Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; University of Groningen, University Medical Center Groningen, Center for Medical Imaging, Groningen, the Netherlands.
Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
Eur J Radiol. 2019 Jul;116:76-83. doi: 10.1016/j.ejrad.2019.04.013. Epub 2019 Apr 27.
The purpose of this study is to assess the value of an automated model-based plaque characterization tool for the prediction of major adverse cardiac events (MACE).
We retrospectively included 45 patients with suspected coronary artery disease of which 16 (33%) experienced MACE within 12 months. Commercially available plaque quantification software was used to automatically extract quantitative plaque morphology: lumen area, wall area, stenosis percentage, wall thickness, plaque burden, remodeling ratio, calcified area, lipid rich necrotic core (LRNC) area and matrix area. The measurements were performed at all cross sections, spaced at 0.5 mm, based on fully 3D segmentations of lumen, wall, and each tissue type. Discriminatory power of these markers and traditional risk factors for predicting MACE were assessed.
Regression analysis using clinical risk factors only resulted in a prognostic accuracy of 63% with a corresponding area under the curve (AUC) of 0.587. Based on our plaque morphology analysis, minimal cap thickness, lesion length, LRNC volume, maximal wall area/thickness, the remodeling ratio, and the calcium volume were included into our prognostic model as parameters. The use of morphologic features alone resulted in an increased accuracy of 77% with an AUC of 0.94. Combining both clinical risk factors and morphological features in a multivariate logistic regression analysis increased the accuracy to 87% with a similar AUC of 0.924.
An automated model based algorithm to evaluate CCTA-derived plaque features and quantify morphological features of atherosclerotic plaque increases the ability for MACE prognostication significantly compared to the use of clinical risk factors alone.
本研究旨在评估一种基于自动模型的斑块特征分析工具在预测主要不良心脏事件（MACE）中的价值。
我们回顾性纳入了 45 例疑似冠心病患者，其中 16 例（33%）在 12 个月内发生了 MACE。使用商业可得的斑块定量软件自动提取定量斑块形态学参数：管腔面积、管壁面积、狭窄百分比、管壁厚度、斑块负荷、重构比、钙化面积、富含脂质的坏死核心（LRNC）面积和基质面积。这些测量值是基于管腔、管壁和每种组织类型的完全 3D 分段，在 0.5mm 的间隔处进行的。评估这些标志物和传统危险因素对预测 MACE 的区分能力。
仅使用临床危险因素的回归分析得出的预测准确性为 63%，相应的曲线下面积（AUC）为 0.587。基于我们的斑块形态分析，最小帽厚度、病变长度、LRNC 体积、最大管壁面积/厚度、重构比和钙体积被纳入我们的预后模型作为参数。仅使用形态特征的方法可将准确性提高到 77%，AUC 为 0.94。将临床危险因素和形态特征结合在多元逻辑回归分析中，可将准确性提高到 87%，AUC 相似，为 0.924。
与仅使用临床危险因素相比，一种基于自动模型的算法来评估 CCTA 衍生的斑块特征并定量分析动脉粥样硬化斑块的形态特征，可显著提高对 MACE 的预测能力。
