title: Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer.
abstract: Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic features across cancer types, we investigated cancer-specific radiomic feature clusters in four independent Lung and Head &Neck (H) cancer cohorts (in total 878 patients). Radiomic features were extracted from the pre-treatment computed tomography (CT) images. Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H RS = 0.92, p < 0.001). Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features. Strongest associations with clinical parameters: Prognosis Lung CI = 0.60 ± 0.01, Prognosis H CI = 0.68 ± 0.01; Lung histology AUC = 0.56 ± 0.03, Lung stage AUC = 0.61 ± 0.01, H HPV AUC = 0.58 ± 0.03, H stage AUC = 0.77 ± 0.02. Full utilization of these cancer-specific characteristics of image features may further improve radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice.
authors.affiliation: 1] Departments of Radiation Oncology [2] Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, Maastricht, the Netherlands [3] Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
authors.forename: Chintan
authors.initials: C
authors.lastname: Parmar
authors.affiliation: Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, Maastricht, the Netherlands.
authors.forename: Ralph T H
authors.initials: RT
authors.lastname: Leijenaar
authors.affiliation: 1] Departments of Radiation Oncology [2] Department of Biostatistics &Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Grossmann
authors.affiliation: Departments of Radiation Oncology.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Rios Velazquez
authors.affiliation: Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
authors.forename: Johan
authors.initials: J
authors.lastname: Bussink
authors.affiliation: Department of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Derek
authors.initials: D
authors.lastname: Rietveld
authors.affiliation: Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Michelle M
authors.initials: MM
authors.lastname: Rietbergen
authors.affiliation: 1] Ontario Cancer Institute, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada [2] Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haibe-Kains
authors.affiliation: Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, Maastricht, the Netherlands.
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.affiliation: 1] Departments of Radiation Oncology [2] Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA [3] Department of Biostatistics &Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
authors.forename: Hugo J W L
authors.initials: HJ
authors.lastname: Aerts
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism; D006258:Head and Neck Neoplasms / Q000378:metabolism / Q000473:pathology*; D006801:Humans; D008175:Lung Neoplasms / Q000378:metabolism / Q000473:pathology*; D011379:Prognosis; D014057:Tomography, X-Ray Computed / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor