Fig. 3

Performance of the radiomic model in predicting post-radiation nasopharyngeal necrosis. A Visualization of the six radiomic features included in the signature. The changes in the individual radiomic features in patients with the highest and lowest probability of 1-year post-radiation nasopharyngeal necrosis (PRNN) are presented. B Output of the radiomic signature for each patient in the training cohort. C Receiver operating characteristic curves of the radiomic signature to predict 1-year PRNN in the training, internal validation, and external validation cohorts. D Comparison of the radiomic and clinical models to predict 1-year PRNN in the training cohort. E Calibration curve of the PRNN radiomic signature. The significance of calibration was analyzed using Hosmer–Lemeshow goodness-of-fit tests, with the P-value shown. F Clinical utility of the PRNN radiomic signature to guide re-radiotherapy based on decision curve analysis. With the threshold probability set at > 20%, using the radiomic signature to predict PRNN adds more benefit than either the treat-all-patients strategy or the treat-none strategy; the radiomic signature produces more net benefits than the clinical model. AUC area under the receiver operating characteristic curve