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Explainable MRI radiomics of the basal ganglia and ventral midbrain distinguishes Parkinson's disease, SWEDD, and healthy controls
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DOI：10.1016/j.neuroimage.2026.121892.png)
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Abstract
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Distinguishing scans without evidence of dopaminergic deficit (SWEDD) from Parkinson's disease (PD) remains challenging on routine MRI. We extracted 1284 radiomic features from T1-and T2-weighted MRI within six a priori subcortical regions of interest in the PPMI cohort. A nested cross-validation framework (inner: univariate ANOVA or Kruskal-Wallis with BH-FDR correction, mRMR, and LASSO; outer: 10-fold) was used for feature selection and classifier training. Five supervised models were evaluated, and performance was summarized by outer-fold micro-and macro-averaged AUCs with bootstrap 95% confidence intervals. Fourteen non-redundant features, primarily from the ventral midbrain, thalamus, putamen, and nucleus accumbens, were retained. The XGBoost classifier achieved a macro-AUC of 0.85 (0.76-0.91), with class-wise AUCs of 0.93 for PD, 0.79 for SWEDD, and 0.79 for healthy controls. SHAP analysis identified ventral midbrain texture heterogeneity and thalamic contrast as dominant contributors to PD prediction, while nucleus accumbens texture and putaminal shape were most informative for SWEDD. Radiomic heterogeneity on standard MRI thus captures disease-relevant patterns along a PD-SWEDD-HC continuum. Although these features are indirect surrogates of microstructure, their spatial profiles align with iron-and connectivity-related alterations reported with quantitative susceptibility and diffusion MRI. This explainable radiomics framework enables biologically coherent, multi-class discrimination between PD and SWEDD, supporting low-burden stratification and hypothesis generation for quantitative MRI studies, with planned external validation in independent cohorts to confirm generalizability.
Keywords:
Parkinson's disease
SWEDD
MRI radiomics
Machine learning
Explainable AI
XGBoost
SHAP
Dopaminergic degeneration
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