ArticleStroke2018
Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms.
Article in Stroke, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Comparative analysis of the geometric morphology and local hemodynamics of small and medium intracranial aneurysms: a two-center retrospective study in a Chinese Han population.Quantitative imaging in medicine and surgery · 2025Article
- Machine Learning-based Prediction of Temporal Velocity-Informatics (TVI) Variables for Accelerated Characterization of Intracranial Aneurysms' Rupture Status.Journal of cardiovascular translational research · 2025Article
- Haemodynamic characteristics of thin-walled regions in intracranial aneurysms: intraoperative imaging and CFD analysis.Acta neurochirurgica · 2025Article
- Prevalence, Geometry, and Hemodynamics of Small and Medium-Sized Intracranial Aneurysms With and Without Blebs in the Chinese Han Population.Journal of central nervous system disease · 2025Article
- Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model.Heliyon · 2024Article
- C5a-C5AR1 axis as a potential trigger of the rupture of intracranial aneurysms.Scientific reports · 2024Article
- Dynamic evaluation of unruptured intracranial aneurysms by 4D-CT angiography: comparison with digital subtraction angiography (DSA) and surgical findings.BMC medical imaging · 2023Article
- Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience.Journal of cardiovascular translational research · 2023Article
- Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster analysis.BMC neurology · 2023Article
- Endothelial Responses to Curvature-Induced Flow Patterns in Engineered Cerebral Aneurysms.Journal of biomechanical engineering · 2023Article
- Effect of FKBP12-Derived Intracellular Peptides on Rapamycin-Induced FKBP-FRB Interaction and Autophagy.Cells · 2022Article
- Irregular Pulsation of Intracranial Aneurysm Detected by Four-Dimensional CT Angiography and Associated With Small Aneurysm Rupture: A Single-Center Prospective Analysis.Frontiers in neurology · 2022Article
- Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling.Frontiers in neurology · 2022Article
- An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs.Frontiers in neurology · 2022Article
- Quantitative analysis of flow vortices: differentiation of unruptured and ruptured medium-sized middle cerebral artery aneurysms.Acta neurochirurgica · 2021Article
- Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification.Scientific reports · 2021Article
- A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms.European radiology · 2021Article
- Machine Learning-Based Prediction of Small Intracranial Aneurysm Rupture Status Using CTA-Derived Hemodynamics: A Multicenter Study.AJNR. American journal of neuroradiology · 2021Article
- A Simple Flow Classification Parameter Can Discriminate Rupture Status in Intracranial Aneurysms.Neurosurgery · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
background and purposeMany ruptured intracranial aneurysms (IAs) are small. Clinical presentations suggest that small and large IAs could have different phenotypes. It is unknown if small and large IAs have different characteristics that discriminate rupture.
methodsWe analyzed morphological, hemodynamic, and clinical parameters of 413 retrospectively collected IAs (training cohort; 102 ruptured IAs). Hierarchal cluster analysis was performed to determine a size cutoff to dichotomize the IA population into small and large IAs. We applied multivariate logistic regression to build rupture discrimination models for small IAs, large IAs, and an aggregation of all IAs. We validated the ability of these 3 models to predict rupture status in a second, independently collected cohort of 129 IAs (testing cohort; 14 ruptured IAs).
resultsHierarchal cluster analysis in the training cohort confirmed that small and large IAs are best separated at 5 mm based on morphological and hemodynamic features (area under the curve=0.81). For small IAs (<5 mm), the resulting rupture discrimination model included undulation index, oscillatory shear index, previous subarachnoid hemorrhage, and absence of multiple IAs (area under the curve=0.84; 95% confidence interval, 0.78-0.88), whereas for large IAs (≥5 mm), the model included undulation index, low wall shear stress, previous subarachnoid hemorrhage, and IA location (area under the curve=0.87; 95% confidence interval, 0.82-0.93). The model for the aggregated training cohort retained all the parameters in the size-dichotomized models. Results in the testing cohort showed that the size-dichotomized rupture discrimination model had higher sensitivity (64% versus 29%) and accuracy (77% versus 74%), marginally higher area under the curve (0.75; 95% confidence interval, 0.61-0.88 versus 0.67; 95% confidence interval, 0.52-0.82), and similar specificity (78% versus 80%) compared with the aggregate-based model.
conclusionsSmall (<5 mm) and large (≥5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.
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