Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study

Frontiers in Neuroscience

Haiyan Zhou¹  Dinggang Shen²  Chencheng Zhang³  Linbin Wang³  Bomin Sun³  Yijie Lai³  Dianyou Li³  Junchen Li⁴  Naying He⁵  Yu Liu⁵  Zhijia Jin⁵  Yan Li⁵  Fuhua Yan⁵  Ewart Mark Haacke⁶  Bin Xiao⁷  Qian Wang⁷  Hongjiang Wei⁸  Feng Shi^1,10 

[1] 0Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China;Department of Artificial Intelligence, Korea University, Seoul, South Korea;Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China;Department of Radiology, Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, Changshu, China;Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China;Department of Radiology, Wayne State University, Detroit, MI, United States;School of Biomedical Engineering, Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China;School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China;School of Biomedical Engineering, ShanghaiTech University, Shanghai, China;Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China;

关键词: quantitative susceptibility mapping;    radiomics;    deep brain stimulation;    Parkinson’s disease;    motor outcome;    prediction;   

DOI  :  10.3389/fnins.2021.731109

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown