Evidence map›Paper›PMID 40237244›Full record

ArticleCNS neuroscience & therapeutics2025

Prediction and SHAP Analysis Integrating Morphological and Hemodynamic Parameters for Unruptured Intracranial Aneurysm Occlusion After Flow Diverter Treatment.

Hongchen Zhang, Chuanhao Lu, Zhen Hu, Deyu Sun, Liang Li, Hongxing Wu, Hua Lu, Bin Lv, Jun Wang, Shuhui Dai and 1 more

Abstract read
In one paragraph

Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Hongchen ZhangDepartment of Neurosurgery, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
Chuanhao LuDepartment of Neurosurgery, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
Zhen HuInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Deyu SunDavid Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Liang LiDepartment of Neurosurgery, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
Hongxing WuDepartment of Neurosurgery, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
Hua LuDepartment of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Bin LvDepartment of Neurology, The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
Jun WangDepartment of Neurology, The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
Shuhui DaiDepartment of Neurosurgery, Xijing 986 Hospital, The Fourth Military Medical University, Xi'an, China.
Xia LiDepartment of Neurosurgery, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.ORCID 0000-0003-2679-9917

Funding

National Natural Science Foundation of China 81974188National Natural Science Foundation of China 82371337National Natural Science Foundation of China 82401531Research Fund for Young Star of Science and Technology in Shaanxi Province 2023KJXX-025Shaanxi Creative Talents Promotion Plan-Technological Innovation Team 2022TD-42Shaanxi International Scientific and Technological Cooperation Project 2023-GHZD-21Xijing Hospital Medical Staff Training Boost Special Plan XJZT25CX34
6 · The paper itself

Abstract

backgroundAlthough most unruptured intracranial aneurysms (UIAs) have good prognosis after flow diverter (FD) treatment, some remain unoccluded for extended periods, posing a persistent rupture risk. This study aims to develop a predictive model for UIA occlusion after FD treatment through integrating morphological and hemodynamic parameters, which may be critical for personalized postoperative management.

methodsData from patients with single UIAs treated with stand-alone FD were collected from June 2018 to December 2022 in four cerebrovascular disease centers. Morphological parameters were obtained from 3D reconstructed aneurysm models, and hemodynamic parameters were derived by computational fluid dynamics (CFD) analysis. A predictive model for aneurysm occlusion was constructed using various machine learning algorithms, including logistic regression, Random Forest, XGBoost, and K-Nearest Neighbors. Model performances were evaluated through repeated cross-validation, 0.632 bootstrap, and 0.632+ bootstrap. Shapley additive explanation (SHAP) analysis was employed to assess the contribution of each parameter to UIA occlusion.

resultsSeventy-nine patients were reviewed; a total of 51 cases met the criteria, with an average age of 53.9 ± 9.9 years. The average aneurysm diameter was 3.72 ± 2.72 mm, comprising 29 occlusions and 22 non-occlusions. Five variables were selected for further modeling, including follow-up time > 6 months, aneurysm rupture ratio (ArR), occlusion ratio (OsR), parent artery wall shear stress (WSS), and the change of parent artery WSS. Logistic regression outperformed other algorithms, achieving an area under the curve (AUC) above 0.75, indicating good predictive performance. SHAP analysis revealed that the change of parent artery WSS contributed most significantly to accurate and early prediction. Additionally, a web application software was developed to assist clinicians in real-time aneurysm occlusion prediction.

conclusionsThis study developed a robust predictive model for UIA occlusion following FD treatment by integrating morphological and hemodynamic parameters, which may provide potentially valuable decision-making support for optimizing treatment strategies.

Indexed as

HemodynamicsIntracranial AneurysmAdultAgedFemaleHumansMachine LearningMaleMiddle AgedRetrospective StudiesTreatment Outcomeaneurysm occlusioncomputational fluid dynamicsflow diverterhemodynamicintracranial aneurysmmorphology

Identifiers

PMID40237244
PMCPMC12001072

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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 OpenQuestion graph.