Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Hugo J. W. L. Aerts (), Emmanuel Rios Velazquez, Ralph T. H. Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Carvalho, Johan Bussink, René Monshouwer, Benjamin Haibe-Kains, Derek Rietveld, Frank Hoebers, Michelle M. Rietbergen, C. René Leemans, Andre Dekker, John Quackenbush, Robert J. Gillies and Philippe Lambin
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Hugo J. W. L. Aerts: Research Institute GROW, Maastricht University
Emmanuel Rios Velazquez: Research Institute GROW, Maastricht University
Ralph T. H. Leijenaar: Research Institute GROW, Maastricht University
Chintan Parmar: Research Institute GROW, Maastricht University
Patrick Grossmann: Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School
Sara Carvalho: Research Institute GROW, Maastricht University
Johan Bussink: Radboud University Medical Center Nijmegen, PB 9101
René Monshouwer: Radboud University Medical Center Nijmegen, PB 9101
Benjamin Haibe-Kains: Princess Margaret Cancer Centre, University of Toronto
Derek Rietveld: VU University Medical Center
Frank Hoebers: Research Institute GROW, Maastricht University
Michelle M. Rietbergen: VU University Medical Center
C. René Leemans: VU University Medical Center
Andre Dekker: Research Institute GROW, Maastricht University
John Quackenbush: Dana-Farber Cancer Institute
Robert J. Gillies: H. Lee Moffitt Cancer Center and Research Institute
Philippe Lambin: Research Institute GROW, Maastricht University

Nature Communications, 2014, vol. 5, issue 1, 1-9

Abstract: 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.

Date: 2014
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DOI: 10.1038/ncomms5006

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