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

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DOI

doi:10.7937/K9/TCIA.2014..ua0JGpDg

URL: http://dx.doi.org/10.7937/K9/TCIA.2014..ua0JGpDg

Authors

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

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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Citation

When using this data, please cite the original publication:

 http://www.nature.com/ncomms/2014/140603/ncomms5006/full/ncomms5006.html

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.

More information about these data sets can be found at:

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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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