Related Experiment Video
Updated: Aug 12, 2025
Minimally Invasive Thumb-sized Pterional Craniotomy for Surgical Clip Ligation of Unruptured Anterior Circulation Aneurysms
Published on: August 11, 2015
Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster
Xin Tong1, Xin Feng2, Fei Peng1
1Department 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.
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.
Insights
Machine learning identified three risk groups for small multiple intracranial aneurysms (SMIAs), aiding in rupture risk assessment. This approach helps stratify patients with SMIAs for better management strategies.
Area of Science:
- Neurosurgery
- Medical Informatics
- Radiology
Background:
- Small multiple intracranial aneurysms (SMIAs) present a higher rupture risk than single aneurysms.
- Current guidelines lack specific recommendations for managing SMIAs.
- Risk stratification for SMIA rupture is crucial for patient management.
Purpose of the Study:
- To evaluate the feasibility of machine learning-based cluster analysis for discriminating SMIA rupture risk.
- To identify distinct patient clusters based on morphological and clinical features.
- To develop a predictive model for SMIA rupture.
Main Methods:
- A multi-institutional cross-sectional study involving 1,427 SMIAs from 660 patients.
- Hierarchical cluster analysis was used for patient classification.
- Machine learning models were constructed and compared using morphological features and identified clusters.
Main Results:
- Three distinct clusters with varying subarachnoid hemorrhage (SAH) risks were identified.
- Cluster 1 showed the highest SAH risk (75.6%) with a higher prevalence of familial aneurysms.
- Cluster 2 had a moderate SAH risk (38.2%) and more vascular risk factors; Cluster 3 had a lower risk (17.6%).
- Lasso regression indicated significantly higher rupture risk in Cluster 1 (OR 7.391) and Cluster 2 (OR 3.014) compared to Cluster 3.
- The developed model achieved an area under the curve of 0.828 for discriminating SMIA rupture.
Conclusions:
- Unsupervised machine learning effectively identified three clusters with differential SAH risk in SMIA patients.
- The proposed model, utilizing morphological factors and identified clusters, demonstrates good discrimination ability for SMIA ruptures.
- This approach offers a promising tool for risk stratification and management of SMIAs.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
04:25Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
Published on: December 15, 2023
