基于冠状动脉CT特征的机器学习技术评价冠状动脉粥样硬化斑块风险

Evaluation of coronary atheroscleroticplaque risk by machine learning technology based on coronary CT angiography features

冠状动脉CT血管造影(CCTA)作为冠心病的一线诊断工具,不但可以评估冠状动脉管腔狭窄的程度,也可以进一步分析斑块组成、形态和易损性,而基于CCTA图像的冠周脂肪CT定量检测则可以反映冠状动脉炎症情况,用于综合评估心血管事件远期风险。在此基础上,随着人工智能和影像组学技术的发展,冠心病患者个体化危险分层和治疗决策的制定越来越多地应用到了机器学习技术,该技术可以深度挖掘组织影像标志物,整合临床、生物学和CCTA影像学信息,构建精准无创预测模型,为进一步精确评估心血管风险提供强有力的支持。本文详细综述了CCTA检查在检测定量斑块易损性及冠状动脉炎症中的作用,并简要总结了基于影像组学的机器学习算法模型评估冠状动脉粥样硬化斑块风险的最新研究进展。

Coronary CT angiography(CCTA)is a first-line diagnostic tool for coronary artery disease,can not only quantitate the severity of coronary artery stenosis,but also further evaluate the plaque composition,morphology and vulnerability.The quantitative detection of perivascular fat based on CCTA imaging can be served as vessel inflammation state for comprehensively evaluation of long-term risk of cardiovascular events.With development of artificial intelligence and radiomics technology,machine learning is more and more applied to individual risk stratification and treatment decision-making of coronary artery diseases.Machine learning can deeply mine histological imaging markers,integrate clinical,biological and CCTA imaging information,generate accurate non-invasive prediction model,and provide support for further evaluation of cardiovascular risk.This article reviews the role of CCTA in the detection and quantification of plaque vulnerability and inflammation,and briefly summarizes the research advance of machine learning algorithm combined with radiomics in the evaluation of atherosclerotic plaque risk.