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.