Radiomics for Deep Brain Stimulation outcome prediction using Quantitative Susceptibility Mapping (RadDBS-QSM)

Motivation: To improve outcome prediction for deep brain stimulation (DBS) surgery using radiomic features on quantitative susceptibility maps (QSMs). Goal(s): To address the inconsistent levodopa challenge test (LCT) prediction for DBS outcomes by describing the target variable, motor symptom improvement, as a weighted sum of QSM radiomic features. Approach: A least absolute shrinkage and selection operator (LASSO) model is implemented, trained, and tested on patient data and known DBS outcomes. Results: Model predictions outperform the conventional LCT prediction and estimate DBS improvement from preoperative motor symptom scores and radiomic features on QSM. Impact: The levodopa challenge test estimates patient response to deep brain stimulation surgery, presenting undesirable side effects and inconsistent outcomes. Radiomic prediction of deep brain surgery outcomes using quantitative susceptibility maps aims to provide a numerical measure of symptom improvement.