Prediction of Vestibular Schwannoma Growth Based on Radiomics Features of MRI Images Using Ensemble Machine Learning Methods - Sineglazov , Victor; Shevchenko , Maksym (Electronics and Control Systems, 2026)
This paper proposes a method for predicting vestibular schwannoma growth based on the analysis of a single MRI scan using radiomics features and ensemble machine learning methods. A total of 96 patients from the public Vestibular-Schwannoma-MC-RC2 dataset were studied. 744 texture features were extracted using wavelet decomposition. A Voting ensemble combining five classifiers was proposed: SVM, logistic regression, k-NN, Random Forest, and LDA. ROC AUC of 0.742 ± 0.072 was achieved using 5-fold cross-validation. The results confirm the effectiveness of the proposed approach for early prediction of tumor growth.