Objective: To develop an objective predictive measure of deep brain stimulation (DBS) treatment outcomes in dystonia.

Background: Dystonia causes involuntary muscle contractions, affecting movements and postures. Deep brain stimulation (DBS) of the globus pallidus (GPi) offers 30%-60% clinical improvement in various dystonia forms. Yet, only about 5% of dystonia patients undergo DBS surgery, and around 25% of them have poor response. Patient selection is challenging due to the lack of predictive biomarkers to inform treatment outcomes prior to neurosurgical intervention.

Method: We developed a deep learning algorithm, DystoniaDBSNet, which uses a structural brain MRI of patients who underwent DBS-GPi to automatically identify the neural biomarker of predictive treatment efficacy. Whole-brain T1-weighted MRIs from 130 patients with focal, multifocal, segmental, or generalized dystonia treated at Massachusetts General Hospital, University College London, and the University of Florida were included in this study. Clinical improvement was defined as at least a 30% reduction of symptom severity based on the standardized Burke-Fahn-Marsden Dystonia Rating Scale. The DystoniaDBSNet model was trained and validated using phenotype-, sex-, age-, and surgical site-matched patient cohorts, allocating 80% of patients for training and 20% for testing.

Results: DystoniaDBSNet automatically identified a neural biomarker of DBS treatment outcome, which included clusters in the bilateral precentral and middle frontal gyri, left superior frontal gyrus, anterior cingulate cortex, thalamus, and right postcentral gyrus. DystoniaDBSNet achieved an overall accuracy of 96.0%, with 100% sensitivity, 85.7% specificity, and a 3.87% referral rate in predicting the DBS treatment outcome for different clinical phenotypes of dystonia.

Conclusion: DystoniaDBSNet yielded a fully automated, objective, and highly accurate predictive outcome of DBS treatment in patients with different forms of dystonia from a single structural MRI that was collected prior to neurosurgical intervention. The components of the DystoniaDBSNet biomarker included brain regions known for their contribution to dystonia pathophysiology. The translational significance of DystoniaDBSNet is in its potential to enhance clinical decision-making in DBS candidate selection and ultimately to deliver improved clinical care to patients with dystonia.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/dystoniadbsnet-a-novel-deep-learning-biomarker-of-predictive-treatment-outcomes-in-dystonia/