基于双能量CT参数结合临床特征的列线图识别症状性颈动脉斑块  

Nomogram based on dual-energy CT parameters and clinical characteristics for identifying symptomatic carotid artery plaques

作　　者：朱昊 林桂涵 陈炜越 陈敏江[1] 纪建松[1] ZHU Hao;LIN Guihan;CHEN Weiyue;CHEN Minjiang;JI Jiansong(Department of Radiology,Zhejiang Key Laboratory of Imaging and Interventional Medicine,the Fifth Affiliated Hospital of Wenzhou Medical University,Lishui Central Hospital,Lishui 323000,China)

机构地区：[1]温州医科大学附属第五医院(丽水市中心医院)放射科全省影像与介入医学重点实验室,浙江丽水323000

出　　处：《温州医科大学学报》2025年第3期179-187,共9页Journal of Wenzhou Medical University

基　　金：浙江省教育厅一般科研项目(Y202352926)。

摘　　要：目的:探讨基于双能量CT参数结合临床特征的列线图在识别症状性颈动脉斑块中的应用价值。方法:回顾性收集2019年1月至2023年5月在丽水市中心医院诊治的170例颈动脉粥样硬化斑块伴颈动脉狭窄患者的数据。病例按7:3的比例随机分为训练集(119例)和验证集(51例),并根据颈动脉供血区是否发生短暂性脑缺血或急性缺血性卒中,将患者分为有症状组和无症状组,收集其一般资料和CT血管造影(CTA)图像特征。同时,测量并计算斑块在不同能量下的CT值、碘浓度(IC)、脂肪分数、有效原子序数(Zeff)以及能谱曲线斜率(λHu)和标准化碘浓度(NIC)。通过单因素和多因素分析,筛选出症状性颈动脉斑块的独立预测因素并构建列线图模型,进而评估模型的预测效能和临床价值。结果:训练集中临床和CTA特征(高密度脂蛋白、高脂血症、斑块溃疡、钙化评分和狭窄程度)以及双能量CT参数(NIC、脂肪分数、Zeff、CT40 keV、CT50 keV、CT60 keV和λHu)在两组间差异均有统计学意义(均P<0.05)。多因素Logistic回归分析结果显示斑块溃疡、狭窄程度、高脂血症、NIC、Zeff和λHu是出现症状性颈动脉斑块的独立预测因素(均P<0.05)。列线图模型在训练集和验证集中的AUC分别为0.892和0.870,显著优于单一指标均表现出良好的预测效能(均P<0.05)。校正曲线表明样本的实际值及预测概率间具有较高的一致性,同时决策曲线分析显示列线图模型具有较高的临床应用前景。结论:双能量CT多个参数均有助于识别症状性颈动脉斑块,结合临床和CTA图像特征建立的列线图模型可进一步提升诊断效能。Objective:To explore the application value of a nomogram combining dual-energy CT parameters and clinical characteristics for the identification of symptomatic carotid artery plaques.Methods:Data of 170 patients with carotid atherosclerotic plaques accompanied by carotid artery stenosis treated at the Fifth Affiliated Hospital of Wenzhou Medical University from January 2019 to May 2023 were retrospectively collected.They were randomly divided into a training set(119 cases)and a validation set(51 cases)at a ratio of 7:3.Patients were further categorized into symptomatic and asymptomatic groups based on the occurrence of transient ischemic attack or acute ischemic stroke in the carotid artery supply area,and their general information and computed tomography angiography(CTA)imaging features were collected.Additionally,the CT values,iodine concentration(IC),fat fraction,effective atomic number(Zeff),spectral curve slope(λHu),and normalized iodine concentration(NIC)of the plaques were measured and calculated under different energy levels.Through univariate and multivariate analyses,independent predictors of symptomatic carotid plaques were identified and used to construct a nomogram model,whose predictive performance and clinical value were then assessed.Results:In the training set,clinical and CTA features(high-density lipoprotein,hyperlipidemia,plaque ulceration,degree of stenosis,and calcification score)and dual-energy CT parameters(NIC,fat fraction,Zeff,CT40 keV,CT50 keV,CT60 keV,andλHu)exhibited statistically significant differences between the two groups(all P<0.05).Multivariate Logistic regression analysis indicated that plaque ulceration,degree of stenosis,hyperlipidemia,NIC,Zeff,andλHu were independent predictors for the occurrence of symptomatic carotid artery plaques(all P<0.05).The nomogram model attained AUC values of 0.892 and 0.870 in the training and validation datasets,respectively,exhibiting a substantial advantages over individual indicators and showcasing robust predictive capabilities(all P<0.0

关 键 词：颈动脉斑块 缺血性脑卒中 短暂性脑缺血发作 双能量计算机断层扫描血管造影 

分 类 号：R743.3[医药卫生—神经病学与精神病学]