Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT Angiography

项目总结 冠状动脉疾病仍然是全球主要的死亡原因，超过一半的人 患有心肌梗塞(心脏病发作)的患者没有先兆症状。慢性阻塞性肺疾病患者的研究 冠状动脉疾病传统上只关注冠状动脉狭窄(狭窄)的严重性 动脉粥样硬化斑块，而不是冠状动脉斑块的不良特征 容易破裂并引发心肌梗死。冠状动脉CT血管成像(CTA)是一种无创性检查 可同时评估冠状动脉狭窄和斑块特征的测试。然而，目前CTA是 在视觉上解释为狭窄。CTA狭窄严重程度和斑块特征的定量测量为 不是目前临床常规的一部分。 我们建议开发新的图像处理算法，用于全自动、稳健的图像量化 冠状动脉斑块的CTA特征。我们还建议自动量化脂肪的特征 冠状动脉周围组织(冠脉周围脂肪组织，PCAT)，已被证明 区分易破裂、高风险的冠状动脉斑块和稳定的斑块。我们建议应用机器 有效结合狭窄、斑块和PCAT特征的学习方法，以及患者的临床数据， 转化为新的综合风险评分，用于预测未来的不良心血管事件。我们将对此进行评估 现实世界、前瞻性、里程碑式的苏格兰心脏试验的风险评分(包括参与试验的所有2073名患者 试验的CTA分支)，在大型多中心患者登记处增加了外部验证，具有可用的CTA 心血管事件的扫描、临床数据和随访(致命性和非致命性心肌梗塞和 总计7844名患者死于心血管疾病)。我们提出三个具体目标： 1)改进、扩展和自动化整个冠状动脉的斑块和管腔的测量 并对连续CTA中斑块变化的测量进行标准化； 2)评估自动量化的斑块特征和PCAT特征对 在未来苏格兰心脏试验和多中心CTA登记中对未来MACE的预测； 3)开发和评估具有完全外部验证的新的自动患者风险评分组合 患者临床数据，CTA测量的定量斑块特征和PCAT特征，使用机器 学习-在前瞻性的苏格兰心脏试验和多中心CTA中预测未来的MACE事件 注册处。 拟议的工作将使自动、多方面和可重复的斑块、狭窄和 CTA的PCAT，结合反映不良心血管事件可能性的客观风险评分。 这项工作将提供一种新颖的、个性化的、现实世界的范式，客观而准确地识别 来自常规CTA成像的有未来心血管事件风险的个体患者。

PROJECT SUMMARY Coronary artery disease remains the leading cause of death worldwide, and more than half of the individuals suffering myocardial infarction (heart attacks) have no premonitory symptoms. Studies of patients with coronary artery disease have traditionally focused only on the severity of narrowing (stenosis) of the coronary arteries by atherosclerotic plaques, rather than the adverse features of coronary plaques which are predisposed to rupture and precipitate myocardial infarction. Coronary CT Angiography (CTA) is a noninvasive test that allows assessment of both coronary stenosis and plaque characteristics. Currently, however, CTA is interpreted visually for stenosis. Quantitative measurements of CTA stenosis severity and plaque features are not part of current clinical routine. We propose to develop novel image processing algorithms for fully automated, robust quantification of coronary plaque features from CTA. We also propose to automatically quantify the characteristics of adipose tissue around the coronary arteries (pericoronary adipose tissue, PCAT), which have been shown to differentiate rupture-prone, high-risk coronary plaques from stable ones. We propose to apply machine learning methods to efficiently combine stenosis, plaque and PCAT features, along with patient clinical data, into a new integrated risk score for the prediction of future adverse cardiovascular events. We will evaluate this risk score in the real-world, prospective, landmark SCOT-HEART trial (including all 2073 patients in the CTA arm of the trial), with added external validation in large multicenter patient registries, with available CTA scans, clinical data, and followup for cardiovascular events (fatal and non-fatal myocardial infarction and cardiovascular death in a grand total of 7844 patients). We propose three specific aims: 1) To refine, expand and automate measurements of coronary plaque and lumen for the entire coronary artery tree, and to standardize measurement of plaque changes in serial CTA; 2) To evaluate the prognostic value of automatically-quantified plaque features and PCAT characteristics for the prediction of future MACE in the prospective SCOT-HEART trial and multicenter CTA registries; 3) To develop and evaluate with full external validation a new automated patient risk score—combining patient clinical data, CTA-measured quantitative plaque features and PCAT characteristics, using machine learning—for the prediction of future MACE events in the prospective SCOT-HEART trial and multicenter CTA registries. The proposed work will enable automated, multi-faceted and reproducible analysis of plaque, stenosis and PCAT from CTA, combined with objective risk scores reflecting likelihood of adverse cardiovascular events. This work will provide a novel, personalized, real-world paradigm that objectively and accurately identifies individual patients at risk of future cardiovascular events, from routine CTA imaging.