Acute stroke risk prediction model based on dual-energy CTA-derived carotid plaque, perivascular adipose tissue characteristics, and serum lipid parameters: a dual-center study.

Background: 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, peri...

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Publicado en:Neuroradiology Vol. 67; no. 11; pp. 3157 - 3172
Autores principales: Zhang, He, Xu, Xu, Long, Juan, Wang, Chenzi, Liu, Xiaohan, Xu, Wenbei, Sun, Xiaonan, Dou, Peipei, Zhou, Dexing, Cao, Wei, Xu, Kai, Meng, Yankai
Formato: research tables/charts Journal Article
Publicado: Springer Nature Nov2025
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Nov2025
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      pub: Springer Nature
      place: New York, New York
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        atl: Acute stroke risk prediction model based on dual-energy CTA-derived carotid plaque, perivascular adipose tissue characteristics, and serum lipid parameters: a dual-center study.
      aug:
        au:
          Zhang, He
          Xu, Xu
          Long, Juan
          Wang, Chenzi
          Liu, Xiaohan
          Xu, Wenbei
          Sun, Xiaonan
          Dou, Peipei
          Zhou, Dexing
          Cao, Wei
          Xu, Kai
          Meng, Yankai
        affil: https://ror.org/02kstas42 Department of Medical Imaging, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
      sug:
        subj:
          Stroke Risk Factors
          Risk Assessment
          Prediction Models
          Adipose Tissue Radiography
          Biological Markers Blood
          Carotid Stenosis Complications
          Computed Tomography Angiography
          Lipids Blood
          Coronary Arteriosclerosis Radiography
          Human
          Male
          Female
          Adult
          Middle Age
          Retrospective Design
          Predictive Value of Tests
          Sensitivity and Specificity
          Data Analysis Software
          Descriptive Statistics
          Chi Square Test
          Mann-Whitney U Test
          ROC Curve
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Male
          Female
      ab: Background: 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. Methods: 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. Results: 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. Conclusions: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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