Analysis and Research on Predicting the Motor Classification of Parkinson's Disease Based on Radiomics

Research overview

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Abstract This study developed and compared single-sequence and multimodal imaging omics models for Parkinson's disease (PD) classification using 3.0T MRI scans (T1WI, T2-FLAIR) from 160 PD patients (82 tremor-type, 78 non-tremor-type) and 100 healthy controls. Regions of interest included the Hippocampus, Substantia Nigra, Red Nucleus, Thalamus, and Amygdala. Data were split into training/test sets (8:2), with the Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection and Support Vector Machine (SVM) for modeling, evaluated via Receiver Operating Characteristic (ROC) curves and area under curve (AUC). The single-sequence Hippocampal-T1WI model showed AUCs of control (training/test:0.940/0.834), non-tremor PD (training/test: 0.923/0.740), and tremor PD (training/test:0.914/0.524). The multimodal model achieved higher AUCs: control (training/test:0.966/0.877), non-tremor PD (training/test:0.952/0.861), and tremor PD (training: 0.942, test: 0.760), indicating improved predictive accuracy, demonstrating superior predictive accuracy. Multimodal imaging omics significantly enhanced PD diagnosis and differentiation compared to single-sequence models.

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