TY - JOUR
T1 - Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
AU - Parmar, Chintan
AU - Leijenaar, Ralph T. H.
AU - Grossmann, Patrick
AU - Velazquez, Emmanuel Rios
AU - Bussink, Johan
AU - Rietveld, Derek
AU - Rietbergen, Michelle M.
AU - Haibe-Kains, Benjamin
AU - Lambin, Philippe
AU - Aerts, Hugo J. W. L.
PY - 2015/6/5
Y1 - 2015/6/5
N2 - 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.
AB - 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.
U2 - 10.1038/srep11044
DO - 10.1038/srep11044
M3 - Article
C2 - 26251068
VL - 5
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
M1 - 11044
ER -