Meeting Banner
Abstract #3018

Substantia Nigra Susceptibility Features Derived by Radiomics Predict Motor Outcome for STN-DBS in Parkinson’s Disease

Naying He1, Yu Liu1, Bin Xiao2, Junchen Li3, Chencheng Zhang4, Yijie Lai4, Feng Shi5, Dinggang Shen5, Yan Li1, Hongjiang Wei6, Ewart Mark Haacke1,7, Weibo Chen8, Qian Wang2, Dianyou Li4, and Fuhua Yan1
1Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, Shanghai, China, 3Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, No. 6 Huanghe Road, Changshu, China, Changshu, China, 4Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, Shanghai, China, 5Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, Shanghai, China, 6Institute for Medical Imaging Technology, Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, Shanghai, China, 7Department of Radiology, Wayne State University, Detroit, Michigan, USA, Detroit, MI, United States, 8Philips Healthcare,Shanghai,China, Shanghai, China

Currently, there are neither individual objective nor quantitative indicators for predicting DBS motor outcome. We hypothesized that the distribution of SN iron changes in PD patients may reflect a specific disease trait and could potentially account for some variability in the motor outcomes after sub-thalamic nucleus (STN) deep brain stimulation (DBS). We developed a radiomics model with machine learning (RA-ML) based on preoperative individual QSM of the SN to predict motor outcome for STN-DBS in PD and it performed best with an AUC of 0.897. In addition, the threshold probability of the RA-ML model can differentiate surgical responders and non-responders.

This abstract and the presentation materials are available to members only; a login is required.

Join Here