Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.

Abstract

Purpose: Computed Tomography Angiography (CTA) is the first line of imaging in the diagnosis of Large Vessel Occlusion (LVO) strokes. We trained and independently validated end-to-end automated deep learning pipelines to predict 3-month outcomes after anterior circulation LVO thrombectomy based on admission CTAs.

Methods: We split a dataset of 591 patients into training/cross-validation (n = 496) and independent test set (n = 95). We trained separate models for outcome prediction based on admission "CTA" images alone, "CTA + Treatment" (including time to thrombectomy and reperfusion success information), and "CTA + Treatment  + Clinical" (including admission age, sex, and NIH stroke scale). A binary (favorable) outcome was defined based on a 3-month modified Rankin Scale ≤ 2. The model was trained on our dataset based on the pre-trained ResNet-50 3D Convolutional Neural Network ("MedicalNet") and included CTA preprocessing steps.

Results: We generated an ensemble model from the 5-fold cross-validation, and tested it in the independent test cohort, with receiver operating characteristic area under the curve (AUC, 95% confidence interval) of 70 (0.59-0.81) for "CTA," 0.79 (0.70-0.89) for "CTA + Treatment," and 0.86 (0.79-0.94) for "CTA + Treatment + Clinical" input models. A "Treatment + Clinical" logistic regression model achieved an AUC of 0.86 (0.79-0.93).

Conclusion: Our results show the feasibility of an end-to-end automated model to predict outcomes from admission and post-thrombectomy reperfusion success. Such a model can facilitate prognostication in telehealth transfer and when a thorough neurological exam is not feasible due to language barrier or pre-existing morbidities.