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

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titleDataset Citation

Hugo J. W. L. Aerts; Emmanuel Rios Velazquez; Ralph 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. (2014). Data from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014..UA0JGPDG

Description

In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. 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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activetrue
titleData Access

Data Access

Note: This data is restricted for commercial use.  Please contact Hugo Aerts, hugo_aerts@dfci.harvard.edu with any questions on usage.

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)
Clinical Data (CSV, XLS)
Gene Expression Data

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


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titleDetailed Description

Detailed Description

More information about these data sets can be found at:

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Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Info
titleDataset Citation

Hugo J. W. L. Aerts; Emmanuel Rios Velazquez; Ralph 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. (2014). Data from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014..UA0JGPDG


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)

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., 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

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): 2016/08/02

Data TypeDownload all or Query/Filter
Image Data (DICOM)
Clinical Data (CSV, XLS)
Gene Expression Data

Note: This data is restricted for commercial use.  Please contact Hugo Aerts, hugo_aerts@dfci.harvard.edu with any questions on usage

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