Exploration of the Prognostic Value of Radiomics Analysis and Deep Learning of Coronary Plaques on Computed Tomography for Cardiovascular Events

计算机断层扫描冠状动脉斑块放射组学分析和深度学习对心血管事件的预后价值的探索

• 批准号: 428222922
• 负责人: Professor Dr. Marc Dewey
• 金额: --
• 依托单位: Institut für Radiologie (CCM) (aufgelöst)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/428222922?language=en
• 关键词: Exploration; Prognostic; Value; Radiomics; Analysis

Background and Objectives: Coronary artery plaques may lead to cardiovascular events and there is early evidence that noninvasive detection of vulnerable plaque features on computed tomography (CT) and subsequent changes in medical management may lead to improved outcomes. CT is an increasingly used test in patients with suspected coronary artery disease (CAD) and is the best noninvasive imaging test to capture three-dimensional information about plaques in all coronary artery segments in one examination. However, existing data is limited by variable definitions of plaque morphology, inconsistency in event collection, and mixed study designs. Moreover, there is limited prospective data on the prognostic value of coronary CT plaque features in stable chest pain patients. We propose radiomics analysis and deep learning (DL) of coronary plaques on CT to identify patients at high-risk of cardiovascular events. In this project, we will explore the capabilities of radiomics and DL to comprehensively characterise and quantify coronary artery plaques.Methods and Work Programme: We prospectively collected CT image data from more than 1700 patients in the multicentre DISCHARGE trial in the clinically relevant prognostic setting of suspected CAD and will conduct long-term clinical follow-up. In the proposed project, we will test the prognostic value of radiomics and DL using convolutional neural networks for coronary artery plaques analysis. First, we will test the prognostic value of the coronary artery calcium score in this prospective patient cohort. Second, we will analyze all CT datasets using conventional plaque segmentation and classification (e.g., non-calcified, partially calcified, calcified) and compare the prognostic value with high-risk plaque features such as low-attenuation, positive remodelling, the napkin ring sign and spotty calcifications. Third, we will adapt and implement existing radiomics- and DL-based image-analysis algorithms to extract features of vulnerable plaques. This includes the above high-risk coronary artery plaque features, radiomic features and DL-predictions to compare the accuracy in prognostication of prospectively defined long-term clinical endpoints such as myocardial infarction and coronary revascularisation. The quantitative results of image analysis will be made available as a database with cardiovascular events.Anticipated Gain of Knowledge: We anticipate novel insights into the association of coronary plaque features with prognosis of stable chest pain patients which will strengthen the clinical implications of radiomics analysis and deep learning of coronary CT. Ultimately, this will allow identifying patients prone to suffer myocardial infarction and testing the validity and generalisability of advanced coronary CT plaque analysis using data from the population-based SCAPIS project of more than 25000 asymptomatic individuals which will have long-term follow-up data for the second three-year funding period.

背景和目的：冠状动脉斑块可能导致心血管事件，早期证据表明，在CT上对易损斑块特征的非侵入性检测以及随后医疗管理的改变可能会导致改善预后。CT在疑似冠状动脉疾病(CAD)患者中的应用越来越广泛，是一次检查中捕捉所有冠状动脉节段斑块的三维信息的最佳无创性影像检查。然而，现有的数据受到斑块形态的不同定义、事件收集的不一致以及混合研究设计的限制。此外，关于稳定性胸痛患者冠状动脉CT斑块特征的预后价值的前瞻性数据有限。我们建议对CT上冠状动脉斑块的放射组学分析和深度学习(DL)来识别心血管事件的高危患者。在这个项目中，我们将探索放射组学和数字减影技术全面描述和量化冠状动脉斑块的能力。方法和工作方案：我们前瞻性地收集了多中心出院试验中1700多名疑似冠心病患者的CT图像数据，并将进行长期的临床随访。在拟议的项目中，我们将使用卷积神经网络来测试放射组学和DL在冠状动脉斑块分析中的预后价值。首先，我们将测试冠状动脉钙化评分在这一前瞻性患者队列中的预后价值。其次，我们将使用传统的斑块分割和分类(例如，非钙化、部分钙化、钙化)分析所有CT数据集，并将其与高危斑块特征(如低密度、阳性重塑、餐巾环征和点状钙化)的预后价值进行比较。第三，我们将调整和实施现有的基于放射组学和基于DL的图像分析算法来提取易损斑块的特征。这包括上述高危冠状动脉斑块特征、放射学特征和DL-预测，以比较在预测心肌梗死和冠状动脉血运重建等预期确定的长期临床终点方面的准确性。图像分析的定量结果将作为心血管事件的数据库提供。知识的积累：我们期待对冠状动脉斑块特征与稳定性胸痛患者预后的关系有新的见解，这将加强放射组学分析和冠状动脉CT的深入学习的临床意义。最终，这将允许识别易患心肌梗死的患者，并使用基于人群的SCAPIS项目的数据来测试先进的冠状动脉CT斑块分析的有效性和普适性，该项目对25000多名无症状个人进行了第二个三年资助期的长期跟踪数据。