Liu Jinjin, Xing Haixia, Chen Yongchun, Lin Boli, Zhou Jiafeng, Wan Jieqing, Pan Yaohua, Yang Yunjun, Zhao Bing
Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Neurosurgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Cardiovasc Med. 2022 May 13;9:900647. doi: 10.3389/fcvm.2022.900647. eCollection 2022.
Although 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.
This 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.
In 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.
The 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.
尽管前交通动脉（ACoA）动脉瘤破裂风险高于其他部位的动脉瘤，但对于偶然发现的未破裂ACoA动脉瘤是否进行治疗是一个难题，因为存在与治疗相关的并发症。机器学习模型已广泛应用于临床医学预测。在本研究中，我们旨在开发一种易于使用的决策树模型来评估ACoA动脉瘤的破裂风险。
这是一项对来自两个医疗中心的ACoA动脉瘤患者破裂风险的回顾性分析。测量并评估了这些动脉瘤的形态学参数。进行单因素分析和多因素逻辑回归分析以研究动脉瘤破裂的危险因素。基于显著危险因素开发了一个决策树模型来评估ACoA动脉瘤的破裂风险。
本研究纳入了285例患者，其中67例为未破裂动脉瘤，218例为破裂动脉瘤。动脉瘤不规则性和血管角度是ACoA动脉瘤破裂的独立预测因素。为决策树建模选择了五个特征，包括大小比、动脉瘤不规则性、血流角度、血管角度和动脉瘤大小。该模型提供了决策树的可视化表示，在训练数据集的受试者操作特征曲线下面积为0.864，在测试数据集为0.787，具有良好的预测性能。
决策树模型是评估ACoA动脉瘤破裂风险的简单工具，可考虑用于未破裂颅内动脉瘤的治疗决策。
