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.
Highlights
Recent advances of medical and computational science have led to the emergence of ‘precision medicine’, which has revolutionized the cancer care and medical science in general
In this study we investigated clustering as a means to deal with the high dimensional feature space generated with radiomics, as well as to investigate common and cancer-type specific radiomic patterns
We applied consensus clustering on 440 radiomic features extracted from Lung cancer and Head & Neck cancer patient cohorts
Summary
Recent advances of medical and computational science have led to the emergence of ‘precision medicine’, which has revolutionized the cancer care and medical science in general. A major proportion of precision medicine research has centered on unveiling different molecular characteristics of the disease tissues by using genomic and proteomic technologies. In spite of their enormous potential, these techniques have found limited implementations in routine clinical practice[1]. Routine clinical practice, tumor response to therapy is measured by the RECIST and/or WHO criteria, based on CT imaging These descriptors measure the change in size of tumors, and often do not succeed in predicting overall survival[5,6]. Identification of cancer-specific radiomic clusters provides a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice
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