Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients

SIMPLE SUMMARY: The prognostic performance of traditional methodologies in advanced nasopharyngeal carcinoma does not allow to successfully stratify patients. Previous studies showed that MRI-radiomics has been used to give additional information to improve the prognosis for this type of pathology in patients from endemic areas (Asia). The purpose of this study was to use MRI-radiomics to develop prognostic models for overall survival in patients from non-endemic areas (Europe or United States). In particular, T1-weighted and T2-weighted MRI were used for the purpose. Radiomic features from those images allowed to successfully train a prognostic signature that improved the prognostic performance of models based on clinical variables alone for different clinical endpoints (overall survival, disease-free survival and loco-regional recurrence-free survival). These results suggest how MRI-radiomics is a useful additional tool for prognosis in nasopharyngeal cancer. ABSTRACT: Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.