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Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006.

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

Author information

1
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].
2
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].
3
Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands.
4
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.
5
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA.
6
Department of Radiation Oncology, Radboud University Medical Center Nijmegen, PB 9101, 6500HB Nijmegen, The Netherlands.
7
Princess Margaret Cancer Centre, University Health Network and Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada M5G 1L7.
8
Department of Radiation Oncology, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands.
9
Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands.
10
Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215-5450, USA.
11
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA.

Erratum in

  • Nat Commun. 2014;5:4644. Cavalho, Sara [corrected to Carvalho, Sara].

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.

PMID:
24892406
PMCID:
PMC4059926
DOI:
10.1038/ncomms5006
[Indexed for MEDLINE]
Free PMC Article

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