Abstract TP144: Deep Learning on Pre-Procedural CT and Clinical Data Predicts Outcome Following Stroke Thrombectomy

Background: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT). Methods: Deep learning models were trained and tested on pre-treatment clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in ischemic stroke patients who underwent EVT. Logistic regression models were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and logistic regression models. Results: There were a total of 975 patients (550 men; mean±SD age 67.5±15.1 years), including 778 patients from the model development cohort, and 197 patients in the external validation cohort. The deep learning model trained on CT head and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 versus 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT angiography alone) and MR PREDICTS models (all Q<0.05). Conclusions: The discriminative performance of deep learning for predicting functional outcome following EVT was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.