Development and application of a multi-task fusion model using susceptibility-weighted imaging-based substantia nigra and adjacent structures for prognostic prediction of subthalamic nucleus deep brain stimulation in Parkinson's disease.

We developed and evaluated a deep learning framework using susceptibility-weighted imaging signatures of the substantia nigra and adjacent structures to predict motor outcomes following subthalamic nucleus deep brain stimulation in Parkinson's disease. This retrospective study included patients undergoing bilateral subthalamic nucleus deep brain stimulation, and motor response was defined as the percentage improvement in the original Unified Parkinson's Disease Rating Scale Part III score from the preoperative medication-off condition to the 1-year postoperative stimulation-on/medication-off condition. Patients with at least 50% improvement were classified as optimal responders. A multi-task fusion network integrating prognostic classification and complementary regression learning was developed using preoperative susceptibility-weighted imaging data. In the initial 36-patient cohort, the proposed model outperformed baseline models and showed consistent 5-fold cross-validation performance. Substantia nigra and adjacent structures-centred sub-volumes showed better prognostic performance than non-substantia nigra and adjacent structures regions. In the expanded 60-patient cohort, the model achieved 88.3% accuracy. Additional analyses showed greater percentage improvement in the 39-item Parkinson's Disease Questionnaire in optimal responders and no prognostic association between swallow-tail sign ratings and motor outcome. These findings suggest that preoperative susceptibility-weighted images covering the substantia nigra and adjacent structures may support prognostic prediction of subthalamic nucleus deep brain stimulation and may provide a practical decision-support tool for preoperative patient selection.