PHASES score and treatment scoring with cigarette smoking in the long-term prediction of rupturing of unruptured intracranial aneurysms

Wall permeability on magnetic resonance imaging is associated with intracranial aneurysm symptoms and wall enhancement

OBJECTIVES

Wall remodeling and inflammation accompany symptomatic unruptured intracranial aneurysms (UIAs). The volume transfer constant (Ktrans) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) reflects UIA wall permeability. Aneurysmal wall enhancement (AWE) on vessel wall MRI (VWI) is associated with inflammation. We hypothesized that Ktrans is related to symptomatic UIAs and AWE.

METHODS

Consecutive patients with UIAs were prospectively recruited for 3-T DCE-MRI and VWI from January 2018 to March 2023. UIAs were classified as asymptomatic and symptomatic if associated with sentinel headache or oculomotor nerve palsy. Ktrans and AWE were assessed on DCE-MRI and VWI, respectively. AWE was evaluated using the AWE pattern and wall enhancement index (WEI). Spearman's correlation coefficient and univariate and multivariate analyses were used to assess correlations between parameters.

RESULTS

We enrolled 82 patients with 100 UIAs (28 symptomatic and 72 asymptomatic). The median Ktrans (2.1 versus 0.4 min-1; p < 0.001) and WEI (1.5 versus 0.4; p < 0.001) were higher for symptomatic aneurysms than for asymptomatic aneurysms. Ktrans (odds ratio [OR]: 1.60, 95% confidence interval [95% CI]: 1.01-2.52; p = 0.04) and WEI (OR: 3.31, 95% CI: 1.05-10.42; p = 0.04) were independent risk factors for symptomatic aneurysms. Ktrans was positively correlated with WEI (Spearman's coefficient of rank correlation (rs) = 0.41, p < 0.001). The combination of Ktrans and WEI achieved an area under the curve of 0.81 for differentiating symptomatic from asymptomatic aneurysms.

CONCLUSIONS

Ktrans may be correlated with symptomatic aneurysms and AWE. Ktrans and WEI may provide an additional value than the PHASES score for risk stratification of UIAs.

CLINICAL RELEVANCE STATEMENT

The volume transfer constant (Ktrans) from DCE-MRI perfusion is associated with symptomatic aneurysms and provides additional value above the clinical PHASES score for risk stratification of intracranial aneurysms.

KEY POINTS

• The volume transfer constant is correlated with intracranial aneurysm symptoms and aneurysmal wall enhancement. • Dynamic contrast-enhanced and vessel wall MRI facilitates understanding of the pathophysiological characteristics of intracranial aneurysm walls. • The volume transfer constant and wall enhancement index perform better than the traditional PHASES score in differentiating symptomatic aneurysms.

Relationships between irregular pulsation and variations in morphological characteristics during the cardiac cycle in unruptured intracranial aneurysms by 4D-CTA

Background and purpose

Irregular pulsation of the aneurysmal wall has been suggested as a novel predictor for aneurysm rupture. Aneurysm volume variations during the cardiac cycle and the association between irregular pulsation and morphological features have been discussed, but the clinical significance remains unclear. The purpose of this study was to quantify changes in morphological characteristics over the cardiac cycle and examine their correlation with irregular pulsation to facilitate comprehension of aneurysm dynamics.

Materials and methods

Fourteen unruptured intracranial aneurysms (UIAs) from 11 patients were included in this study, and each of them underwent 4D-CTA after diagnosis by DSA. The R-R intervals were divided into 20-time phases at 5% intervals to determine whether an aneurysm had irregular pulsation throughout the cardiac cycle. CT images from the 20-time phases were used to reconstruct 3D aneurysm models, measure 14 morphological parameters, and quantify each parameter's absolute change and relative rates of change during the cardiac cycle.

Results

Seven of 14 UIAs exhibited irregular pulsation over the cardiac cycle by 4D-CTA, 5 of which were small aneurysms (< 7 mm). The UIAs with irregular pulsation exhibited greater changes in morphological characteristics. As aneurysm size increased, the absolute change in aneurysm volume increased (p = 0.035), but the relative rates of change in aneurysm size (p = 0.013), height (p = 0.014), width (p = 0.008), height-to-width ratio (p = 0.009), dome-to-neck ratio (p = 0.019) and bottleneck factor (p = 0.012) decreased.

Conclusion

Although the larger the aneurysm, the greater the amplitude of its volumetric variation, small aneurysms are prone to irregular pulsation during the cardiac cycle and have more pronounced and dramatic morphological changes during the cardiac cycle that may increase the risk of rupture. This proof-of-concept study could help to explain the importance of dynamic changes using 4D-CTA in assessing the rupture risk of UIAs.

