Biologically Informed Clot Histomics Are Predictive of Acute Ischemic Stroke Etiology

Toward development of diagnostics for cryptogenic stroke, we hypothesize that histomic features of stroke blood clots retrieved by mechanical thrombectomy could be used to delineate stroke etiology.

Clots were retrieved from patients undergoing thrombectomy, and etiology was determined by the trial of TOAST (Trial of Org 10172 in Acute Stroke Treatment) score. After sectioning and hematoxylin and eosin staining, clot components (red blood cells [RBCs], fibrin-platelet aggregates [FPs], and white blood cells [WBCs]) were segmented on whole slide images. Histomic features were engineered to capture structural distribution of RBC/FP regions, including radiomics, radial composition, and RBC/FP object features. To locally characterize WBCs, textural features derived from nuclear and extranuclear regions were computed from each WBC to define classes, which we summarized into class frequency distributions. Univariate and multivariate statistics were used to identify significant differences in engineered features between large artery atherosclerosis (LAA) and cardioembolic cases. The top 3 significant RBC/FP and WBC features were used to train a complement Naïve Bayes model, which was then used to predict the etiology of cryptogenic cases.

In our data (n=53), 31 clots were cardioembolic, 8 were LAA, 4 were of strokes of other determined etiology, and 10 were cryptogenic. We identified 17 significant RBC/FP features and 3 significant WBC class frequency distributions that were different between cardioembolic and LAA. A complement Naïve Bayes model accurately classified cardioembolic versus LAA with a validation area under the receiver operating characteristic curve of 0.87±0.03, a performance substantially higher to using clot component percent composition (area under the receiver operating characteristic curve=0.69±0.16) that is the current state-of-the-art. Further, cryptogenic cases were reliably classified as cardioembolic or LAA in cross-validation analysis.

We present a first-of-its-kind histomics pipeline to robustly quantify the complex structure and WBC heterogeneity in acute ischemic stroke clots and classify cryptogenic cases. We hope this work begins to pave the way for histopathology biomarkers for stroke etiology diagnosis.