Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Velazquez, Emmanuel Rios
Leijenaar, Ralph T. H.
Rietbergen, Michelle M.
Leemans, C. René
Gillies, Robert J.
Lambin, PhilippeNote: Order does not necessarily reflect citation order of authors.
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CitationAerts, 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.
AbstractHuman 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12406714
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