基于基于机器学习的聚类分析的多个小（<7mm）颅内动脉瘤的破裂鉴别。
Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster analysis.
机构信息
Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, 119 Fanyang Road, Beijing, 100070, China.
National Key Clinical Specialty, Department of Neurosurgery, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
出版信息
BMC Neurol. 2023 Jan 28;23(1):45. doi: 10.1186/s12883-023-03088-8.
BACKGROUND
Small multiple intracranial aneurysms (SMIAs) are known to be more prone to rupture than are single aneurysms. However, specific recommendations for patients with small MIAs are not included in the guidelines of the American Heart Association and American Stroke Association. In this study, we aimed to evaluate the feasibility of machine learning-based cluster analysis for discriminating the risk of rupture of SMIAs.
METHODS
This multi-institutional cross-sectional study included 1,427 SMIAs from 660 patients. Hierarchical cluster analysis guided patient classification based on patient-level characteristics. Based on the clusters and morphological features, machine learning models were constructed and compared to screen the optimal model for discriminating aneurysm rupture.
RESULTS
Three clusters with markedly different features were identified. Cluster 1 (n = 45) had the highest risk of subarachnoid hemorrhage (SAH) (75.6%) and was characterized by a higher prevalence of familiar IAs. Cluster 2 (n = 110) had a moderate risk of SAH (38.2%) and was characterized by the highest rate of SAH history and highest number of vascular risk factors. Cluster 3 (n = 505) had a relatively mild risk of SAH (17.6%) and was characterized by a lower prevalence of SAH history and lower number of vascular risk factors. Lasso regression analysis showed that compared with cluster 3, clusters 1 (odds ratio [OR], 7.391; 95% confidence interval [CI], 4.074-13.150) and 2 (OR, 3.014; 95% CI, 1.827-4.970) were at a higher risk of aneurysm rupture. In terms of performance, the area under the curve of the model was 0.828 (95% CI, 0.770-0.833).
CONCLUSIONS
An unsupervised machine learning-based algorithm successfully identified three distinct clusters with different SAH risk in patients with SMIAs. Based on the morphological factors and identified clusters, our proposed model has good discrimination ability for SMIA ruptures.
背景
多发性颅内小动脉瘤（SMIA）比单发动脉瘤更容易破裂。然而，美国心脏协会和美国中风协会的指南中并未包含针对小 MIAs 患者的具体建议。在这项研究中，我们旨在评估基于机器学习的聚类分析在区分 SMIA 破裂风险方面的可行性。
方法
这项多机构的横断面研究纳入了来自 660 名患者的 1427 个 SMIA。基于患者特征的分层聚类分析指导患者分类。基于聚类和形态特征，构建了机器学习模型并进行比较，以筛选出用于区分动脉瘤破裂的最佳模型。
结果
确定了三个特征明显不同的聚类。聚类 1（n=45）蛛网膜下腔出血（SAH）风险最高（75.6%），其特征是家族性 IAs 患病率较高。聚类 2（n=110）SAH 风险中等（38.2%），其特征是 SAH 病史发生率最高，血管危险因素数量最多。聚类 3（n=505）SAH 风险相对较轻（17.6%），其特征是 SAH 病史发生率较低，血管危险因素数量较少。Lasso 回归分析显示，与聚类 3 相比，聚类 1（比值比 [OR]，7.391；95%置信区间 [CI]，4.074-13.150）和 2（OR，3.014；95% CI，1.827-4.970）的动脉瘤破裂风险更高。在性能方面，该模型的曲线下面积为 0.828（95%CI，0.770-0.833）。
结论
基于无监督机器学习的算法成功识别了具有不同 SAH 风险的三个不同聚类。基于形态学因素和识别的聚类，我们提出的模型对 SMIA 破裂具有良好的判别能力。
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