Prediction of Blood Supply in Vestibular Schwannomas Using Radiomics Machine-Learning Classifiers
Abstract ObjectiveThis study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively.MethodsBy retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply group according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomic classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination result with the performance of differentiation through visual observation in clinical diagnosis.Results191 patients were included in this study. 3918 stable features were extracted each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC=0.90 and F1-score= 0.89.ConclusionRadiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.