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Quantitative extraction of high-dimensional mineable data from medical imagesis a process known as radiomics. Radiomics is foreseen as an essentialprognostic tool for cancer risk assessment and the quantification ofintratumoural heterogeneity. In this work, 1615 radiomic features (quantifyingtumour image intensity, shape, texture) extracted from pre-treatment FDG-PETand CT images of 300 patients from four different cohorts were analyzed for therisk assessment of locoregional recurrences (LR) and distant metastases (DM) inhead-and-neck cancer. Prediction models combining radiomic and clinicalvariables were constructed via random forests and imbalance-adjustmentstrategies using two of the four cohorts. Independent validation of theprediction and prognostic performance of the models was carried out on theother two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).Furthermore, the results obtained via Kaplan-Meier analysis demonstrated thepotential of radiomics for assessing the risk of specific tumour outcomes usingmultiple stratification groups. This could have important clinical impact,notably by allowing for a better personalization of chemo-radiation treatmentsfor head-and-neck cancer patients from different risk groups.

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer