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

项目摘要/摘要 颈动脉粥样硬化是一个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危险标志是颈动脉狭窄或狭窄的程度。 颈动脉管腔。虽然狭窄通常通过血管造影术进行量化，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聚与脆弱斑块元素相关的数据， 这可以通过成像捕捉到，但会增加中风的风险。识别高危或易受攻击的颈动脉 中风发生前的斑块很重要，因为中风预防治疗，如颈动脉内膜切除术 或支架，有风险，理想情况下只应该只对中风风险最高的患者进行手术。CTA (计算机断层血管造影术)是一种有吸引力的斑块成像工具，因为它不依赖于操作员， 可以快速进行，而且比MRI更广泛地使用。尽管CTA提供了巨大的潜力来 评估这些斑块的特征，小型研究还没有就它们的可靠性和 临床相关性。在这个项目中，我们计划探索CTA在详细的颈动脉血管壁中的应用 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。我们最重要的是 该R21项目的目标是进行发展和跨学科研究，为 实施和验证基于CTA的新技术的基础，这些技术可在 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 较标准的狭窄分级敏感和特异。为了实现我们的目标，我们将追求两个具体目标 目标：在具体目标1中，我们计划优化使用人类读者定义的斑块特征来预测 罪犯颈动脉斑块。我们将进行一项CTA来源的颈动脉斑块特征的盲法多读者研究。 一个大规模的临床数据集，以测试CTA衍生的人类定义特征与中风之间的关联， 并计算精度指标。在具体目标2中，我们计划开发算法来自动表征 并对CTA中的颈动脉斑块进行鉴别。我们将实现和测试图像处理算法， 从CTA扫描自动计算与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犯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、机器学习辅助的成像 一种策略，可以帮助识别高危颈动脉斑块，在它们导致中风之前以及何时 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.