What question did this study set out to answer?

This study aims to assess whether non-invasive hemodynamic and morphological profiling can enhance intracranial aneurysm rupture risk prediction.

May 8, 2026Open Access

Abstract Number: Esoc2026a240 Non-Invasive Flow and Morphology Profiling for Intracranial Aneurysm Risk Stratification: Cohort Insights From Retrospective Study

Key Points

This study aims to assess whether non-invasive hemodynamic and morphological profiling can enhance intracranial aneurysm rupture risk prediction.
Retrospective analysis of 71 patients with saccular aneurysms, with 10 ruptured and 61 unruptured.
Non-invasive CFD simulations to derive hemodynamic and morphological features from patient-specific vascular models.
Integration of features into a composite Risk-of-Rupture score using ensemble machine learning techniques with cross-validation.
Risk-of-Rupture score achieved AUC 0.93 with 88% sensitivity and 85% specificity, outperforming PHASES and UIATS (AUC 0.66 and 0.62 respectively).
Stratified aneurysms as Low-risk (64%), Moderate-risk (34%), and High-risk (2%), facilitating precise patient management.
Model maintained strong performance and calibration across diverse age, morphology, and clinical subsets.

Abstract

Abstract Background and aims Size-based heuristics and the absence of patient-specific flow characterization limit rupture risk assessment for intracranial aneurysms (IA). Many flow patterns linked to rupture traditionally require invasive angiography. This study tests whether integrated features from non-invasive hemodynamics, morphology, and clinical history can capture these signatures and improve prediction. Methods 71 IA patients with saccular aneurysms were retrospectively analyzed (n=71; 10 ruptured, 61 unruptured, n aneurysms). Expert segmentation enabled patient-specific vascular models for non-invasive CFD simulations, yielding hemodynamic (TAWSS, low-WSS area%, OSI, RRT) and morphological features (aspect ratio, size ratio, irregularity, volume, neck and dome geometry). Limited PHI (age, sex, hypertension, prior SAH) was extracted from chart reviews. Features were integrated into a composite Risk-of-Rupture (RoR) score (1-10) via ensemble machine learning (Logistic Regression, Random Forest, Gradient Boosting) with cross-validation. Results RoR achieved AUC 0.93 (88% sensitivity, 85% specificity), significantly outperforming PHASES (AUC 0.66) and UIATS (AUC 0.62). Risk stratification classified aneurysms as Low-risk (64%), Moderate-risk (34%), and High-risk (2%), enabling precise triage. Model retained strong performance (AUC 0.93) under minimal PHI and demonstrated excellent calibration across age, morphology, and clinical subgroups. Conclusions Ruptured aneurysms exhibited characteristic geometric risk factors and hemodynamic profiles (depressed WSS, elevated OSI/RRT) identified non-invasively via CFD. Among unruptured cases, integrated profiling revealed concealed moderate-risk phenotypes missed by morphology alone. These findings demonstrate that imaging-derived non-invasive analysis delivers physiological insights comparable to invasive angiography, enabling precise risk stratification to guide surveillance and treatment decisions. Conflict of interest Prithvinath Reddy Garigapuram: nothing to disclose

Abstract Number: Esoc2026a240 Non-Invasive Flow and Morphology Profiling for Intracranial Aneurysm Risk Stratification: Cohort Insights From Retrospective Study

Key Points

Abstract

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