Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

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

SUBMITTER: Aerts HJ 

PROVIDER: S-EPMC4059926 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature