Ye Kunlin, Zhang Lingtao, Zhou Hao, Mo Xukai, Shi Changzheng
Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Subingtian center for speed research and training/Guangdong Key Laboratory of speed capability research, School of physical education, Jinan University,Guangzhou, China.
Ann Med. 2025 Dec;57(1):2431606. doi: 10.1080/07853890.2024.2431606. Epub 2024 Dec 12.
This study expolored the relationship between perivascular adipose tissue (PVAT) radiomic features derived from coronary computed tomography angiography (CCTA) and the presence of coronary artery plaques. It aimed to determine whether PVAT radiomic could non-invasively assess vascular inflammation associated with plaque presence.
In this retrospective cohort study, data from patients undergoing coronary artery examination between May 2021 and December 2022 were analyzed. Demographics, clinical data, plaque location and stenosis severity were recorded. PVAT radiomic features were extracted using PyRadiomics with key features selected using Least Absolute Shrinkage and Selection Operator (LASSO) and recursive feature elimination (RFE) to create a radiomics signature (RadScore).Stepwise logistic regression identified clinical predictors. Predictive models (clinical, radiomics-based and combined) were constructed to differentiate plaque-containing segments from normal ones. The final model was presented as a nomogram and evaluated using calibration curves, ROC analysis and decision curve analysis.
Analysis included 208 coronary segments from 102 patients. The RadScore achieved an Area Under the Curve (AUC) of 0.897 (95% CI: 0.88-0.92) in the training set and 0.717 (95% CI: 0.63-0.81) in the validation set. The combined model (RadScore + Clinic) demonstrated improved performance with an AUC of 0.783 (95% CI: 0.69-0.87) in the validation set and 0.903 (95% CI: 0.83-0.98) in an independent test set. Both RadScore and combined models significantly outperformed the clinical model ( < .001). The nomogram integrating clinical and radiomics features showed robust calibration and discrimination (c-index: 0.825 in training, 0.907 in testing).
CCTA-based PVAT radiomics effectively distinguished coronary artery segments with and without plaques. The combined model and nomogram demostrated clinical utility, offering a novel approach for early diagnosis and risk stratification in coronary heart disease.
本研究探讨了源自冠状动脉计算机断层扫描血管造影（CCTA）的血管周围脂肪组织（PVAT）影像组学特征与冠状动脉斑块存在之间的关系。其目的是确定PVAT影像组学是否能够无创评估与斑块存在相关的血管炎症。
在这项回顾性队列研究中，分析了2021年5月至2022年12月期间接受冠状动脉检查的患者的数据。记录了人口统计学、临床数据、斑块位置和狭窄严重程度。使用PyRadiomics提取PVAT影像组学特征，并使用最小绝对收缩和选择算子（LASSO）和递归特征消除（RFE）选择关键特征以创建影像组学特征（RadScore）。逐步逻辑回归确定临床预测因素。构建预测模型（临床、基于影像组学和联合模型）以区分含斑块节段和正常节段。最终模型以列线图形式呈现，并使用校准曲线、ROC分析和决策曲线分析进行评估。
分析纳入了102例患者的208个冠状动脉节段。RadScore在训练集中的曲线下面积（AUC）为0.897（95%CI：0.88 - 0.92），在验证集中为0.717（95%CI：0.63 - 0.81）。联合模型（RadScore + 临床因素）表现出更好的性能，在验证集中的AUC为0.783（95%CI：0.69 - 0.87），在独立测试集中为0.903（95%CI：0.83 - 0.98）。RadScore和联合模型均显著优于临床模型（P < 0.001）。整合临床和影像组学特征的列线图显示出稳健的校准和区分能力（训练集c指数：0.825，测试集c指数：0.907）。
基于CCTA的PVAT影像组学有效地区分了有斑块和无斑块的冠状动脉节段。联合模型和列线图显示出临床实用性，为冠心病的早期诊断和风险分层提供了一种新方法。
