Evaluation of MRI-Based Radiomic Texture Analysis and Ensemble Machine Learning Models in the Diagnosis of Hemifacial Spasm

Aim: To investigate whether MRI-derived texture (radiomic) features combined with machine learning can provide objective diagnostic discrimination between patients with hemifacial spasm (HFS) and healthy controls, and to evaluate the added value of high-resolution 3D T2-SPACE imaging.Materials and Methods: This retrospective study included 35 patients with clinically diagnosed HFS and 36 age- and sex-matched controls. All subjects underwent 3-Tesla MRI with axial T2-weighted imaging (T2WI), and a subgroup of 21 HFS patients and their matched controls also underwent 3D T2-SPACE imaging. Segmentation was performed across axial slices encompassing the brainstem, the entire posterior fossa, and the inner ear, middle ear, and mastoid portions, with the anterior boundary extending to the clivus, using a single manually drawn region of interest (ROI) to capture comprehensive radiomic features. 137 radiomic texture features were extracted using the PyRadiomics platform. Multiple machine learning classifiers, including k-nearest neighbors, Naive Bayes, discriminant analysis, decision trees, and ensemble learning methods, were evaluated. Data were split into 80% training and 20% testing sets, and performance was assessed using accuracy, sensitivity, specificity, and ROC-AUC over 20 randomized repetitions.Results: Among all tested models, the Ensemble Bagged Trees classifier demonstrated the highest overall performance among the tested models. Using T2WI, the model achieved a mean accuracy of 0.71 ± 0.10, sensitivity of 0.72 ± 0.15, specificity of 0.70 ± 0.18, and ROC-AUC of 0.76 ± 0.16. In the subgroup analyzed with high-resolution 3D T2-SPACE imaging, performance further improved, yielding a mean accuracy of 0.80 ± 0.12, sensitivity of 0.67 ± 0.23, specificity of 0.95 ± 0.12, and ROC-AUC of 0.85 ± 0.11. High-resolution T2-SPACE images provided an improvement in specificity compared with conventional T2WI.Conclusions: Radiomic texture features extracted from conventional and high-resolution MRI, when combined with ensemble machine learning methods, show potential for facilitating discrimination between patients with hemifacial spasm and healthy controls. The particularly high specificity achieved with 3D T2-SPACE imaging suggests that this approach may serve as a promising complementary diagnostic tool, supporting conventional imaging and clinical assessment and offering a step toward objective, image-based biomarkers for HFS.