Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations–based supervised clustering: a novel approach to rupture prediction

OBJECTIVE The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) analyses by applying a supervised clustering framework to identify distinct aneurysm phenotypes and improve rupture risk prediction. METHODS Geometric and morphological data for 103 cerebral aneurysms were obtained from the AneuriskWeb dataset. To segment the cerebral aneurysm data into information-dense clusters that relate to aneurysm rupture risk, the authors trained an Extreme Gradient Boosting model for Shapley Additive Explanations (SHAP)–based feature attribution followed by nonlinear dimensionality reduction. Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN) was then used on the SHAP-transformed feature space to identify clusters that were, subsequently, interpreted directly using rule-based machine learning and indirectly with phenotype visualization. RESULTS The initial SHAP analysis identified the parent vessel diameter, neck vessel angle, and the cross-sectional area along the centerline of the sac as the most significant predictors of rupture risk. Clustering revealed three distinct aneurysm phenotypes with a high degree of separation (Silhouette score = 0.915). Cluster α, characterized by parent vessel diameters > 3.08 mm and elongated geometries, was a low-risk phenotype with a 4.16% rupture rate. Cluster β only included ruptured aneurysms, with vessel diameters ≤ 1.65 mm and nonspherical structures. Cluster γ represented a mixed-risk aneurysm phenotype (rupture rate of 45.45%), with intermedial vessel diameters (range 1.65–3.08 mm); acute neck angles (< 90°) increased the rupture rate within this cluster. CONCLUSIONS The supervised clustering identified distinct cerebral aneurysm phenotypes, balancing granularity with interpretability in CFD data analysis. Future studies should build on these phenotype-driven insights with temporal analyses and larger datasets for validation, as well as an end-to-end framework to enhance scalability.