Rupture Risk Assessment for Anterior Communicating Artery Aneurysms Using Decision Tree Modeling

Frontiers in Cardiovascular Medicine

Jieqing Wan¹  Bing Zhao¹  Yongchun Chen¹  Yaohua Pan¹  Jinjin Liu¹  Yunjun Yang²  Haixia Xing³  Jiafeng Zhou⁴  Boli Lin⁴ 

[1] Department of Neurosurgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China;Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Pathology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China;Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;

关键词: intracranial aneurysm;    anterior communicating artery aneurysm;    rupture risk;    decision tree model;    machine learning;   

DOI  :  10.3389/fcvm.2022.900647

来源: DOAJ

【 摘 要 】

BackgroundAlthough anterior communicating artery (ACoA) aneurysms have a higher risk of rupture than aneurysms in other locations, whether to treat unruptured ACoA aneurysms incidentally found is a dilemma because of treatment-related complications. Machine learning models have been widely used in the prediction of clinical medicine. In this study, we aimed to develop an easy-to-use decision tree model to assess the rupture risk of ACoA aneurysms.MethodsThis is a retrospective analysis of rupture risk for patients with ACoA aneurysms from two medical centers. Morphologic parameters of these aneurysms were measured and evaluated. Univariate analysis and multivariate logistic regression analysis were performed to investigate the risk factors of aneurysm rupture. A decision tree model was developed to assess the rupture risk of ACoA aneurysms based on significant risk factors.ResultsIn this study, 285 patients were included, among which 67 had unruptured aneurysms and 218 had ruptured aneurysms. Aneurysm irregularity and vessel angle were independent predictors of rupture of ACoA aneurysms. There were five features, including size ratio, aneurysm irregularity, flow angle, vessel angle, and aneurysm size, selected for decision tree modeling. The model provided a visual representation of a decision tree and achieved a good prediction performance with an area under the receiver operating characteristic curve of 0.864 in the training dataset and 0.787 in the test dataset.ConclusionThe decision tree model is a simple tool to assess the rupture risk of ACoA aneurysms and may be considered for treatment decision-making of unruptured intracranial aneurysms.

【 授权许可】

Unknown