Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer

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Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer

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Title: Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Author: Parmar, Chintan; Leijenaar, Ralph T. H.; Grossmann, Patrick; Rios Velazquez, Emmanuel; Bussink, Johan; Rietveld, Derek; Rietbergen, Michelle M.; Haibe-Kains, Benjamin; Lambin, Philippe; Aerts, Hugo J.W.L.

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Citation: Parmar, Chintan, Ralph T. H. Leijenaar, Patrick Grossmann, Emmanuel Rios Velazquez, Johan Bussink, Derek Rietveld, Michelle M. Rietbergen, Benjamin Haibe-Kains, Philippe Lambin, and Hugo J.W.L. Aerts. 2015. “Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer.” Scientific Reports 5 (1): 11044. doi:10.1038/srep11044. http://dx.doi.org/10.1038/srep11044.
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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∓N) 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 & N cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H & N 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 & N CI = 0.68 ± 0.01; Lung histology AUC = 0.56 ± 0.03, Lung stage AUC = 0.61 ± 0.01, H & N HPV AUC = 0.58 ± 0.03, H & N 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.
Published Version: doi:10.1038/srep11044
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937496/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:27822129
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