ArticleFrontiers in neurology2024
Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Mechanisms of Intracranial Aneurysm Rupture: An Integrative Review of Experimental and Clinical Evidence.Journal of clinical medicine · 2025Review
- Development and validation of a nomogram for predicting early rupture and rebleeding risk after intracranial aneurysm embolization.Frontiers in neurologyArticle
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Authors and funding
6 authors.
Funding
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Abstract
Objective: This study aimed to develop and validate a multivariate logistic regression model for predicting intracranial aneurysm (IA) rupture by integrating clinical data, aneurysm morphology, and parent artery characteristics using high-resolution vessel wall imaging (HR-VWI). Methods: A retrospective analysis was conducted on 298 patients with 386 aneurysms. Patients were randomly divided into training ( Results: The logistic regression model demonstrated superior predictive performance with an area under the curve of 0.814, which was significantly higher than PHASES and ELAPSS scores ( Conclusion: The multivariate model based on HR-VWI, which incorporates aneurysm and parent artery features, provides a more accurate prediction of IA rupture risk than conventional scoring systems, offering a valuable tool for clinical decision-making.
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