Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

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• Radboud University Nijmegen (repository.ubn.ru.nl)Preprint · PDF
• National Institutes of Health (ncbi.nlm.nih.gov)Preprint · Web page
• Harvard University (nrs.harvard.edu)Preprint · Web page
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• Radboud University Nijmegen (hdl.handle.net)Preprint · Web page

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.