Multi-parameter coronary CT angiography features based on artificial intelligence combined with clinical indicators for predicting plaque progression
10.13929/j.issn.1003-3289.2025.09.011
- VernacularTitle:基于人工智能冠状动脉CT血管造影多参数特征联合临床指标预测斑块进展
- Author:
Ying MENG
1
;
Zhiyuan WANG
;
Ji ZHANG
;
Longshan SHEN
;
Zhenhuan WANG
;
Liucheng CHEN
Author Information
1. 蚌埠医科大学第一附属医院放射科,安徽 蚌埠 233004
- Publication Type:Journal Article
- Keywords:
coronary artery disease;
atherosclerosis;
disease progression;
computed tomography angiography;
artificial intelligence
- From:
Chinese Journal of Medical Imaging Technology
2025;41(9):1506-1511
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of artificial intelligence(AI)based multi-parameter coronary CT angiography(CCTA)features combined with clinical indicators for predicting coronary plaque progression.Methods Totally 143 coronary atherosclerosis(AS)patients were retrospectively enrolled and divided into progression group(arithmetic average annual growth rate of plaque load＞1％,n=73)and non-progression group(arithmetic average annual growth rate of plaque load＜1％,n=70).The baseline clinical data,CT-derived fractional flow reserve(CT-FFR),perivascular fat attenuation index(FAI),and quantitative plaque features were collected and compared between groups.For variables being statistically different between groups,those had collinearity with others were excluded,and then multivariable logistic regression was used to screen independent predictors of plaque progression from the retained variables,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Progression group had higher proportions of hypertension and diabetes,higher apolipoprotein A1(ApoA1)and high-sensitivity C-reactive protein(hs-CRP)levels but lower high-density lipoprotein cholesterol(HDL-C)levels than non-progression group(all P＜0.05).Progression group showed smaller minimum lumen area and lower CT-FFR,but greater degree of lumen stenosis,total plaque volume,plaque load,non-calcified plaque volume,lipid-rich plaque volume,fibrolipid plaque volume and FAI values than non-progression group(all P＜0.05).Plaque types were different between groups(P＜0.05).Diabetes,low HDL-C,small minimum lumen area and large lipid-rich plaque volume were all independent predictors of plaque progression in patients with coronary AS(all P＜0.05),and the AUC of the combined model for predicting plaque progression was 0.859.Conclusion Multi-parameter CCTA features based on AI combined with clinical indicators could be used to effectively predict progression of coronary AS plaque.
