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

基于 CTA 的颈动脉粥样硬化斑块表征和中风风险预测的机器学习方法

• 批准号: 9904175
• 负责人: Ajay Gupta
• 金额: $ 12.2万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904175
• 关键词: Adopted; Algorithms; Angiography; Arterial Fatty Streak; Atherosclerosis; Biological Markers; Blinded; Carotid Arteries; Carotid Artery Plaques; Carotid Atherosclerotic Disease; Carotid Endarterectomy; Carotid Stenosis; Carotid stent; Clinical; Computer Vision Systems; Consensus; Data; Data Set; Development; Discrimination; Disease; Elements; Fostering; Foundations; Future; Hand; Hemorrhage; Human; Image; Imaging Device; Incidence; Institution; Interdisciplinary Study; Ischemia; Ischemic Stroke; Machine Learning; Magnetic Resonance Imaging; Medicine; Mission; Modeling; Modernization; National Heart, Lung, and Blood Institute; Operative Surgical Procedures; Patients; Performance; Prevention; Procedures; Process; Public Health; Reader; Receiver Operating Characteristics; Registries; Reproducibility; Research; Risk; Risk Factors; Risk Marker; Risk stratification; Scanning; Statistical Models; Stenosis; Stroke; Stroke prevention; Techniques; Technology; Testing; Training; Ulcer; Ultrasonography; Validation; Vascular Diseases; Work; X-Ray Computed Tomography; attenuation; automated algorithm; base; calcification; cerebrovascular; clinically relevant; cohort; convolutional neural network; feature extraction; high risk; image processing; improved; learning strategy; machine learning algorithm; novel; patient stratification; predictive modeling; radiologist; screening; stroke patient; stroke risk; systematic review; tool; vascular risk factor

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.

项目概要/摘要 颈动脉粥样硬化是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程度。 颈动脉管腔。虽然狭窄通常通过血管造影进行量化，但成像也可以提供详细的信息 斑块评估。我们的项目的动机是汇聚与易损斑块元素相关的数据， 可以通过成像捕获，增加中风风险。识别高风险或脆弱的颈动脉 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 或支架置入术存在风险，理想情况下应仅对中风风险最高的患者进行。计算机辅助技术协会 （计算机断层扫描血管造影）是斑块成像的一种有吸引力的工具，因为它不依赖于操作员， 可以快速执行，并且比 MRI 更广泛使用。尽管 CTA 具有巨大的潜力 评估这些斑块特征，小型研究尚未就其可靠性和 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。我们的首要任务 R21 项目的目标是进行发展和跨学科研究，这将奠定 为实施和验证可在以下领域采用的基于 CTA 的新型技术奠定了基础 颈动脉粥样硬化患者的危险分层。我们的中心假设是存在基于 CTA 的 可以可靠地提取颈动脉斑块特征并将其用于中风风险分层，这将更加重要 比标准狭窄分级更敏感、更特异。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，我们计划优化人类读者定义的斑块特征在预测中的使用 颈动脉斑块的罪魁祸首。我们将对 CTA 衍生的颈动脉斑块特征进行盲法、多读者研究 用于测试 CTA 衍生的人类定义特征与中风之间关联的大规模临床数据集， 并计算准确性指标。在具体目标 2 中，我们计划开发算法来自动表征 并在 CTA 中区分罪魁祸首颈动脉斑块。我们将实现并测试图像处理算法 根据 CTA 扫描自动计算中风相关颈动脉斑块特征（来自目标 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 相信这项 R21 研究意义重大，因为它将建立一种新颖的机器学习辅助成像 该策略可以帮助在高风险颈动脉斑块引起中风之前以及何时发生 可以得到适当的治疗，以防止将来发生中风。