest-20230519_CC3-NC.tcia?api=v2



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labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=NSCLC-Radiomics,NSCLC-Radiomics-Interobserver1,NSCLC-Radiomics-Genomics



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Corresponding Original Images from RIDER-LungCT-Seg (RIDER test/retest)  (DICOM)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/46334165/RIDER%20Lung%20CT%20RTSTRUCTS%20DICOM%20SEGS%20Leonard%20Wee%20Feb%2010%202020.tcia?api=v2



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



Localtab
titleDetailed Description

Detailed Description



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:

Tcia limited license policy

Info
titleDataset Data Citation

Hugo J. W. L. Aerts; Emmanuel Rios Velazquez; Ralph Aerts, H., Velazquez, E. R., Leijenaar, R. T. H. Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M. Rietbergen; C. René Leemans; Andre Dekker; John Quackenbush; Robert J. Gillies; Philippe Lambin, Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. (2014). Data from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (Radiomics-Tumor-Phenotypes). [Data set]. The Cancer Imaging Archive. http https://doi.org/10.7937/K9/TCIA.2014..UA0JGPDG


Info
TCIA Citation
title

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)

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

Info
titlePublication Citation

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., CavalhoCarvalho, S., … Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group. http, 5(1). https://doi.org/10.1038/ncomms5006

Questions may be directed to help@cancerimagingarchive.net. 


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, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7


Other Publications Using This Data

TCIA maintainsmaintains a list of publications that leverage TCIA our data. If you have a manuscript you'd like to add please contact the TCIA's Helpdesk.


Localtab
titleVersions

Version 2 (Current): 2020/03/23

Added links to the recently published TCIA collections which reflect the additional arms of the study described in Nature Communications (http://doi.org/10.1038/ncomms5006).


Data TypeDownload all or Query/Filter
Image Data (DICOM) and Clinical Data

Please refer to each Collection page to download available images and clinical data:

NSCLC-Radiomics-Genomics (Lung3)

Gene Expression Data

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58661

Version 1

(Current)

: 2016/08/02

Data TypeDownload all or Query/Filter
Image Data (DICOM)

Clinical Data (CSV, XLS)

ClinicalMetadata

Gene Expression Data



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