Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster analysis
Xin Tong,
Xin Feng,
Fei Peng,
Hao Niu,
Xin Zhang,
Xifeng Li,
Yuanli Zhao,
Aihua Liu,
Chuanzhi Duan
Affiliations
Xin Tong
Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases
Xin Feng
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
Fei Peng
Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases
Hao Niu
Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases
Xin Zhang
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
Xifeng Li
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
Yuanli Zhao
Department of Neurosurgery, Peking University International Hospital
Aihua Liu
Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases
Chuanzhi Duan
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
Abstract 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.
