Evidence map›Paper›PMID 42701484›Full record

ArticleQuantitative imaging in medicine and surgery2026

Local pressure as a dominant hemodynamic driver of wall enhancement in anterior communicating artery aneurysms: a facet-level computational fluid dynamics and vessel wall imaging analysis.

Zhangyu Pang, Chi Huang, Jingtao Ma, Hui Tang, Xiaojing Guo, Yao Zhang, Kaiyan Tan, Linhan Yan, Zhengjie Fang, Qian Wu and 3 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Zhangyu Pang *School of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Chi Huang *Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, Engineering Research Center of Diagnostic and Therapeutic Technology and Devices for Cerebrovascular Diseases in Ministry of Education, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital Institute for Brain Science and Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Jingtao MaSchool of Engineering and Technology, University of New South Wales, Canberra, Australia.
Hui TangDepartment of Neurosurgery, The First People's Hospital of Neijiang, Neijiang, China.
Xiaojing GuoSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Yao ZhangSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Kaiyan TanSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Linhan YanSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Zhengjie FangSecond Clinical Medical College, Southern Medical University, Guangzhou, China.
Qian WuSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Yu FuSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Xin FengNeurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, Engineering Research Center of Diagnostic and Therapeutic Technology and Devices for Cerebrovascular Diseases in Ministry of Education, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital Institute for Brain Science and Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Yuqian MeiSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.ORCID https://orcid.org/0000-0003-3245-3867

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Size-based risk stratification often overlooks small but unstable intracranial aneurysms (IAs). Aneurysm wall enhancement (AWE) on vessel wall imaging (VWI) is a validated marker of wall instability, yet the local hemodynamic drivers of this pathology, particularly in complex anterior communicating artery (ACoA) aneurysms, remain incompletely characterized. This study leverages a combined computational fluid dynamics (CFD)-VWI approach to characterize the mechanobiological coupling between local hemodynamics and quantitative wall remodeling in ACoA aneurysms. Methods: We retrospectively analyzed 24 patients harboring 25 ACoA aneurysms. A Vector-Integrated Surface Parametrization (VISP) pipeline achieved sub-voxel sampling density [through adaptive interpolation rather than imaging resolution beyond the native 0.6 mm magnetic resonance imaging (MRI) voxel] for co-registration of CFD and 3T-VWI, with wall enhancement defined at a contrast ratio (CR) ≥0.6. To identify hemodynamic drivers of enhanced wall thickness (EWT) while explicitly accounting for within-patient hierarchical clustering, four complementary analytical frameworks were applied in parallel: (I) intra-patient paired bootstrap tests (2,000 resamples) comparing enhanced and non-enhanced wall segments within each of the 13 AWE-positive patients; (II) a multivariate linear mixed model (LMM) with patient-level random intercepts for EWT severity (n=12,473 enhanced segments); (III) generalized estimating equations (GEEs) with cluster-robust variance for AWE presence (n=157,284 segments); and (IV) ensemble machine-learning models (Random Forest and XGBoost) interpreted via Shapley Additive exPlanations (SHAP) values across segment-level, patient-centered, and patient-level GroupKFold cross-validation (CV). Cross-patient generalization of EWT prediction was disclosed separately as an out-of-sample analysis. Results: Focal AWE was identified in 14 of 25 aneurysms (13 patients), spatially coinciding with hemodynamic stagnation zones. Enhanced segments exhibited significantly lower local Pressure Conclusions: We present a facet-level CFD-VWI pipeline that achieves sub-voxel sampling density for spatially mapping local hemodynamics onto quantitative wall remodeling in ACoA aneurysms on a clinical 3T platform. Across four independent hierarchical analyses, local Pressure

Indexed as

hemodynamicsIntracranial aneurysm (IA)machine learningvessel wall imaging (VWI)wall enhancement

Identifiers

PMID42701484
PMCPMC13545608

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.