Abstract P330: Optimizing Predictions of Infarct Core Using Machine Learning

Introduction: Evaluation of infarct core by advanced neuroimaging has facilitated patient selection for endovascular stroke therapy (EST), however the accuracy of machine-learning analysis compared to these modalities remains unexplored. We test the performance of computed tomography-Alberta Stroke Program Early Computed Tomography Score (CT- ASPECTS) vs. Computed Tomography Perfusion (CTP)-RAPID, vs. an extension of our novel machine-learning model, Deep Symmetry-sensitive Network (DeepSymNet [ref]), using the final infarct volume (FIV) in patients with rapid and successful endovascular reperfusion as the gold standard. Methods and Materials: We identified consecutive patients with large vessel occlusion acute ischemic stroke that underwent EST with TICI 2b/3 reperfusion. FIV was determined by volumetric measurements on 24-48h DWI MRI. The DeepSymNet algorithm combines symmetric and absolute brain representations and had been trained to predict CTP-RAPID core size from CTA source images acquired at presentation. Performance at predicting FIV was determined by Pearson’s correlation for CT- ASPECTS, CTP-RAPID, and DeepSymNet. Data are presented as median [IQR]. Results: Among the 76 patients that met inclusion criteria, 55.2% were male, the median age was 68 years [54-77], and 32.8% were White. 71% of the patients demonstrated an MCA occlusion, and 55% of all occlusions were left-sided. Median ASPECTS on presentation was 8 [7-8.5] and the median FIV was 10 mL [2-37]. ASPECTS, CTP-RAPID and DeepSymNet all correlated with FIV, with comparable performances from ASPECTS (R 2 =-0.398) and CTP-RAPID (R 2 =0.403) and superior performance by DeepSymNet (R 2 =-0.606)(Table). Conclusions: The DeepSymNet machine learning model analyzing CTA source images demonstrated superior performance to ASPECTS and CTP-RAPID in FIV prediction. These findings suggest machine learning models may provide improved predictions of infarct core and selection for EST.