702 Multimodal CT Imaging of Ischemic Stroke Thrombi Identifies Scale-invariant Radiomic Features That Reflect Clot Biology

Abstract

INTRODUCTION: Biological interpretability of ischemic stroke clot imaging remains challenging. Paired CT/microCT imaging of ischemic stroke clots resected by thrombectomy has the potential to identify interpretable image features that are correlated among pre-treatment image modalities and post-treatment histopathology. METHODS: We performed multimodal CT-imaging and histology for 10 stroke clots retrieved by mechanical thrombectomy. Clots were manually segmented from co-registered, pre-treatment CTA and nCCT. For the same cases, resected clots were iodine-stained, and imaged with a ScanCo microCT-100 (4.9μm resolution). Afterwards, clots were subjected to histological processing (H&E staining) and whole slide scanned (40X). Clot radiomic features(RFs) (n = 93 per modality, 279 total) were extracted using PyRadiomics and histological composition was computed using Orbit Image Analysis. Correlation analysis was used to test associations between microCT and CTA (or nCCT) RFs as well as between RFs and histological composition. Statistical significance was considered at R = 0.65 and q < 0.05. RESULTS: From paired RF correlation analysis, we identified 23 scale-invariant RFs significantly correlated between microCT and CTA (18), and microCT and nCCT (5). Correlation of unpaired RFs identified 377 positively and 36 negatively correlated RFs between microCT and CTA, and 168 positively and 41 negatively correlated RFs between microCT and nCCT. Scale invariant RFs computed from CTA and nCCT demonstrated significant correlation with red blood cell and fibrin-platelet components, while microCT RFs were found to be correlated with WBC percent composition. CONCLUSIONS: This study is the first to complete a detailed statistical analysis of radiomic features across scale, and investigation of their relationship to histological composition. Our findings suggest that RFs from resected clot microCT can be used to identify strong and significant correlations between histology and pre-thrombectomy CT, with increased biological interpretability.