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

TitleDecoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
Publication TypeJournal Article
Year of Publication2014
AuthorsAerts, HJWL, Velazquez, ERios, Leijenaar, RTH, Parmar, C, Grossmann, P, Carvalho, S, Bussink, J, Monshouwer, R, Haibe-Kains, B, Rietveld, D, Hoebers, F, Rietbergen, MM, C Leemans, R, Dekker, A, Quackenbush, J, Gillies, RJ, Lambin, P
JournalNat Commun
Date Published2014 Jun 03
Publication Languageeng
KeywordsAdenocarcinoma, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Female, Head and Neck Neoplasms, Humans, Lung Neoplasms, Male, Multimodal Imaging, Phenotype, Positron-Emission Tomography, Prognosis, Tomography, X-Ray Computed, Tumor Burden

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.

Alternate JournalNat Commun
PubMed ID24892406
PubMed Central IDPMC4059926
Grant ListU01 CA143062 / CA / NCI NIH HHS / United States
NIH-USA U01CA 143062-01 / CA / NCI NIH HHS / United States