Differentiation of benign and malignant parotid gland tumors using T2-weighted MRI-based radiomics: the role of feature selection and dimensionality reduction.

OBJECTIVE

To identify the optimal machine learning strategy, including feature selection and dimensionality reduction methods, for differentiating benign and malignant parotid gland tumors using radiomic features from T2-weighted magnetic resonance images (MRI).

MATERIAL AND

METHODS

This retrospective study included 95 patients with histopathologically confirmed parotid gland tumors (49 benign, 46 malignant). A total of 107 radiomic features (morphological, first-order, and texture features) were extracted using PyRadiomics. Seven feature selection and dimensionality reduction methods were evaluated in combination with five classifiers. Model performance was assessed using five-fold cross-validation, all data preprocessing and feature selection steps were entirely embedded within the cross-validation framework for data leakage. Performance metrics included accuracy, precision, recall, F1-score, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS

The full feature set showed limited performance (AUC: 0.65). PCA, PCR, and mRMR did not significantly improve results, whereas recursive feature elimination (RFE) consistently identified more informative features. The RFE-SVM model achieved the best performance with an AUC of 0.85.

CONCLUSION

RFE combined with SVM improves the differentiation of benign and malignant parotid gland tumors on T2-weighted MRI. These findings highlight the importance of model-based feature selection for developing clinically applicable radiomic models.