Prediction of Vestibular Schwannoma Growth Based on Radiomics Features of MRI Images Using Ensemble Machine Learning Methods
DOI:
https://doi.org/10.18372/1990-5548.87.20884Keywords:
vestibular schwannoma, radiomics features, machine learning, ensemble methods, wavelet decomposition, tumor growth predictionAbstract
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.
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