Cross-regional radiomics: a novel framework for relationship-based feature extraction with validation in Parkinson's...

Traditional radiomics approaches focus on single-region feature extraction, limiting their ability to capture complex inter-regional relationships crucial for understanding pathophysiological mechanisms in complex diseases. This study introduces a novel cross-regional radiomics framework that systematically extracts relationship-based features between anatomically and functionally connected brain regions. We analyzed T1-weighted magnetic resonance imaging (MRI) data from 140 early-stage Parkinson's disease patients (70 tremor-dominant, 70 postural instability gait difficulty) from the Parkinson's Progression Markers Initiative (PPMI) database across multiple imaging centers. Eight bilateral motor circuit regions (putamen, caudate nucleus, globus pallidus, substantia nigra) were segmented using standardized atlases. Two feature sets were developed: 48 traditional single-region of interest (ROI) features and 60 novel motor-circuit features capturing cross-regional ratios, asymmetry indices, volumetric relationships, and shape distributions. Six feature engineering scenarios were evaluated using center-based 5-fold cross-validation with six machine learning classifiers to ensure robust generalization across different imaging centers. Motor-circuit features demonstrated superior performance compared to single-ROI features across enhanced preprocessing scenarios. Peak performance was achieved with area under the curve (AUC) of 0.821 ± 0.117 versus 0.650 ± 0.220 for single-ROI features (p = 0.0012, Cohen's d = 0.665). Cross-regional ratios, particularly putamen-substantia nigra relationships, dominated the most discriminative features. Motor-circuit features showed superior generalization across multi-center data and better clinical utility through decision curve analysis and calibration curves. The proposed cross-regional radiomics framework significantly outperforms traditional single-region approaches for Parkinson's disease motor subtype classification. This methodology provides a foundation for advancing radiomics applications in complex diseases where inter-regional connectivity patterns are fundamental to pathophysiology.