Predicting impact of Deep Brain Stimulation on Non-motor symptoms of Parkinson's disease

Abstract

This is the largest study on Radiomics analysis looking into the impact of Deep Brain Stimulation on Non-Motor Symptoms (NMS) of Parkinson's disease. Preoperative brain white matter radiomics of 120 patients integrated with clinical variables were used to predict the DBS effect on NMS after 1 year from the surgery. Patients were classified "suboptimal" vs "good" based on a 10% or more improvement in NMS score. The combined Radiomics-Clinical Random Forrest (RF) model achieved an AUC of 0.96, Accuracy of 0.91, Sensitivity of 0.94 and Specificity of 0.88. The Youden's index showed optimal threshold for the RF of 0.535. The confusion matrix of the RF classifier gave a TPR of 0.92 and a FPR of 0.03. This corresponds to a PPV of 0.93 and a NPV of 0.93. The predictive models can be easily interpreted and after careful large-scale validation be integrated in assisting clinicians and patients to make informed decisions.Clinical Relevance- This paper shows the lesser studied positive impact of Deep Brain Stimulation on Non motor symptoms of Parkinson's disease while allows clinicians to predict non responders to the therapy.