A logistic regression-based nomogram incorporating four independent predictors achieved good discrimination (AUC 0.808) for predicting wall irregular pulsation in unruptured intracranial aneurysms, demonstrating 'potential for future clinical implementation in bedside decision-making.'
Key Findings
Results
Four independent predictors of wall irregular pulsation were identified via multivariable analysis: aneurysm location at ACA/PCom/Posterior, presence of daughter sac, aspect ratio, and neck size.
Aneurysm location at ACA/PCom/Posterior: OR 2.17 (95% CI 1.21–3.88; p = 0.0099)
Presence of daughter sac: OR 5.15 (95% CI 1.91–13.86; p = 0.001)
Aspect ratio: OR 1.80 (95% CI 1.09–2.99; p = 0.022)
Neck size: OR 1.23 (95% CI 1.02–1.48; p = 0.030)
Predictors were identified using a generalized estimating equation (GEE) method to account for multiple aneurysms per patient
Results
The logistic regression-based predictive model demonstrated good discrimination in the primary cohort.
AUC of 0.808 (95% CI 0.704–0.913)
Sensitivity of 88.5% and specificity of 71.2%
Overall accuracy of 76.9%
Model performance was evaluated using receiver operating characteristic analysis
Results
Independent validation of the predictive model yielded comparable or improved performance metrics.
Validation AUC of 0.855 (95% CI 0.694–1.000)
Sensitivity of 76.9% and specificity of 88.9% in the validation cohort
Overall accuracy of 81.8% in the validation cohort
Validation was performed using an independent cohort
Methods
The study included 202 patients with 262 unruptured intracranial aneurysms assessed by four-dimensional CT angiography (4D-CTA).
Consecutive cross-sectional observational study with prospective data collection between 2017 and 2020
Irregular pulsation was defined as sequential morphological alterations observed in three or more consecutive phases on 4D-CTA
The GEE method was used to handle the clustered data structure arising from multiple aneurysms per patient
Results
A nomogram was constructed to visually represent individualized risk of wall irregular pulsation for unruptured intracranial aneurysms.
The nomogram incorporates the four identified independent predictors: location, daughter sac, aspect ratio, and neck size
The tool is intended to visually assess individualized risks
Authors state it demonstrates 'potential for future clinical implementation in bedside decision-making'
Background
Wall irregular pulsation on 4D-CTA was used as an imaging biomarker of aneurysm vulnerability to serve as the prediction target.
Irregular pulsation was defined as sequential morphological alterations observed in three or more consecutive phases on 4D-CTA
The study frames wall irregular pulsation as a marker relevant to rupture risk stratification for unruptured intracranial aneurysms
Accurate rupture risk stratification for UIAs was identified as presenting 'clinical challenges'
What This Means
This research suggests that certain measurable features of brain aneurysms can predict whether the aneurysm wall shows irregular pulsation — an abnormal movement pattern visible on a specialized CT scan called 4D-CTA that may indicate the aneurysm is at higher risk of rupturing. The study followed 202 patients with 262 unruptured brain aneurysms and found that four factors were independently associated with this irregular pulsation: the location of the aneurysm (particularly in the anterior communicating artery, posterior communicating artery, or posterior circulation), the presence of a small outpouching called a daughter sac, a higher aspect ratio (meaning the aneurysm is taller relative to its neck width), and a larger neck size.
Using these four factors, the researchers built a predictive tool called a nomogram — essentially a visual chart that can combine individual patient measurements to estimate the probability that an aneurysm has irregular pulsation. The tool performed well, correctly identifying irregular pulsation cases with an overall accuracy of about 77% in the main group and 82% in a separate validation group, with AUC values (a measure of overall predictive accuracy) of 0.808 and 0.855 respectively, where 1.0 would be perfect prediction.
This research suggests that a relatively simple set of aneurysm characteristics — which can be obtained from standard imaging — may help clinicians identify which unruptured brain aneurysms are more vulnerable without requiring highly specialized testing. If further validated, such a tool could assist neurosurgeons and neurologists in making more individualized decisions about which aneurysms require closer monitoring or earlier treatment, potentially improving outcomes for patients living with unruptured brain aneurysms.
Liu J, Zhou J, Zhang J, Li X, Li O, Zhao H, et al.. (2026). Development of a nomogram for predicting wall irregular pulsation and risk stratification in intracranial aneurysms.. Frontiers in neurology. https://doi.org/10.3389/fneur.2026.1913037