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

Autor: Aerts HJ; 1] Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands [2] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA [3] Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA [4] Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215-5450, USA [5]., Velazquez ER; 1] Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands [2] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA [3]., Leijenaar RT; Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands., Parmar C; 1] Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands [2] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA., Grossmann P; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA., Carvalho S, Bussink J; Department of Radiation Oncology, Radboud University Medical Center Nijmegen, PB 9101, 6500HB Nijmegen, The Netherlands., Monshouwer R; Department of Radiation Oncology, Radboud University Medical Center Nijmegen, PB 9101, 6500HB Nijmegen, The Netherlands., Haibe-Kains B; Princess Margaret Cancer Centre, University Health Network and Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada M5G 1L7., Rietveld D; Department of Radiation Oncology, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands., Hoebers F; Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands., Rietbergen MM; Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands., Leemans CR; Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands., Dekker A; Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands., Quackenbush J; Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215-5450, USA., Gillies RJ; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA., Lambin P; Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2014 Jun 03; Vol. 5, pp. 4006. Date of Electronic Publication: 2014 Jun 03.
DOI: 10.1038/ncomms5006
Abstrakt: 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.
Databáze: MEDLINE
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