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

doi:10.3389/fnins.2021.731109

	Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study
	Liu, Yu 1; Xiao, Bin 2; Zhang, Chencheng 3; Li, Junchen 4; Lai, Yijie 3; Shi, Feng 5; Shen, Dinggang5,6,7 ; Wang, Linbin 3; Sun, Bomin 3; Li, Yan 1; Jin, Zhijia 1; Wei, Hongjiang 8; Haacke, Ewart Mark 9; Zhou, Haiyan 10; Wang, Qian 2; Li, Dianyou 3; He, Naying 1; Yan, Fuhua 1
	2021-09-07
发表期刊	FRONTIERS IN NEUROSCIENCE (IF:3.2[JCR-2023],4.3[5-Year])
EISSN	1662-453X
卷号	15
DOI	10.3389/fnins.2021.731109
摘要	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.
关键词	quantitative susceptibility mapping radiomics deep brain stimulation Parkinson's disease motor outcome prediction
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收录类别	SCIE
语种	英语
WOS研究方向	Neurosciences & Neurology
WOS类目	Neurosciences
WOS记录号	WOS:000697292800001
出版者	FRONTIERS MEDIA SA
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/131838
专题	生物医学工程学院_PI研究组_沈定刚组
通讯作者	Wang, Qian; Li, Dianyou; He, Naying
作者单位	1.Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Radiol, Sch Med, Shanghai, Peoples R China; 2.Shanghai Jiao Tong Univ, Sch Biomed Engn, Inst Med Imaging Technol, Shanghai, Peoples R China; 3.Shanghai Jiao Tong Univ, Ruijin Hosp, Ctr Funct Neurosurg, Dept Neurosurg,Sch Med, Shanghai, Peoples R China; 4.Nanjing Univ Chinese Med, Dept Radiol, Changshu Hosp, Changshu, Jiangsu, Peoples R China; 5.Shanghai United Imaging Intelligence Co Ltd, Shanghai, Peoples R China; 6.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China; 7.Korea Univ, Dept Artificial Intelligence, Seoul, South Korea; 8.Shanghai Jiao Tong Univ, Sch Biomed Engn, Med X Res Inst, Shanghai, Peoples R China; 9.Wayne State Univ, Dept Radiol, Detroit, MI USA; 10.Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Neurol, Sch Med, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yu,Xiao, Bin,Zhang, Chencheng,et al. Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study[J]. FRONTIERS IN NEUROSCIENCE,2021,15.
APA	Liu, Yu.,Xiao, Bin.,Zhang, Chencheng.,Li, Junchen.,Lai, Yijie.,...&Yan, Fuhua.(2021).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,15.
MLA	Liu, Yu,et al."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 15(2021).