Table 1_Automatic differentiation of Parkinson’s disease motor subtypes based on deep learning and radiomics.docx

ObjectiveParkinson’s disease (PD) is a common neurodegenerative disorder, and the early and accurate differentiation of its motor subtypes is of significant importance for clinical diagnosis and treatment planning. Research has shown that deep brain nuclei such as the thalamus, caudate nucleus, putamen, and globus pallidus play a critical role in the pathogenesis of different motor subtypes of Parkinson’s disease. This study aims to utilize deep learning and radiomics technology to establish an automated method for differentiating motor subtypes of Parkinson’s disease.
MethodsThe data for this study were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database, including a total of 135 Parkinson’s disease patients, comprising 43 cases of the Postural Instability/Gait Difficulty (PIGD) subtype and 92 cases of the Tremor Dominant (TD) subtype. High-resolution MRI scans were used to extract 2,264 radiomics features from 8 deep brain nuclei, including bilateral thalamus, caudate nucleus, putamen, and globus pallidus. After dimensionality reduction, five independent machine learning classifiers [AdaBoost, Bagging Decision Tree (BDT), Gaussian Process (GP), Logistic Regression (LR), and Random Forest (RF)] were trained on the training set and validated on the test set. Model performance was evaluated using the Area Under the Curve (AUC) metric.
ResultsAfter feature selection, 17 most discriminative radiomics features were retained. Among the models, the BDT-based diagnostic model demonstrated the best performance, achieving AUC values of 1.000 and 0.962 on the training and test sets, respectively. DeLong’s test results indicated that the BDT model significantly outperformed other models. Calibration curve analysis showed that the Parkinson’s disease subtype classification model based on MRI radiomics features exhibited good calibration and stability. Clinical decision curve analysis revealed that the BDT model demonstrated significant clinical net benefits across a wide probability range, indicating high clinical utility.
ConclusionThe BDT model based on MRI radiomics features constructed in this study exhibited excellent performance in differentiating motor subtypes of Parkinson’s disease and can serve as an effective tool for clinical auxiliary diagnosis. This fully automated model is capable of processing MRI data and providing results within 3 min, offering an efficient and reliable solution for the early differentiation of Parkinson’s disease motor subtypes, with significant clinical application value.