Combining Clinical Characteristics With CTA Radiomics for Predicting Intracranial Aneurysm Rupture Status

Intracranial aneurysms (IAs) are clinically categorized as ruptured or unruptured. Size variability complicates precise segmentation and rupture assessment. This study integrates deep learning, machine learning, clinical characteristics, and computed tomography angiography (CTA) radiomics to determine IA rupture status. A dataset of 443 aneurysms (101 unruptured and 342 ruptured) was curated from affiliated hospitals. IAs were segmented via Swin UNETR, with radiomic features extracted via PyRadiomics. Following dimensionality reduction, five classifiers, support vector machine (SVM), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and voting classifier, were evaluated via the area under the receiver operating characteristic curve (AUC‐ROC). Among the 1074 radiomic features, 25 were significantly correlated with rupture status. The RF, voting, MLP, LR, and SVM classifiers achieved AUCs of 0.94, 0.93, 0.88, 0.88, and 0.84, respectively. These results demonstrate the discriminative power of the combined t‐test, LASSO, and PCA feature selection methods.