Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer

SIMPLE SUMMARY: A lack of external validation is still one of the major limitations of radiomics, hampering its clinical translation. The aim of this study was to build and externally validate an [(18)F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma treated with preoperative afatinib. Radiomic analysis of two cohorts of 20 and 34 patients was performed, where each cohort served once as a training and once as an external validation set. The radiomic model was compared to a clinical model and to a model that combined clinical and radiomic features. The radiomic model surpassed the clinical model in terms of predictive performance, but the combination of the radiomic and clinical model performed best. The [(18)F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival in HNSSC treated with preoperative afatinib. ABSTRACT: Aim: To build and externally validate an [(18)F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [(18)F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index). Results: In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82. Conclusion: Although assessed in two small but independent cohorts, an [(18)F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort.