Background/Objectives: Recent studies have increasingly focused on the morphological characteristics of surrounding arteries as rupture predictors, particularly because these vessel configurations remain stable before and after aneurysm rupture, providing a reliable anatomical substrate for risk assessment. This study aimed to identify independent predictors of rupture by evaluating both aneurysmal and internal carotid artery (ICA) morphological characteristics. Methods: We retrospectively analyzed imaging data from 64 patients with posterior communicating artery (PcomA) aneurysms who underwent treatment at a single tertiary center between 2018 and 2022, including 25 ruptured aneurysms (39.1%). Only treated aneurysms were included to ensure the availability of high-quality pre-treatment digital subtraction angiography (DSA) suitable for three-dimensional (3D) reconstruction and centerline-based analysis. Seventeen aneurysm morphological parameters and thirteen ICA-related parameters were measured. Because time-to-event data were not available, logistic regression analysis was performed with rupture status as the outcome variable. Receiver operating characteristic (ROC) curve analyses were conducted to evaluate discriminative performance. Results: Multivariate logistic regression revealed that three ICA-associated factors—the tortuosity of the communicating ICA segment (Tcco), the ICA cross-sectional area at the PcomA origin (Pcs), and the angle between the ICA and PcomA (θ2)—were independently associated with rupture. Among aneurysm-related factors, Maximum 3D Diameter remained significantly related to rupture risk. ROC analyses demonstrated that Maximum 3D Diameter had the highest discriminative value (AUC 0.779; cut-off 7.805 mm), followed by Pcs, Tcco, and θ2. Conclusions: Both aneurysm morphology and the anatomical configuration of surrounding arteries significantly contribute to rupture risk in PcomA aneurysms. Incorporating parent-vessel morphological features into rupture-risk assessment may enhance patient-specific decision-making.
Nahm et al. (Thu,) studied this question.