通过分析冠状动脉周围脂肪的影像特征，预测狭窄是否会导致心肌缺血。
Pericoronary adipose tissue attenuation as a predictor of functional severity of coronary stenosis
- 利用CT扫描中的冠周脂肪影像特征，量化炎症状态。
- 机器学习模型准确率达84%，可识别可能导致缺血的狭窄病变。
- 无需侵入性检查，即可从常规CT推测病变功能严重性，适合临床筛查。
本研究旨在通过分析冠状动脉狭窄周围冠周脂肪（PCAT）的低层级影像组学特征，评估其炎症状态，进而判断狭窄的功能意义。基于72例接受冠状动脉计算机断层扫描血管成像（CCTA）的患者数据，结合前期研究的3D分割与计算流体动力学（CFD）模拟结果，自动提取主要心外膜分支中心线，并采用高斯核回归估计健康血管管径以定位病变。在每支血管及以病变为中心、向外延伸两个血管半径范围内的区域中，计算了包括脂肪体积和平均衰减（FAI）在内的多项PCAT特征，分析其与血流储备分数（FFR）和壁面剪切应力（WSS）等血流动力学生物标志物的相关性。这些特征还用于构建机器学习（ML）分类器，以区分可能引起缺血的病变。结果显示，血流动力学显著狭窄（即FFR < 0.80）时，PCAT平均衰减较高，但统计差异有限；而基于PCAT特征训练的ML模型在区分潜在缺血性病变上表现良好，平均准确率达到0.84。结论表明，PCAT衰减与冠状动脉狭窄的功能状态相关，可用于构建机器学习模型预测潜在缺血。意义在于：PCAT特征可从常规CCTA图像中获取，无需侵入性FFR检查即可预估病变的血流动力学特性。
原文摘要 · Abstract (English)
Objective: This study aims to evaluate the functional significance of coronary stenosis by analyzing low-level radiomic features of the pericoronary adipose tissue (PCAT) surrounding the lesions, which are indicative of its inflammation status. Methods: A dataset of 72 patients who underwent coronary computed tomography angiography (CCTA) was analyzed, with 3D segmentation and computational fluid dynamics (CFD) simulations from a prior study. Centerlines of the main epicardial branches were automatically extracted, and lesions identified using Gaussian kernel regression to estimate healthy branch caliber. PCAT features were computed per vessel following guideline recommendations and per lesion within a region extending radially for two vessel radii. Features like fat volume and mean attenuation (FAI) were analyzed for their relationship with CFD-derived hemodynamic biomarkers, such as fractional flow reserve (FFR) and wall shear stress (WSS). These features also informed a machine learning (ML) model for classifying potentially ischemic lesions. Results: PCAT exhibited, on average, higher attenuation in the presence of hemodynamically significant lesions (i.e., FFR < 0.80), although this difference was of limited statistical significance. The ML classifier, trained on PCAT features, successfully distinguished potentially ischemic lesions, yielding average accuracy of 0.84. Conclusion: PCAT attenuation is correlated with the functional status of coronary stenosis and can be used to inform ML models for predicting potential ischemia. Significance: PCAT features, readily available from CCTA, can be used to predict the hemodynamic characteristics of a lesion without the need for an invasive FFR examination.
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