Suppr超能文献

使用冠状动脉计算机断层扫描血管造影术鉴别非ST段抬高型心肌梗死与不稳定型心绞痛:影像特征和冠状动脉周围脂肪组织放射组学的作用

Differentiation of non-ST-segment elevation myocardial infarction from unstable angina using coronary computed tomography angiography: the role of imaging features and pericoronary adipose tissue radiomics.

作者信息

Lu Yang, Wang Qing, Liu Haifeng, Liu Qi, Wang Siqi, Xing Wei

机构信息

Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.

出版信息

Cardiol J. 2025;32(3):291-300. doi: 10.5603/cj.98559. Epub 2025 May 13.

Abstract

BACKGROUND

To ascertain the diagnostic value of radiomic features of pericoronary adipose tissue (PCAT) and other coronary computed tomography angiography (CCTA) parameters for differentiating non-ST-segment-elevation myocardial infarction (NSTEMI) from unstable angina (UA).

METHODS

This study included NSTEMI and UA patients (n = 102 each). The radiomic features of PCAT were selected according to the intraclass correlation coefficient, Pearson's coefficient, the t test, and least absolute shrinkage and selection operator. Six classifiers-random forest, support vector machine, naive Bayes, K-nearest neighbors, extreme gradient boosting, and light gradient boosting machine (LightGBM)-were used to build radiomics models, and the best were selected. Four CCTA parameter models, encapsulating plaque parameters (model 1), plaque parameters + fatty attenuation index (FAI) (model 2), plaque parameters + CT fractional flow reserve (CT-FFR) (model 3), and plaque parameters + CT-FFR + FAI (model 4), were constructed. Finally, we established a fusion model (nomogram) with all CCTA parameters and radiomics model scores. All models were compared regarding their performance.

RESULTS

The LightGBM radiomics model achieved the highest AUC. Among CCTA parameter models, only model 4 achieved a predictive performance similar to that of the radiomics model in the training and test cohorts (AUC = 0.904 vs. 0.898 and 0.860 vs. 0.877). The combined model (nomogram) showed greater predictive efficacy (AUC = 0.963, 0.910) than model 4 or the radiomics model.

CONCLUSION

The PCAT-based radiomics model accurately distinguishes between NSTEMI and UA, with similar diagnostic performance as the model that combined all the significant CCTA parameters. The nomogram integrating CCTA parameters and the radiomic score has good clinical application prospects.

摘要

背景

确定冠状动脉周围脂肪组织(PCAT)的放射组学特征及其他冠状动脉计算机断层扫描血管造影(CCTA)参数对非ST段抬高型心肌梗死(NSTEMI)与不稳定型心绞痛(UA)的鉴别诊断价值。

方法

本研究纳入NSTEMI和UA患者(各102例)。根据组内相关系数、Pearson系数、t检验以及最小绝对收缩和选择算子选择PCAT的放射组学特征。使用六种分类器——随机森林、支持向量机、朴素贝叶斯、K近邻、极端梯度提升和轻梯度提升机(LightGBM)——构建放射组学模型,并选出最佳模型。构建四个CCTA参数模型,分别为包含斑块参数的模型(模型1)、斑块参数+脂肪衰减指数(FAI)的模型(模型2)、斑块参数+CT血流储备分数(CT-FFR)的模型(模型3)以及斑块参数+CT-FFR+FAI的模型(模型4)。最后,我们建立了一个包含所有CCTA参数和放射组学模型评分的融合模型(列线图)。比较所有模型的性能。

结果

LightGBM放射组学模型的AUC最高。在CCTA参数模型中,只有模型4在训练队列和测试队列中的预测性能与放射组学模型相似(AUC分别为0.904对0.898以及0.860对0.877)。联合模型(列线图)显示出比模型4或放射组学模型更高的预测效能(AUC分别为0.963、0.910)。

结论

基于PCAT的放射组学模型能够准确区分NSTEMI和UA,其诊断性能与整合所有重要CCTA参数的模型相似。整合CCTA参数和放射组学评分的列线图具有良好的临床应用前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fdab/12221326/042c873db15e/cardj-32-3-291f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验