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

项目摘要 在过去的四年里，我们的研究小组从美国和加拿大的中心收集了超过1800例中风栓塞， 脑卒中血栓栓塞影像学和病理学登记处（STRIP）。STRIP注册表是我们的 目前R01奖的目标1，是非常富有成效的。我们已经进一步阐明了血栓与 组织学和血运重建结果，以及凝块组织学和成像特征。条带 登记研究还使我们能够发现卒中血栓形成的新机制，催化血栓切除术 设备研究和溶栓相关研究。 我们认为，我们登记研究中最有影响力的发现将是揭示以下方面的关联： 取出栓子的组织病理学和卒中病因学。开发预测中风病因的工具很重要 因为将近40%的中风病因不明确定卒中病因（即心源性与 大动脉粥样硬化）是重要的，因为二级卒中预防策略高度依赖于 中风病因的确定。然而，当我们进行表面的定量分析时， 纤维蛋白、血小板、白细胞和红细胞密度之间的关系，我们的结果没有揭示。但我们 假设基于对取出的卒中栓子的分析来预测卒中病因仍然是可行的 通过深度学习和机器学习方法。机器学习和深度学习方法可以 还有助于揭示与中风相关的器械和药理学失效相关的组织学特征 血运重建 因此，这份行政补充文件的目标是：1）允许我们的 超过1800个检索到的凝块样本的数据库以及来自Aim 1的所有可用匿名临床数据， 我们目前的R01，以促进深度学习和机器学习，2）在整体上进行深度学习 从这些患者身上提取样本，以确定各种深度学习和机器学习算法是否可以 仅根据取出的卒中栓子的组织学外观，用于预测卒中病因。

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.