Ms. Sarah Fricano Profile

Sarah Fricano

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Proceedings Article | 15 February 2021 Presentation + Paper

Clot organization on histology is associated with radiomics features that predict treatment outcomes from mechanical thrombectomy

Tatsat R. Patel, Muhammad Waqas, Sarah Fricano, Adam Dmytriw, John Tomaszewski, Elad Levy, Kenneth Snyder, Jason Davies, Adnan Siddiqui, Vincent Tutino

Proceedings Volume 11603, 116030L (2021) https://doi.org/10.1117/12.2581130

KEYWORDS: Rutherfordium, Computed tomography, Statistical analysis, Image segmentation, Image analysis, Blood, Medical imaging, Machine vision, Ischemic stroke

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Radiomics is an emerging computer vision technique that extracts high level textural information from medical images. Several studies have reported associations between radiomics features(RFs) and first pass effect(FPE) after mechanical thrombectomy(MT) treatment of acute ischemic stroke(AIS). However, the pathobiology behind the manifestation of such RFs remains unknown. To that end, we collected 15 clots samples retrieved from AIS patients (FPE: 5/15) treated with MT therapy along with their pre-treatment CT imaging (non-contrast CT–NCCT, and CT Angiography–CTA). We then segmented the clot regions on co-registered CT images and extracted 293 RFs, including. 1). shape-size metrics, 2). first-order statistics, and 3). higher order texture features. Univariate analysis was performed to test for significant differences in these RFs between FPE and non-FPE cases. Hematoxylin and eosin-stained clot sections from these cases were analyzed by Orbit Image Analysis software to determine if clot composition (i.e. % red blood cell-RBC, white blood cell-WBC, fibrin/platelets-FP) and structure (i.e. heterogeneity, organization) was significantly related to these RFs. Our results indicated that 5RFs, all from higher-order textural feature analysis, were significantly associated with FPE. These RFs were also associated with patient outcomes (delta NIHSS), albiet less significantly. There was no difference in RFs among clots of different composition (i.e. low vs high RBC clots), however, there were significant associations between the 5 RFs and clot organization parameters indicating that clots with ordered structures were easier to remove. These results need to be validated in larger datasets to establish the ability of RFs to predict FPE.

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