Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.
Authors
Affiliations (6)
Affiliations (6)
- Department of Neurology, Second Xiangya Hospital of Central South University, No. 139Middle Renmin Road, Changsha, Hunan, 410011, China.
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, Hunan, China.
- Department of Radiology, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
- School of Humanities, Central South University, Changsha, 410083, China. [email protected].
- Department of Neurology, Second Xiangya Hospital of Central South University, No. 139Middle Renmin Road, Changsha, Hunan, 410011, China. [email protected].
Abstract
The unrelenting progression of Parkinson's disease (PD) leads to severely impaired quality of life, with considerable variability in progression rates among patients. Identifying biomarkers of PD progression could improve clinical monitoring and management. Radiomics, which facilitates data extraction from imaging for use in machine learning models, offers a promising approach to this challenge. This study investigated the use of multi-modality imaging, combining conventional magnetic resonance imaging (MRI) and dopamine transporter single photon emission computed tomography (DAT-SPECT), to predict motor progression in PD. Motor progression was measured by changes in the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor subscale scores. Radiomic features were selected from the midbrain region in MRI and caudate nucleus, putamen, and ventral striatum in DAT-SPECT. Patients were stratified into fast progression vs. slow progression based on change in MDS-UPDRS in follow-up. Various feature selection methods and machine learning classifiers were evaluated for each modality, and the best-performing models were combined into an ensemble. On the internal test set, the ensemble model, which integrated clinical information, T1WI, T2WI and DAT-SPECT achieved a ROC AUC of 0.93 (95% CI: 0.80-1.00), PR AUC of 0.88 (95%CI 0.61-1.00), accuracy of 0.85 (95% CI: 0.70-0.89), sensitivity of 0.72 (95% CI: 0.43-1.00), and specificity of 0.92 (95% CI: 0.77-1.00). On the external test set, the ensemble model outperformed single-modality models with a ROC AUC of 0.77 (95% CI: 0.53-0.93), PR AUC of 0.79 (95% CI: 0.56-0.95), accuracy of 0.68 (95% CI: 0.50-0.86), sensitivity of 0.53 (95% CI: 0.27-0.82), and specificity of 0.82 (95% CI: 0.55-1.00). In conclusion, this study developed an imaging-based model to identify baseline characteristics predictive of disease progression in PD patients. The findings highlight the strength of using multiple imaging modalities and integrating imaging data with clinical information to enhance the prediction of motor progression in PD.