Abstract
BackgroundEmerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson’s Disease (PD). We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD.ObjectiveTo investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD.MethodsThirty-three PD patients underwent bilateral STN-DBS were recruited. The bilateral SN were segmented based on preoperative quantitative susceptibility mapping to extract susceptibility features using RA-ML. MDS-UPDRS III scores were recorded 1–3 days before and 6 months after STN-DBS surgery. Finally, we constructed three predictive models using logistic regression analyses: (1) the RA-ML model based on radiomics features, (2) the RA-ML+LCT (levodopa challenge test) response model which combined radiomics features with preoperative LCT response, (3) the LCT response model alone.ResultsFor the predictive performances of global motor outcome, the RA-ML model had 82% accuracy (AUC = 0.85), while the RA-ML+LCT response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55). For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RA-ML+LCT response model (76% accuracy, AUC = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42).ConclusionOur findings demonstrated that SN susceptibility features from radiomics could predict global motor and rigidity outcomes of STN-DBS in PD. This RA-ML predictive model might provide a novel approach to counsel candidates for STN-DBS.
Highlights
Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) is a surgical therapy with class I evidence for improving motor symptoms of Parkinson’s disease (PD) (Odekerken et al, 2013)
For the predictive performances of global motor outcome, the radiomics with machine learning (RA-machine learning (ML)) model had an accuracy of 82% (AUC = 0.85), while the RA-ML+levodopa challenge test (LCT) response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55)
For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RAML+LCT response model (76% accuracy, area under the curve (AUC) = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42)
Summary
Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) is a surgical therapy with class I evidence for improving motor symptoms of Parkinson’s disease (PD) (Odekerken et al, 2013). Several factors can contribute to the variable outcome after DBS surgery, including candidate selection, target localization, postoperative programming, and medication adjustment (Okun et al, 2005). Most of these key factors can be well-controlled at experienced sites. Emerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson’s Disease (PD) We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD
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