Impact of Clot Histological and Physical Properties on Revascularization Strategies in Acute Ischaemic Stroke - Administrative Supplement

项目总结

PROJECT SUMMARY In the last four years, our group collected >1800 stroke emboli from centers across the US and Canada as part of the Stroke Thromboembolism Registry of Imaging and Pathology (STRIP). The STRIP registry was our current R01 award’s Aim 1 and is very fruitful. We have further elucidated the association between clot histology and revascularization outcomes, as well as clot histology and imaging characteristics. The STRIP registry has also allowed us to uncover novel mechanisms in stroke thrombosis, catalyzing thrombectomy device research and thrombolysis related research in our lab. We believe that the most impactful finding from our registry will be uncovering associations between histopathology of retrieved emboli and stroke etiology. Developing tools to predict stroke etiology is important because nearly 40% of strokes are of unknown etiology. Determine stroke etiology (i.e. cardiac source versus large artery atherosclerosis) is important as secondary stroke prevention strategies are highly dependent on determination of stroke etiology. However, when we performed superficial quantitative analyses examining the relationships between fibrin, platelet, WBC and RBC density, our results were unrevealing. Still, we hypothesize that it remains feasible to predict stroke etiology based of analysis of retrieved stroke emboli through deep learning and machine learning approaches. Machine learning and deep learning approaches can also aid in uncovering histological features associated with device and pharmacological failure related to stroke revascularization. Thus, this administrative supplement’s goal of is to 1) allow for complete digitization and online archiving of our database of over 1800 retrieved clot specimens as well as all available anonymized clinical data from Aim 1 of our current R01 to facilitate deep learning and machine learning and 2) to perform deep learning on the whole slide specimens from these patients to determine if various deep learning and machine learning algorithms can be used to predict stroke etiology based solely off of the histological appearance of retrieved stroke emboli.