Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas - Ko, Ching-Chung; Zhang, Yang; Chen, Jeon-Hor; Chang, Kai-Ting; Chen, Tai-Yuan; Lim, Sher-Wei; Wu, Te-Chang; Su, Min-Ying (Frontiers in Neurology, 2021)
Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. The purpose of this study applied preoperative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas. Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone preoperative MRIs and postoperative follow-up MRIs for more than one year were studied. Preoperative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first order features and 75 textural features were extracted. SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient. Results: Gross-total resection (Simpson Grades I-III) was performed in 93 (93/128, 72.7%) patients, and nineteen (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13 respectively. Using SVM score, AUC of 0.80 with optimal cut-off value of 0.224 were obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003). Conclusions: Our preliminary results showed that preoperative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.