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Purpose <p>To assess the associations between cerebral vessels morphological features, white matter hyperintensities (WMHs), and the one-year risk of ischemic stroke recurrence.</p> Methods <p>A total of 677 patients diagnosed with acute ischemic stroke from January 2018 to April 2021 were consecutively enrolled. Head computed tomography angiography (CTA) and magnetic resonance imaging including fluid-attenuated inversion recovery (FLAIR), were obtained on admission. Cerebral vessels morphological features such as volume, length, radius, density, tortuosity, branch complexity, and degree of stenosis were extracted and calculated using CTA data. Additionally, automated segmentation was employed for delineating WMHs lesions based on FLAIR images. By incorporating clinical characteristics, six predictive models were developed using Cox proportional hazards analysis to estimate the one-year risk of stroke recurrence. The performance of these models was evaluated by comparing the concordance index (C-index).</p> Results <p>The study found significant associations between the lack of antiplatelet therapy at discharge, reduced length and branching of cerebral vessels, and increased burden of WMHs, with a higher one-year risk of recurrent ischemic stroke (all <i>P</i> &lt; 0.05). The integrated model demonstrated superior prognostic capability (C-index: 0.750; 95% CI: 0.684–0.817), outperforming models based solely on clinical characteristics (C-index: 0.636; 95% CI: 0.555–0.717), cerebral vessels morphology (C-index: 0.601; 95% CI: 0.526–0.676), and WMHs burden (C-index: 0.680; 95% CI: 0.603–0.757).</p> Conclusion <p>The quantitative assessment of cerebral vessels morphological features and WMHs provides a promising neuroimaging tool for estimating the one-year risk of ischemic stroke recurrence. The incorporation of cerebral vessels morphological features enhances the predictive accuracy.</p>

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The impact of cerebral vessels morphological alteration and white matter hyperintensities burden on the one-year risk of ischemic stroke recurrence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月5日

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  • Hao Wang,
  • Guoqing Wu,
  • Bei Wang,
  • Ying Liu,
  • Lan Zheng,
  • He Wang,
  • Jing Ding

摘要

Purpose

To assess the associations between cerebral vessels morphological features, white matter hyperintensities (WMHs), and the one-year risk of ischemic stroke recurrence.

Methods

A total of 677 patients diagnosed with acute ischemic stroke from January 2018 to April 2021 were consecutively enrolled. Head computed tomography angiography (CTA) and magnetic resonance imaging including fluid-attenuated inversion recovery (FLAIR), were obtained on admission. Cerebral vessels morphological features such as volume, length, radius, density, tortuosity, branch complexity, and degree of stenosis were extracted and calculated using CTA data. Additionally, automated segmentation was employed for delineating WMHs lesions based on FLAIR images. By incorporating clinical characteristics, six predictive models were developed using Cox proportional hazards analysis to estimate the one-year risk of stroke recurrence. The performance of these models was evaluated by comparing the concordance index (C-index).

Results

The study found significant associations between the lack of antiplatelet therapy at discharge, reduced length and branching of cerebral vessels, and increased burden of WMHs, with a higher one-year risk of recurrent ischemic stroke (all P < 0.05). The integrated model demonstrated superior prognostic capability (C-index: 0.750; 95% CI: 0.684–0.817), outperforming models based solely on clinical characteristics (C-index: 0.636; 95% CI: 0.555–0.717), cerebral vessels morphology (C-index: 0.601; 95% CI: 0.526–0.676), and WMHs burden (C-index: 0.680; 95% CI: 0.603–0.757).

Conclusion

The quantitative assessment of cerebral vessels morphological features and WMHs provides a promising neuroimaging tool for estimating the one-year risk of ischemic stroke recurrence. The incorporation of cerebral vessels morphological features enhances the predictive accuracy.