Nomograms for assessing the rupture risk of anterior choroid artery aneurysms based on clinical, morphological, and hemodynamic features

Background and purpose

A notable prevalence of subarachnoid hemorrhage is evident among patients with anterior choroidal artery aneurysms in clinical practice. To evaluate the risk of rupture in unruptured anterior choroidal artery aneurysms, we conducted a comprehensive analysis of risk factors and subsequently developed two nomograms.

Methods

A total of 120 cases of anterior choroidal artery aneurysms (66 unruptured and 54 ruptured) from 4 medical institutions were assessed utilizing computational fluid dynamics (CFD) and digital subtraction angiography (DSA). The training set, consisting of 98 aneurysms from 3 hospitals, was established, with an additional 22 cases from the fourth hospital forming the external validation set. Statistical differences between the two data sets were thoroughly compared. The significance of 9 clinical baseline characteristics, 11 aneurysm morphology parameters, and 4 hemodynamic parameters concerning aneurysm rupture was evaluated within the training set. Candidate selection for constructing the nomogram models involved regression analysis and variance inflation factors. Discrimination, calibration, and clinical utility of the models in both training and validation sets were assessed using area under curves (AUC), calibration plots, and decision curve analysis (DCA). The DeLong test, net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed to compare the effectiveness of classification across models.

Results

Two nomogram models were ultimately constructed: model 1, incorporating clinical, morphological, and hemodynamic parameters (C + M + H), and model 2, relying primarily on clinical and morphological parameters (C + M). Multivariate analysis identified smoking, size ratio (SR), normalized wall shear stress (NWSS), and average oscillatory shear index (OSIave) as optimal candidates for model development. In the training set, model 1 (C + M + H) achieved an AUC of 0.795 (95% CI: 0.706 ~ 0.884), demonstrating a sensitivity of 95.6% and a specificity of 54.7%. Model 2 (C + M) had an AUC of 0.706 (95% CI: 0.604 ~ 0.808), with corresponding sensitivity and specificity of 82.4 and 50.3%, respectively. Similarly, AUCs for models 1 and 2 in the external validation set were calculated to be 0.709 and 0.674, respectively. Calibration plots illustrated a consistent correlation between model evaluations and real-world observations in both sets. DCA demonstrated that the model incorporating hemodynamic parameters offered higher clinical benefits. In the training set, NRI (0.224, p = 0.007), IDI (0.585, p = 0.002), and DeLong test (change = 0.089, p = 0.008) were all significant. In the external validation set, NRI, IDI, and DeLong test statistics were 0.624 (p = 0.063), 0.572 (p = 0.044), and 0.035 (p = 0.047), respectively.

Conclusion

Multidimensional nomograms have the potential to enhance risk assessment and patient-specific treatment of anterior choroidal artery aneurysms. Validated by an external cohort, the model incorporating clinical, morphological, and hemodynamic features may provide improved classification of rupture states.

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor

OBJECTIVE

The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional statistical methods in predicting aneurysm rupture risk.

METHODS

This retrospective study included 1740 patients with 1809 intracranial aneurysms confirmed by digital subtraction angiography at two hospitals in China from January 2014 to December 2018. We randomly divided the dataset (hospital 1) into training (80%) and internal validation (20%). External validation was performed using independent data collected from hospital 2. The prediction models were developed based on clinical, aneurysm morphological, and radiomics parameters by logistic regression (LR). Additionally, the DL model for predicting aneurysm rupture risk using integration parameters was developed and compared with other models.

RESULTS

The AUCs of LR models A (clinical), B (morphological), and C (radiomics) were 0.678, 0.708, and 0.738, respectively (all p < 0.05). The AUCs of the combined feature models D (clinical and morphological), E (clinical and radiomics), and F (clinical, morphological, and radiomics) were 0.771, 0.839, and 0.849, respectively. The DL model (AUC = 0.929) outperformed the machine learning (ML) (AUC = 0.878) and the LR models (AUC = 0.849). Also, the DL model has shown good performance in the external validation datasets (AUC: 0.876 vs 0.842 vs 0.823, respectively).

CONCLUSION

Radiomics signatures play an important role in predicting aneurysm rupture risk. DL methods outperformed conventional statistical methods in prediction models for the rupture risk of unruptured intracranial aneurysms, integrating clinical, aneurysm morphological, and radiomics parameters.

KEY POINTS

• Radiomics parameters are associated with the rupture risk of intracranial aneurysms. • The prediction model based on integrating parameters in the deep learning model was significantly better than a conventional model. • The radiomics signature proposed in this study could guide clinicians in selecting appropriate patients for preventive treatment.

Geometric uncertainty in intracranial aneurysm rupture status discrimination: a two-site retrospective study

OBJECTIVES

Assessing the risk associated with unruptured intracranial aneurysms (IAs) is essential in clinical decision making. Several geometric risk parameters have been proposed for this purpose. However, performance of these parameters has been inconsistent. This study evaluates the performance and robustness of geometric risk parameters on two datasets and compare it to the uncertainty inherent in assessing these parameters and quantifies interparameter correlations.

