Pericoronary fat: MACE risk from non-contrast CT and the role of iodine perfusion in contrast CT

冠状动脉周围脂肪：非造影 CT 的 MACE 风险以及造影 CT 中碘灌注的作用

• 批准号: 10577558
• 负责人: Sanjay Rajagopalan
• 金额: $ 78.61万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577558
• 关键词: Adipose tissue; Advocate; Agatston Score; Angiography; Appearance; Archives; Arterial Fatty Streak; Arteries; Artificial Intelligence; Atherosclerosis; Big Data; Biological; Biology; Biomedical Engineering; Blood flow; Calcium; Cardiac; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cells; Clinical; Coronary; Coronary Arteriosclerosis; Coronary Artery Risk Development in Young Adults Study; Coronary artery; Detection; Diabetes Mellitus; Disease; Early Diagnosis; Engineering; Ensure; Event; Fatty acid glycerol esters; Future; Gender; Genes; Goals; Health; Image; Individual; Inflammation; Inflammatory; Infusion procedures; Institution; Iodine; Knowledge; Location; Machine Learning; Measures; Medicine; Methods; Modeling; Myocardial Infarction; Oncology; Pathway interactions; Patients; Perfusion; Phenotype; Physicians; Psoriasis; Race; Reproducibility; Research; Risk; Risk Factors; Role; Scanning; Scientist; Services; Severities; Site; Texture; Time; Tissues; University Hospitals; Validation; Vascular Diseases; X-Ray Computed Tomography; attenuation; calcification; cardiometabolic risk; cardiovascular disorder risk; cardiovascular health; cohort; comorbidity; contrast enhanced computed tomography; cost; deep learning; diverse data; evidence base; experimental study; health disparity; high risk; image processing; improved; indexing; meetings; mortality; multiple datasets; personalized medicine; programs; radiomics; research study; risk prediction; screening; success; theories; tool; vasa vasorum

Pericoronary fat: MACE risk from non-contrast CT and the role of iodine perfusion in contrast CT Summary Pericoronary adipose tissue (PCAT) inflammation is an important, emerging concept in coronary artery dis- ease, giving rise to the “outside-in” theory where inflammatory cells within PCAT, delivered by the vasa- vasorum, influence atherosclerosis plaque progression. Using cardiovascular CT images, we will use ad- vanced image processing and AI to better understand pericoronary fat appearance and to predict major ad- verse cardiovascular events (MACE). As cardiovascular disease remains the most common cause of death in the US, improved early detection, disease prediction, and patient management will positively impact health for many individuals. Using cardiac CT imaging (angiography, CCTA; perfusion, CCTP; and calcium score, CTCS) in elegant experiments and analyses, we will elucidate pericoronary fat assessments and create a new, inex- pensive CTCS assessment of pericoronary fat suitable for screening. As the principal pericoronary fat inflam- mation feature in CCTA is elevated HU, we will use CCTP to assess pericoronary fat perfusion and clarify the role of iodine on existing CCTA signatures, including confounds due to varying filling rates with obstructive dis- ease. Using paired images, we will associate CTCS pericoronary fat features to established ones from CCTA. Using appropriate pericoronary fat features from CTCS exams, we will predict major adverse cardiac events (MACE) without the iodine confound and combine with Agatston to get an even better prediction. Large CTCS cohorts enable interesting research studies. For example, using the serial Coronary Artery Risk Development in Young Adults (CARDIA) study, we will determine if pericoronary fat features precede the appearance of cal- cifications, giving credence to the “outside-in” theory. The CTCS exam is inexpensive (≤$99) at many institu- tions. At University Hospitals (UH), our nationally acknowledged free CTCS program currently servicing >13,000 patients/year with an archive of >65,000 cases, will provide an opportunity for big data, machine/deep learning analysis of PCAT. In addition, improved MACE prediction from PCAT plus Agatston will enable multi- ple future studies on health disparities, genes, cardiometabolic risk, co-morbidities (e.g., diabetes and psoria- sis), and cardio-oncology. To accomplish goals, we have assembled a world class team of biomedical engi- neers and physician scientists at University Hospitals/CWRU with deep knowledge of cardiovascular CT imag- ing and the biology of atherosclerosis. This proposal will ultimately advance the field of predictive medicine and propel new ways to pre-emptively detect at-risk patients so that they can be placed on evidence-based thera- pies.

冠状动脉周围脂肪：非增强CT的MACE风险和碘灌注在增强CT中的作用 总结 冠状动脉周围脂肪组织（PCAT）炎症是冠状动脉疾病中一个重要的新兴概念。 容易，引起了“由外而内”的理论，其中PCAT内的炎症细胞，由血管递送， vasorum，影响动脉粥样硬化斑块的进展。使用心血管CT图像，我们将使用AD- 先进的图像处理和人工智能，以更好地了解冠状动脉周围脂肪的外观，并预测主要的AD， 心血管事件（MACE）。由于心血管疾病仍然是最常见的死亡原因， 在美国，改善早期检测、疾病预测和患者管理将对健康产生积极影响， 许多个人。使用心脏CT成像（血管造影，CCTA;灌注，CCTP;和钙评分，CTCS） 在优雅的实验和分析中，我们将阐明冠状动脉周围脂肪的评估，并创建一个新的，inex- 适合筛查的冠状动脉周围脂肪的被动CTCS评估。作为主要的冠状动脉周围脂肪炎- CCTA的信息特征是HU升高，我们将使用CCTP评估冠状动脉周围脂肪灌注并阐明 碘对现有CCTA特征的作用，包括因阻塞性病变的充盈率不同而引起的混淆， 放松。使用配对图像，我们将CTCS冠状动脉周围脂肪特征与CCTA中已建立的特征相关联。 利用CTCS检查的冠状动脉周围脂肪特征，我们将预测主要的不良心脏事件 （MACE）没有碘混淆，并与Agatston联合收割机，以获得更好的预测。大型CTCS 群组能够进行有趣的研究。例如，使用连续冠状动脉风险开发 在年轻成人（CARDIA）研究中，我们将确定冠状动脉周围脂肪特征是否先于冠状动脉出现， 引用，相信“由外而内”的理论。CTCS考试价格便宜（≤ 99美元），在许多国家， 选择。在大学医院（UH），我们国家承认的免费CTCS计划目前为 > 13，000名患者/年，存档> 65，000例病例，将为大数据、机器/深度 PCAT的学习分析此外，PCAT加Agatston改善的MACE预测将使多 未来将对健康差异、基因、心脏代谢风险、合并症（例如，糖尿病和牛皮癣 SIS）和心脏肿瘤学。为了实现目标，我们组建了一支世界一流的生物医学工程团队- neers和医生科学家在大学医院/CWRU与心血管CT成像的深入了解， 以及动脉粥样硬化的生物学。这一提议将最终推动预测医学领域的发展， 推动新的方法来先发制人地发现高危患者，使他们能够接受循证治疗， 派