Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study - Liu, Yu; Xiao, Bin; Zhang, Chencheng; Li, Junchen; Lai, Yijie; Shi, Feng; Shen, Dinggang; Wang, Linbin; Sun, Bomin; Li, Yan; Jin, Zhijia; Wei, Hongjiang; et al. (Frontiers in Neuroscience, 2021)
Background: 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. Objective: To investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD. Methods: Thirty-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. Results: For 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). Conclusion: Our 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.