Wu Yue, Qi Haicheng, Zhang Xinwei, Xing Yan
Radiological Imaging Center, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, China (Y.W.).
Medical Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China (H.Q., X.Z., Y.X.).
Acad Radiol. 2025 Jan;32(1):91-101. doi: 10.1016/j.acra.2024.08.022. Epub 2024 Sep 20.
To evaluate the ability of the radiomic characteristics of pericoronary adipose tissue (PCAT) as determined by coronary computed tomography angiography (CCTA) to predict the likelihood of major adverse cardiovascular events (MACEs) within the next five years.
In this retrospective casecontrol study, the case group consisted of 210 patients with coronary artery disease (CAD) who developed MACEs within five years, and the control group consisted of 210 CAD patients without MACEs who were matched with the case group patients according to baseline characteristics. Both groups were divided into training and testing cohorts at an 8:2 ratio. After data standardization and the exclusion of features with Pearson correlation coefficients of |r| ≥ 0.9, independent logistic regression models were constructed using selected radiomics features of the proximal PCAT of the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) via least absolute shrinkage and selection operator (LASSO) techniques. An integrated PCAT radiomics model including all three coronary arteries was also developed. Five models, including individual PCAT radiomics models for the LAD artery, LCX artery, and RCA; an integrated radiomics model; and a fat attenuation index (FAI) model, were assessed for diagnostic accuracy via receiver operating characteristic (ROC) curves, calibration curves, and decision curves.
Compared with the FAI model (AUC=0.564 in training, 0.518 in testing), the integrated radiomics model demonstrated superior diagnostic performance (area under the curve [AUC]=0.923 in training, 0.871 in testing). The AUC values of the integrated model were greater than those of the individual coronary radiomics models, with all the models showing goodness of fit (P > 0.05). The decision curves indicated greater clinical utility of the radiomics models than the FAI model.
PCAT radiomics models derived from CCTA data are highly valuable for predicting future MACE risk and significantly outperform the FAI model.
评估冠状动脉计算机断层扫描血管造影（CCTA）测定的冠状动脉周围脂肪组织（PCAT）的放射组学特征预测未来五年内主要不良心血管事件（MACE）发生可能性的能力。
在这项回顾性病例对照研究中，病例组由210例在五年内发生MACE的冠心病（CAD）患者组成，对照组由210例未发生MACE的CAD患者组成，这些患者根据基线特征与病例组患者匹配。两组均按8:2的比例分为训练队列和测试队列。在数据标准化并排除Pearson相关系数|r|≥0.9的特征后，通过最小绝对收缩和选择算子（LASSO）技术，使用左前降支（LAD）动脉、左旋支（LCX）动脉和右冠状动脉（RCA）近端PCAT的选定放射组学特征构建独立的逻辑回归模型。还开发了一个包括所有三支冠状动脉的综合PCAT放射组学模型。通过受试者操作特征（ROC）曲线、校准曲线和决策曲线评估了五个模型的诊断准确性，这五个模型包括LAD动脉、LCX动脉和RCA的单个PCAT放射组学模型、一个综合放射组学模型和一个脂肪衰减指数（FAI）模型。
与FAI模型（训练时AUC = 0.564，测试时AUC = 0.518）相比，综合放射组学模型表现出更好的诊断性能（训练时曲线下面积[AUC] = 0.923，测试时AUC = 0.871）。综合模型的AUC值大于单个冠状动脉放射组学模型的AUC值，所有模型均显示出良好的拟合度（P > 0.05）。决策曲线表明放射组学模型比FAI模型具有更大的临床实用性。
从CCTA数据得出的PCAT放射组学模型对于预测未来MACE风险具有很高的价值，并且明显优于FAI模型。
