AUTHOR=Liu Yu , Xiao Bin , Zhang Chencheng , Li Junchen , Lai Yijie , Shi Feng , Shen Dinggang , Wang Linbin , Sun Bomin , Li Yan , Jin Zhijia , Wei Hongjiang , Haacke Ewart Mark , Zhou Haiyan , Wang Qian , Li Dianyou , He Naying , Yan Fuhua 

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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.731109

DOI=10.3389/fnins.2021.731109

ISSN=1662-453X

ABSTRACT=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.