Supplementary file 1_Exploratory radiomics analysis of T1-weighted and FLAIR MRI in relation to Parkinson’s disease motor subtypes.docx

L1-regularized logistic regression models were trained independently for each sequence and evaluated in the common cohort. A stacking model combined T1- and FLAIR-derived probability scores. Model performance was assessed using the area under the precision–recall curve (PR-AUC), balanced accuracy, and F1-score, and Shapley Additive Explanations (SHAP) analysis was used for interpretation.</p>Results<p>In the common evaluation cohort (n = 148; 118 TD, 30 PIGD), the T1-weighted model achieved a PR-AUC of 0.814, balanced accuracy of 0.829, and F1-score of 0.750.

The FLAIR model achieved a PR-AUC of 0.858, balanced accuracy of 0.832, and F1-score of 0.650. The stacked model showed higher performance, with a PR-AUC of 0.928, balanced accuracy of 0.912, and F1-score of 0.818. The T1-weighted model showed relatively better TD classification, whereas the FLAIR model showed higher PIGD sensitivity.

SHAP analysis showed that T1-weighted contributions were mainly concentrated in the putamen, while FLAIR contributions were more broadly distributed across basal ganglia and brainstem regions.</p>Conclusion<p>T1-weighted and FLAIR MRI radiomics showed distinct subtype-associated error profiles and preliminary evidence of cross-sequence complementarity in this PPMI-based exploratory analysis. These findings require external validation before clinical interpretation, but provide a hypothesis-generating framework for multimodal structural radiomics research in Parkinson’s disease motor subtype characterization.</p>