A Machine Learning Approach For CTA-based Plaque Characterization and Stroke Risk Prediction in Carotid Artery Atherosclerosis

项目总结/摘要 颈动脉粥样硬化是一种主要的血管危险因素，约占所有中风的15%。 颈动脉粥样硬化患者的一个主要风险标志是颈动脉狭窄或狭窄程度， 颈动脉腔虽然狭窄通常通过血管造影术量化，但成像也可以提供详细的 斑块的评估。我们的项目的动机是汇集与脆弱斑块元素相关的数据， 这可以通过成像来捕捉，增加了中风的风险。识别高危或脆弱的颈动脉 在中风发生前发现斑块非常重要，因为中风预防治疗，如颈动脉内膜切除术， 或支架植入术具有风险，理想情况下，仅应仅对中风风险最高的患者进行。CTA （计算机断层摄影血管造影术）是用于斑块成像的有吸引力的工具，因为它不依赖于操作者， 可以快速进行，并且比MRI更广泛地使用。虽然CTA提供了巨大的潜力， 评估这些斑块特征，小型研究尚未就其可靠性达成共识， 临床相关性在这个项目中，我们计划探索CTA对详细颈动脉血管壁的实用性 通过采用独特的大规模临床数据集和先进的算法进行成像。我们的总体 在这个R21项目的目标是进行发展和跨学科的研究，将奠定 为实施和验证新的基于CTA的技术奠定了基础，这些技术可以在 颈动脉粥样硬化患者的危险分层。我们的中心假设是，有基于CTA的 颈动脉斑块特征可以可靠地提取并用于中风风险分层，这将是更多 比标准狭窄分级更敏感和特异。为了实现我们的目标，我们将寻求两个具体的 目的：在具体目标1中，我们计划优化人类读者定义的斑块特征在预测中的使用。 罪魁祸首颈动脉斑块我们将进行一项关于CTA衍生的颈动脉斑块特征的盲法、多读者研究， 大规模临床数据集，用于测试CTA衍生的人类定义特征与卒中之间的关联， 并计算准确度指标。在具体目标2中，我们计划开发算法来自动表征 并在CTA中鉴别颈动脉斑块。我们将实现和测试图像处理算法， 根据CTA扫描自动计算中风相关的颈动脉斑块特征（来自Aim 1），以及 然后训练机器学习算法来区分罪魁祸首和无症状的颈动脉斑块。我们 我相信这项R21研究意义重大，因为它将建立一种新的机器学习辅助成像技术。 一种策略，可以帮助识别高风险的颈动脉斑块之前，他们导致中风，当他们 可以得到适当的治疗，以防止将来发生中风。

PROJECT SUMMARY/ABSTRACT Carotid artery atherosclerosis is a major vascular risk factor and accounts for approximately 15% of all strokes. A major risk marker in patients with carotid atherosclerosis has been the degree of narrowing, or stenosis, of the carotid artery lumen. While stenosis is often quantified via angiography, imaging can also provide detailed assessments of plaque. Our project is motivated by converging data that correlate vulnerable plaque elements, which can be captured with imaging, with increased stroke risk. Identifying high-risk or vulnerable carotid plaques before a stroke occurs is important because stroke prevention treatments, like carotid endarterectomy or stenting, carry risks and ideally should only be performed only those patients at highest risk of stroke. CTA (computed tomographic angiography) is an attractive tool for plaque imaging since it is not operator dependent, can be quickly performed, and is more widely available than MRI. Although CTA offers significant potential to evaluate these plaque features, small studies have not reached a consensus regarding their reliability and clinical relevance. In this project, we plan to explore the utility of CTA for the detailed carotid vessel wall imaging by employing a unique, large-scale clinical dataset and advanced algorithms. Our overarching objective in this R21 project is to conduct developmental and interdisciplinary research that will lay the foundation for the implementation and validation of novel CTA-based technologies that can be adopted in the risk stratification of patients with carotid atherosclerosis. Our central hypothesis is that there are CTA-based carotid plaque features that can be reliably extracted and used for stroke risk stratification, which will be more sensitive and specific than standard stenosis grading. To pursue our objective, we will pursue two specific aims: In Specific Aim 1 we plan to optimize the use of human reader defined plaque features in predicting culprit carotid plaques. We will perform a blinded, multi-reader study of CTA-derived carotid plaque features in a large scale clinical dataset to test the association between CTA-derived human-defined features and stroke, and compute accuracy metrics. In Specific Aim 2, we plan to develop algorithms to automatically characterize and discriminate culprit carotid plaque in CTA. We will implement and test image processing algorithms that automatically compute from a CTA scan stroke-associated carotid artery plaque features (from Aim 1), and then train a machine learning algorithm to distinguish culprit from asymptomatic carotid artery plaques. We believe that this R21 study is significant because it will establish a novel, machine learning-aided imaging strategy which can aid in identifying high-risk carotid artery plaques before they cause stroke and when they can be properly treated to prevent stroke from occurring in the future.