2026-10-14T03:54:24Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33961585 2026-09-22T04:21:19Z category_363 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Exploratory radiomics analysis of T1-weighted and FLAIR MRI in relation to Parkinson’s disease motor subtypes.docx Hyeongjin Lim (19372857) Jeongwon Lee (5754422) Sangyoon Park (22768916) Yongha Gi (9591371) Seol-Hee Baek (1783045) Byung-Jo Kim (624552) Myonggeun Yoon (9591380) Neurology and Neuromuscular Diseases machine learning magnetic resonance imaging motor subtypes Parkinson’s disease radiomics Background<p>Parkinson’s disease motor subtypes, tremor-dominant (TD) and postural instability/gait difficulty (PIGD), differ in disease trajectory and prognosis, yet their classification depends on MDS-UPDRS-based clinical ratings that are susceptible to inter-rater variability and medication state.</p>Purpose<p>This exploratory study investigated whether T1-weighted and FLAIR MRI radiomics show differential associations with TD and PIGD motor subtypes in Parkinson’s disease, and whether the two sequences contribute complementary subtype-relevant information when combined in a multimodal framework.</p>Methods<p>Baseline 3 T MRI data from 568 patients with Parkinson’s disease in the PPMI cohort were analyzed (448 TD; 120 PIGD). Radiomics features were extracted from predefined basal ganglia and brainstem regions on T1-weighted and FLAIR images using PyRadiomics. Participants with only T1-weighted or only FLAIR MRI were used as sequence-specific development cohorts; those with both sequences formed a common evaluation cohort. ComBat harmonization was applied within each modality before cohort separation, followed by development-cohort-based feature selection and model fitting. 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> 2026-09-22T04:21:19Z Dataset Dataset 10.3389/fneur.2026.1920363.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Exploratory_radiomics_analysis_of_T1-weighted_and_FLAIR_MRI_in_relation_to_Parkinson_s_disease_motor_subtypes_docx/33961585 CC BY 4.0