Quantitative Susceptibility Mapping Radiomics with Label Noise Compensation for Predicting Deep Brain Stimulation Outcomes in Parkinson's Disease

Parkinsons disease patients with motor complications are often considered for deep brain stimulation (DBS) surgery. DBS candidate selection involves an assessment known as the levodopa challenge test (LCT). The LCT aims to predict DBS outcomes by measuring symptom improvement accompanying changes in levodopa dosage. While used in the patient selection process, inconsistent LCT predictions have been widely documented, verified here with Pearsons correlation r = 0.12. Estimating symptom improvement to separate DBS responders and non-responders remains an unmet need. Quantitative susceptibility mapping (QSM) is routinely acquired for pre-surgical planning and depicts the iron distribution in substantia nigra and subthalamic nuclei. Iron deposition in these nuclei has correlated with disease progression and motor symptom severity. A novel QSM radiomic approach is presented using a regression model and features extracted from the pre-surgical targeting acquisition. Noise compensation in training labels improves outcome prediction in regression and classification models. The model predicts improvement in the unified Parkinsons disease rating scale (UPDRS-III) (r = 0.75). Predictive feature maps in deep gray nuclei offer contrast between responders and non-responders. The QSM radiomic approach has potential to improve DBS candidate selection by accurately estimating symptom improvement, eliminating difficult medication manipulation, and avoiding time-consuming evaluations to reduce patient and clinician burden.