Multimodal Deep Learning for Functional Outcome Prediction in Endovascular Therapy

The efficacy of endovascular therapy (EVT) in large vessel occlusion (LVO) of the anterior circulation depends on adequate patient selection. Patients can be selected based on their predicted functional outcome after EVT. Using a dataset composed of 1929 patients, we compare the functional outcome prediction performance of clinical baseline models, including the clinically validated MR PREDICTS decision tool, with an imaging based pipeline and a multimodal approach. The predicted outcome measure is dichotomized modified Rankin Scale score 90 days after mechanical thrombectomy. Binary classifier performance is quantified using Area-Under the receiver operating characteristic Curve (AUC). Combining clinical features with information extracted from CTA images does not significantly improve the performance of functional outcome prediction methods compared to the baseline model. This multimodal approach can however replace radiologically derived biomarkers, as its performance is non-inferior.