Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study

Abstract

Purpose To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). Methods We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. A total of 144 texture features were extracted from the MRI before RHES + CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES + CCRT for NPC was explored. Results The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677). Conclusion The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES + CCRT early response in patients with NPC.