METHODS

Two datasets containing 244 ruptured and unruptured IA geometries from 178 patients were retrospectively analysed. IAs were stratified by anatomical region, based on the PHASES score locations. 37 geometric risk parameters representing four groups (size, neck, non-dimensional, and curvature parameters) were assessed. Analysis included standardised absolute group differences (SADs) between ruptured and unruptured IAs, ratios of SAD to median relative uncertainty (MRU) associated with the parameters, and interparameter correlation.

RESULTS

The ratio of SAD to MRU was lower for higher dimensional size parameters (ie, areas and volumes) than for one-dimensional size parameters. Non-dimensional size parameters performed comparatively well with regard to SAD and MRU. SAD was higher in the posterior anatomical region. Correlation of parameters was strongest within parameter (sub)groups and between size and curvature parameters, while anatomical region did not strongly affect correlation patterns.

CONCLUSION

Non-dimensional parameters and few parameters from other groups were comparatively robust, suggesting that they might generalise better to other datasets. The data on discriminative performance and interparameter correlations presented in this study may aid in developing and choosing robust geometric parameters for use in rupture risk models.

A web-based dynamic nomogram for rupture risk of posterior communicating artery aneurysms utilizing clinical, morphological, and hemodynamic characteristics

Background

Predicting rupture risk is important for aneurysm management. This research aimed to develop and validate a nomogram model to forecast the rupture risk of posterior communicating artery (PcomA) aneurysms.

Methods

Clinical, morphological, and hemodynamic parameters of 107 unruptured PcomA aneurysms and 225 ruptured PcomA aneurysms were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) analysis was applied to identify the optimal rupture risk factors, and a web-based dynamic nomogram was developed accordingly. The nomogram model was internally validated and externally validated independently. The receiver operating characteristic (ROC) curve was used to assess the discrimination of nomogram, and simultaneously the Hosmer–Lemeshow test and calibration plots were used to assess the calibration. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical utility of nomogram additionally.

Results

Four optimal rupture predictors of PcomA aneurysms were selected by LASSO and identified by multivariate logistic analysis, including hypertension, aspect ratio (AR), oscillatory shear index (OSI), and wall shear stress (WSS). A web-based dynamic nomogram was then developed. The area under the curve (AUC) in the training and external validation cohorts was 0.872 and 0.867, respectively. The Hosmer–Lemeshow p > 0.05 and calibration curves showed an appropriate fit. The results of DCA and CIC indicated that the net benefit rate of the nomogram model is higher than other models.

Conclusion

Hypertension, high AR, high OSI, and low WSS were the most relevant risk factors for rupture of PcomA aneurysms. A web-based dynamic nomogram thus established demonstrated adequate discrimination and calibration after internal and external validation. We hope that this tool will provide guidance for the management of PcomA aneurysms.

The Biological Effects of Smoking on the Formation and Rupture of Intracranial Aneurysms: A Systematic Review and Meta-Analysis

Background

Aneurysms of the cerebral vasculature are relatively common, which grow unpredictably, and even small aneurysms carry a risk of rupture. Rupture of intracranial aneurysms (IA) is a catastrophic event with a high mortality rate. Pieces of evidence have demonstrated that smoking is closely related to the formation and rupture of IA. However, the biological effect of smoking cigarettes on the formation and rupture of IA is still underrepresented.

Methods

The study protocol was prospectively registered in PROSPERO, registration number CRD42020203634. We performed a systematic search in PubMed and CNKI for studies exploring the biological effects of smoking on intracranial aneurysms published up to December 2021, and all studies were included in the analysis. The RevMan software was used for data analysis.

Results

A total of 6,196 patients were included in 14 original articles in this meta-analysis. The risk of ruptured IA in the current smoking group was significantly higher than that in the non-smoking group, with statistical significance (RR_total = 1.23, 95% CI: 1.11–1.37). After heterogeneity among cohorts was removed by the sensitivity analysis, there was still a statistically significant difference in the risk of ruptured IA between the smoking and non-smoking groups (RR total = 1.26, 95% CI: 1.18–1.34). There was no statistically significant difference in the risk of ruptured IA between the former smoking (smoking cessation) group and the non-smoking group (RR_total = 1.09, 95% CI: 0.50–2.38). After heterogeneity among cohorts was removed by sensitivity analysis, there was still no statistically significant difference in the risk of ruptured IA between the former smoking (smoking cessation) group and the non-smoking group (RRtotal = 0.75, 95% CI: 0.47–1.19). The risk of the ruptured IA in the current smoking group was significantly higher than that in the former smoking (smoking cessation) group, with a statistically significant difference (RR_total=1.42, 95%CI: 1.27–1.59).

Conclusion

Although the biological effects of smoking on the formation and rupture of IA are unknown, this study suggests that current smoking is a risk factor for ruptured IA. Quitting smoking is very important for patients with IA.