Whole-Tumor Histogram and Texture Imaging Features on Magnetic Resonance Imaging Combined With Epstein-Barr Virus Status to Predict Disease Progression in Patients With Nasopharyngeal Carcinoma

Purpose: We aimed to investigate whether Epstein-Barr virus (EBV) could produce differences on magnetic resonance imaging (MRI) by examining histogram and texture imaging features. We also sought to determine the predictive value of pretreatment MRI texture analyses incorporating with EBV status for disease progression (PD) in patients with primary nasopharyngeal carcinoma (NPC). Materials and Methods: Eighty-one patients with primary T2-T4 NPC and known EBV status who underwent contrast-enhanced MRI were included in this retrospective study. Whole-tumor-based histogram and texture features were extracted from pretreatment T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced (CE)-T1WI images. Mann-Whitney U tests were performed to identify differences in histogram and texture parameters between EBV DNA-positive and EBV DNA-negative NPC images. Receiver operating characteristic (ROC) curve analyses were employed to assess the ability of each feature to predict EBV status. The different clinical as well as histogram and texture feature prediction models were evaluated using the area under the ROC curve (AUC) analyses to predict PD. Finally, an integrated model with the best performance was built. Results: Of the 81 patients included, 54 had EBV DNA-positive NPC, and 27 had EBV DNA-negative NPC. EBV DNA-positive patients had higher overall stage (P=0.016), more lymphatic metastases (P<0.0001), and easier distant metastases (P=0.026) than EBV CNA-negative patients. Tumor volume, T1WISkewness and T2WIKurtosis showed significant differences between the two groups. The three features combined achieved an AUC of 0.783 (95% confidence interval [CI] 0.678-0.888) with a sensitivity and specificity of 70.4% and 74.1%, respectively, in differentiating EBV DNA-positive tumors from EBV DNA-negative tumors. The combination of smoking history, overall stage, T2WIKurtosis, and EBV status was the most effective model for predicting PD in patients with primary NPC. The overall accuracy was 84.8%, with a sensitivity and specificity of 87.5% and 79.6%, respectively, (AUC, 0.894; 95% CI 0.805-0.958). Conclusions: In this study, differences in the clinical and imaging features between patients with EBVDNA-positive and EBV DNA-negative NPC were revealed. We also developed a useful model combining clinicoradiological features and EBV status to help predict PD in NPC patients.