背景:冠状动脉粥样硬化性心脏病(CAD)的演变与冠状动脉周围脂肪组织(PCAT)的改变密切相关。在最近的时代,PCAT的特征已逐渐成为CAD风险分层和个性化临床决策研究的重点。利用放射组学方法允许从这些脂肪沉积物中细致地提取成像特征。再加上机器学习范式,我们致力于建立主要不良心血管事件(MACE)发病的预测模型.
目的:评价冠状动脉CT血管造影(CCTA)的PCAT影像特征在预测MACE中的预测作用。
方法:我们回顾性地纳入了从2019年6月至2022年12月入住我们机构的314名疑似或确诊的CAD患者的数据。来自两个外部机构的242名患者的额外队列被纳入外部验证。考虑的终点是1年随访后MACE的发生。MACE被描述为心血管死亡率,新诊断的心肌梗死,因心力衰竭住院(或再次住院),和冠状动脉靶血管血运重建发生在CCTA检查后30天以上。所有入选患者均接受CCTA扫描。从CCTA图像的最佳舒张期轴向切片中精心提取了放射学特征。通过复合特征选择算法实现了特征约简,为放射学特征模型奠定基础。单变量和多变量分析均用于评估临床变量。多方面的逻辑回归分析有助于构建临床-放射学-放射学组合模型(或列线图)。接收器工作特性(ROC)曲线,校准,并描绘了决策曲线分析(DCA),计算ROC曲线下面积(AUC)来衡量临床模型的预测能力,放射学模型,和合成的合奏。
结果:确定了与MACE密切相关的12个放射学特征,以建立放射学模型。多因素logistic回归结果表明,吸烟,年龄,高血压,血脂异常与MACE显著相关。在综合列线图中,在临床上合并,成像,和放射学参数,诊断性能如下:0.970AUC,0.949精度(ACC),0.833灵敏度(SEN),0.981特异性(SPE),0.926阳性预测值(PPV),阴性预测值(NPV)为0.955。校准曲线表明列线图的一致性值得称赞,决策曲线分析强调了其优越的临床实用性。
结论:基于CCTA的PCAT放射学特征的整合,临床指标,和成像参数到列线图中是预测MACE事件的有前途的工具。
BACKGROUND: The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE).
OBJECTIVE: To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE.
METHODS: We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30 days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble.
RESULTS: A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (ACC), 0.833 sensitivity (SEN), 0.981 specificity (SPE), 0.926 positive predictive value (PPV), and 0.955 negative predictive value (NPV). The calibration curve indicated a commendable concordance of the nomogram, and the decision curve analysis underscored its superior clinical utility.
CONCLUSIONS: The integration of radiomic signatures from PCAT based on CCTA, clinical indices, and imaging parameters into a nomogram stands as a promising instrument for prognosticating MACE events.