Abstract 15640: Histomic-Based Clot Structure Quantification for Prediction of Ischemic Stroke Etiology

Abstract

Introduction: Determining stroke etiology is paramount to clinical management and prevention of recurrent strokes. Although stroke patients undergo extensive post-treatment work-ups, 30-40% of cases remain cryptogenic. Hypothesis: Engineered histomic features from digital pathology images of stroke blood clots collected during mechanical thrombectomy (MT) can be used to delineate stroke etiology. Methods: For clots retrieved from patients undergoing MT, etiology was determined by the trial of Trial of Org 10172 in Acute Stroke Treatment (TOAST) score. After sectioning and H&E staining, clot components (red blood cells-RBCs, fibrin-platelet regions-FP, and white blood cells-WBCs) were segmented from whole slide images. Histomic features were engineered to capture the structural distribution of RBC/FP regions throughout the clot. To measure clot WBC diversity, WBC instances were clustered into WBC “classes” based on texture, and summarized as a class frequency distribution (CFD). The three most significant RBC/FP and WBC features between large artery atherosclerosis (LAA) and cardioembolic (CE) cases were used to train a complement Naïve Bayes (CNB) model, which was then implemented to predict the etiology of cryptogenic cases. Results: In our data (n=53), 31 clots were CE, 8 were LAA, 4 were from strokes of other determined etiology, and 10 were cryptogenic. 17 significant RBC/FP features and 3 significant WBC CFDs were different between CE and LAA. The trained CNB model accurately classified CE vs. LAA with a validation AUC of 0.87±0.03, exhibiting superior performance to a model trained using common clot percent cellular composition metrics (AUC=0.69±0.16). Furthermore, we showed the potential to classify cryptogenic cases as CE or LAA. Conclusions: This first-of-its-kind, biologically-informed clot histomic pipeline captured significant information that may augment clinical and laboratory features used in stroke etiology classification.