Abstract

BACKGROUND AND OBJECTIVES: Histologic and transcriptomic analyses of retrieved stroke clots have identified features associated with patient outcomes. Previous studies have demonstrated the predictive capacity of histology or expression features in isolation. Few studies, however, have investigated how paired histologic image features and expression patterns from the retrieved clots can improve understanding of clot pathobiology and our ability to predict long-term prognosis. We hypothesized that computational models trained using clot histomics and mRNA expression can predict early neurological improvement (ENI) and 90-day functional outcome (modified Rankin Scale Score, mRS) better than models developed using histological composition or expression data alone. METHODS: We performed paired histological and transcriptomic analysis of 32 stroke clots. ENI was defined as a delta-National Institutes of Health Stroke Score/Scale > 4, and a good long-term outcome was defined as mRS ≤2 at 90 days after procedure. Clots were H&E-stained and whole-slide imaged at 40×. An established digital pathology pipeline was used to extract 237 histomic features and to compute clot percent composition (%Comp). When dichotomized by either the ENI or mRS thresholds, differentially expressed genes were identified as those with absolute fold-change >1.5 and q < 0.05. Machine learning with recursive feature elimination (RFE) was used to select clot features and evaluate computational models for outcome prognostication. RESULTS: For ENI, RFE identified 9 optimal histologic and transcriptomic features for the hybrid model, which achieved an accuracy of 90.8% (area under the curve [AUC] = 0.98 ± 0.08) in testing and outperformed models based on histomics (AUC = 0.94 ± 0.09), transcriptomics (AUC = 0.86 ± 0.16), or %Comp (AUC = 0.70 ± 0.15) alone. For mRS, RFE identified 7 optimal histomic and transcriptomic features for the hybrid model. This model achieved an accuracy of 93.7% (AUC = 0.94 ± 0.09) in testing, also outperforming models based on histomics (AUC = 0.90 ± 0.11), transcriptomics (AUC = 0.55 ± 0.27), or %Comp (AUC = 0.58 ± 0.16) alone. CONCLUSION: Hybrid models offer improved outcome prognostication for patients with stroke. Identified digital histology and mRNA signatures warrant further investigation as biomarkers of patient functional outcome after thrombectomy.