Differentiating idiopathic Parkinson’s disease from multiple system atrophy-P using brain MRI-based radiomics: a multicenter study - Huang, Yin-Hui; Yang, Mei-Li; Li, Yuan-Zhe; Chen, Ya-Fang; Cai, Chi; Huang, Jing; Wang, Yi; Li, Tie-Qiang; Ye, Qin-Yong (Therapeutic advances in neurological disorders, 2025)
Differentiating idiopathic Parkinson's disease (IPD) from multiple system atrophy-parkinsonian type (MSA-P) is essential for optimizing patient care and prognosis, given the differences in disease progression and treatment response. This study aimed to develop and evaluate a radiomics-based model using magnetic resonance imaging (MRI)-derived features to distinguish IPD from MSA-P. A multicenter retrospective study. A multicenter retrospective study was conducted with 287 patients (186 IPD and 101 MSA-P) who underwent brain MRI. Radiomic features were extracted from T1-weighted imaging and T2-weighted imaging sequences, and various machine learning classifiers were applied, including logistic regression, support vector machine (SVM), ExtraTrees, extreme gradient boosting, and Light Gradient Boosting Machine. Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, and specificity. A nomogram combining clinical and radiomic features was also evaluated. The SVM model, selected as the base for the Rad-signature, achieved the best diagnostic performance, with AUCs of 0.885 and 0.900 in the training and testing cohorts, respectively. The Rad-signature significantly outperformed clinical-only models in distinguishing IPD from MSA-P. The nomogram incorporating radiomic and clinical features yielded the highest diagnostic accuracy (AUC = 0.973 and 0.963 for training and testing cohorts, respectively) and balanced sensitivity and specificity. Decision curve analysis confirmed the nomogram's clinical utility. Radiomics-based MRI analysis offers a powerful tool for distinguishing IPD from MSA-P, enhancing diagnostic accuracy, and aiding personalized treatment planning. Integrating radiomic and clinical data may improve diagnostic workflows in clinical practice.