Prediction of blood supply in vestibular schwannomas using radiomics machine-learning classifiers

Abstract

Objective This 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. Methods By 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. Results 191 patients were included in this study, with 119 in the rich blood supply group and 72 in the poor blood supply group. A total of 5226 features were extracted each patient, of which 3918 features were stable. 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, and the model outperformed the neurosurgeons' visual observation with a mean F1-score = 0.64. Conclusion Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.