Magnetic resonance imaging-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma after intensity-modulated radiotherapy

Background To develop a model based on magnetic resonance imaging (MRI) radiomics and clinical features for predicting radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Methods Two hundred and sixteen patients with NPC were retrospectively included. Radiomics features were extracted and selected. The logistic regression analysis was performed for prediction models construction. The area under the receiver operating characteristic curve (AUC) was calculated for performance evaluation. Results Three radiomics features were selected to construct the radiomics signature (AUC of 0.94 and 0.92). The clinical-radiomics model, integrating radiomics signature with T classification, achieved higher predictive performance in the training and validation cohorts (AUC of 0.95 and 0.93), as well as improved accuracy of the classification of RTLI outcomes (net reclassification improvement: 0.711; 95% CI: 0.57-0.86; p < 0.001). Conclusions The clinical-radiomics model and radiomics signature both showed great performance in predicting RTLI in patients with NPC.

Automated summary

This study developed a model based on MRI radiomics and clinical features to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC). The clinical-radiomics model and radiomics signature both showed excellent performance in predicting RTLI in patients with NPC.