The differential diagnosis value of radiomics-based machine learning in Parkinson’s disease: a systematic review and meta-analysis

BACKGROUND: In recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson’s disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD. METHODS: We systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson’s disease and various atypical parkinsonism syndromes (APS). RESULTS: Twenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833–0.891), 0.91 (95% CI: 0.86–0.94), and 0.93 (95% CI: 0.87–0.96) in the training set, and 0.871 (95% CI: 0.853–0.890), 0.86 (95% CI: 0.81–0.89), and 0.87 (95% CI: 0.83–0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843–0.889), 0.86 (95% CI: 0.84–0.88), and 0.80 (95% CI: 0.75–0.84) in the training set, and 0.879 (95% CI: 0.854–0.903), 0.87 (95% CI: 0.85–0.89), and 0.82 (95% CI: 0.77–0.86) in the validation set, respectively. CONCLUSION: Radiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson’s disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson’s disease and related fields. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197.