Abstract #4718

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

Alexandra Grace Roberts¹, Jinwei Zhang², Heejong Kim³, Dominick Romano⁴, Sema Akkus⁵, Mert Sabuncu^1,3, Jianqi Li⁶, Brian Harris Kopell⁵, Pascal Spincemaille³, and Yi Wang^3,4

¹Electrical and Computer Engineering, Cornell University, New York, NY, United States, ²Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States, ³Radiology, Weill Cornell Medicine, New York, NY, United States, ⁴Biomedical Engineering, Cornell University, New York, NY, United States, ⁵Neurosurgery, Mount Sinai Hospital, New York, NY, United States, ⁶Changhai Hospital, Shanghai, China

Synopsis

Keywords: Diagnosis/Prediction, Radiomics

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.

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