Differentiating Parkinson's disease motor subtypes: A radiomics analysis based on deep gray nuclear lesion and white matter.

ABSTRACT

OBJECTIVE: To investigate the feasibility of radiomics analysis of brain MR images to differentiate Parkinson's disease motor subtypes. METHODS: 42 postural instability gait difficulty (PIGD) patients, 92 tremor-dominant (TD) patients and 96 healthy controls were included from the Parkinson's Progressive Marker Initiative public database. For each subject, 4850 radiomic features from 148 cortical and 14 subcortical brain regions were extracted. The variance threshold and the least absolute shrinkage and selection operator were used to select the optimal features. Classification models based on Support Vector Machine, Logistic Regrcession, and Multi-Layer Perceptron were constructed to assess the performance of optimal features in the discrimination of the two subtypes. Correlations between radiomic features and clinical scores of the two subtypes were estimated. RESULTS: The Support Vector Machine demonstrated the best performance in discriminating between the two subtypes, and the mean area under the curve was 0.833 (specificity = 83.3%, sensitivity = 75.0%, and accuracy = 80.7%). For the postural instability gait difficulty patients, these optimal features in the hippocampal showed closed correlations with the Montreal Cognitive Assessment scores (P < 0.05). CONCLUSION: The results of our study provide preliminary evidence that radiomics analysis of brain MR images could allow discrimination between patients with TD, PIGD and control subjects and has great potential value in the clinical practice.