Zhang He, Xu Xu, Long Juan, Wang Chenzi, Liu Xiaohan, Xu Wenbei, Sun Xiaonan, Dou Peipei, Zhou Dexing, Cao Wei, Xu Kai, Meng Yankai
Department of Medical Imaging, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
School of Medical Imaging, Xuzhou Medical University, Xuzhou, China.
Neuroradiology. 2025 Aug 7. doi: 10.1007/s00234-025-03723-w.
Acute stroke is a major global cause of mortality and disability. Accurate prediction of stroke risk is crucial for effective clinical management. This study aimed to develop a multidimensional prediction model for acute stroke using carotid plaque characteristics, lumen parameters, perivascular adipose tissue (PVAT) quantitative metrics derived from dual-energy computed tomography angiography (DE-CTA), and serum lipid biomarkers.
This retrospective dual-center study enrolled 212 patients who underwent DE-CTA and MRI between January 2023 and October 2024, comprising a training cohort (137 patients) and an external validation cohort (75 patients). Quantitative parameters including carotid plaque features (composition and intraplaque parameters), lumen metrics, PVAT quantitative indices, and serum lipid levels were collected. Patients with ipsilateral acute anterior circulation infarcts identified on MRI were classified as symptomatic (STA), and those without infarcts as asymptomatic (ATA). Variables were selected via univariate analysis and LASSO regression to construct a multivariate logistic regression model. Model performance was evaluated by ROC analysis, confusion matrix, calibration curves, and clinical decision curves, followed by external validation.
External validation of the final model showed an area under the ROC curve (AUC) of 0.810, with a sensitivity of 80.8% and specificity of 65.3%, indicating robust predictive performance and good clinical applicability.
The multidimensional predictive model integrating DE-CTA-derived carotid plaque features, PVAT metrics, and serum lipid parameters effectively predicts acute stroke risk, providing a reliable quantitative tool for early screening and clinical intervention.
急性中风是全球死亡和残疾的主要原因。准确预测中风风险对于有效的临床管理至关重要。本研究旨在利用颈动脉斑块特征、管腔参数、双能计算机断层血管造影（DE-CTA）得出的血管周围脂肪组织（PVAT）定量指标以及血清脂质生物标志物，开发一种用于急性中风的多维预测模型。
这项回顾性双中心研究纳入了2023年1月至2024年10月期间接受DE-CTA和MRI检查的212例患者，包括一个训练队列（137例患者）和一个外部验证队列（75例患者）。收集了包括颈动脉斑块特征（成分和斑块内参数）、管腔指标、PVAT定量指数和血清脂质水平在内的定量参数。MRI上确定为同侧急性前循环梗死的患者被分类为有症状（STA），无梗死的患者为无症状（ATA）。通过单变量分析和LASSO回归选择变量，以构建多变量逻辑回归模型。通过ROC分析、混淆矩阵、校准曲线和临床决策曲线评估模型性能，随后进行外部验证。
最终模型的外部验证显示，ROC曲线下面积（AUC）为0.810，灵敏度为80.8%，特异性为65.3%，表明具有强大的预测性能和良好的临床适用性。
整合DE-CTA得出的颈动脉斑块特征、PVAT指标和血清脂质参数的多维预测模型有效地预测了急性中风风险，为早期筛查和临床干预提供了可靠的定量工具。
