P09.01 Radiomics-based machine learning approach in differentiation between vestibular schwannoma and meningioma in the cerebellopontine angle

Abstract BACKGROUND Vestibular schwannoma (VS) and meningioma are the most two common tumors in the cerebellopontine angle (CPA). Accurate preoperative differentiation of the two lesions is important due to their different surgical approaches and outcomes for the preservation of hearing and facial nerve function. Magnetic resonance (MR) scan is commonly performed to preoperatively evaluate CPA tumors and to differentiate VS from meningioma in clinical routine. However, in some cases, overlaps of conventional MR imaging patterns between the two lesions could make preoperative diagnosis challenging. The purpose of this study is to investigate the ability of radiomics, a novel method providing objective and quantitative information beyond visual assessment, in differentiation between VS and meningioma located at CPA using machine learning technology. MATERIAL AND METHODS This retrospective study enrolled eligible patients who were diagnosed with VS (N = 50) or meningioma (N = 41) in the CPA. A set of mineable three-dimensional radiomic parameters were extracted from preoperative contrast-enhanced T1-weighted images. Optimal features were selected first with three selection methods including distance correlation, least absolute shrinkage and selection operator (LASSO) and gradient boosting decision tree (GBDT). Then three machine learning classification algorithms, namely linear discriminant analysis (LDA), support vector machine (SVM) and random forest were employed to build discriminative models. Area under the curve (AUC), accuracy, sensitivity and specificity were calculated to assess the performance of each model. RESULTS Nine models were established with different combinations of selection methods and machine learning classifiers. Three classifiers with the suitable selection method all represented feasible ability in differentiation with AUC more than 0.86 in the validation set, and LDA-based models seemed to show better diagnostic performance than those based on the other two classifiers. The combination of LASSO and LDA classifier was found to show the highest AUC of 0.942 in the validation set. CONCLUSION Radiomics-based models via machine learning approaches could potentially be utilized to assist in preoperative differentiation between VS and meningioma in the CPA.