Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

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Title: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Author: Aerts, Hugo J. W. L.; Velazquez, Emmanuel Rios; Leijenaar, Ralph T. H.; Parmar, Chintan; Grossmann, Patrick; Cavalho, Sara; Bussink, Johan; Monshouwer, René; Haibe-Kains, Benjamin; Rietveld, Derek; Hoebers, Frank; Rietbergen, Michelle M.; Leemans, C. René; Dekker, Andre; Quackenbush, John; Gillies, Robert J.; Lambin, Philippe

Note: Order does not necessarily reflect citation order of authors.

Citation: Aerts, H. J. W. L., E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Cavalho, J. Bussink, et al. 2014. “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.” Nature Communications 5 (1): 4006. doi:10.1038/ncomms5006. http://dx.doi.org/10.1038/ncomms5006.
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Abstract: Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is 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.
Published Version: doi:10.1038/ncomms5006
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059926/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12406714
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