Interpretable model using serum biomarkers and intra-/peri-aneurysmal CTA radiomics for discriminating ruptured from unruptured intracranial aneurysms: an external validation retrospective study.

Objective: To develop an interpretable model that integrates diverse machine learning algorithms, fusion strategies, intra-/perianeurysmal CTA radiomics, and serum biomarkers to differentiate ruptured from unruptured intracranial aneurysms (IAs), and to investigate the underlying biological features associated with rupture status. Methods: Totally, 744 IAs patients who underwent CTA were retrospectively enrolled. Data from Center 1 ( Results: Among the 11 models, LRSLM exhibited robust performance in the external validation set, achieving an AUC of 0.9177 with good calibration. It also demonstrated high accuracy (0.8847), recall (0.8171), and a low Brier score (0.0999), alongside greater clinical net benefit across a wide range of threshold probabilities (10-85%). Correlation analyses revealed strong positive associations between intra-aneurysmal and peri-aneurysmal radiomic features (Spearman's Conclusion: The LRSLM model achieves robust discriminative performance for differentiating ruptured from unruptured IAs. SHAP analysis enhances the interpretability of the model and may assist in identifying high-risk phenotypic features, although prospective validation is required before predictive clinical application can be considered.