Morphological risk model assessing anterior communicating artery aneurysm rupture: Development and validation

Optimal PHASES Scoring for Risk Stratification of Surgically Treated Unruptured Aneurysms

OBJECTIVE

The PHASES (Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, Site) score was developed to facilitate risk stratification for management of unruptured intracranial aneurysms (UIAs). This study aimed to identify the optimal PHASES score cutoff for predicting neurologic outcomes in patients with surgically treated aneurysms.

METHODS

All patients who underwent microneurosurgical treatment for UIA at a large quaternary center from January 1, 2014, to December 31, 2020, were retrospectively reviewed. Inclusion criteria included a modified Rankin Scale (mRS) score of ≤2 at admission. The primary outcome was 1-year mRS score, with a "poor" neurologic outcome defined as an mRS score >2.

RESULTS

In total, 375 patients were included in the analysis. The mean (SD) PHASES score for the entire study population was 4.47 (2.67). Of 375 patients, 116 (31%) had a PHASES score ≥6, which was found to maximize prediction of poor neurologic outcome. Patients with PHASES scores ≥6 had significantly higher rates of poor neurologic outcome than patients with PHASES scores <6 at discharge (58 [50%] vs. 90 [35%], P = 0.005) and follow-up (20 [17%] vs. 18 [6.9%], P = 0.002). After adjusting for age, Charlson Comorbidity Index score, nonsaccular aneurysm, and aneurysm size, PHASES score ≥6 remained a significant predictor of poor neurologic outcome at follow-up (odds ratio, 2.75; 95% confidence interval, 1.42-5.36, P = 0.003).

CONCLUSIONS

In this retrospective analysis, a PHASES score ≥6 was associated with significantly greater proportions of poor outcome, suggesting that awareness of this threshold in PHASES scoring could be useful in risk stratification and UIA management.

Associations between morphological parameters and ruptured anterior communicating artery aneurysm: A propensity score-matched, single center, case-control study

BACKGROUND

To identify an association between morphological parameters and the rupture risk of anterior communicating artery (ACoA) aneurysms using propensity score matching (PSM).

METHODS

Data for 109 patients with ACoA aneurysms treated from January 2018 to October 2021 were reviewed; 94 patients were enrolled. The geometrical parameters of the ACoA aneurysms were measured and calculated using three-dimensional reconstructed digital subtraction angiography images. The aneurysms' morphological parameters were analyzed using a propensity score for six factors (age, sex, excess alcohol intake, smoking, hypertension, diabetes mellitus). Univariate logistic regression was used to analyze the relationship between the aneurysms' morphological parameters and rupture risk.

RESULTS

Twenty-five patients each with or without ruptured aneurysms were selected. After matching, no statistically significant differences were seen between the groups in their baseline characteristics. Aneurysm neck size (p = 0.038) was higher in the unruptured group than that in the ruptured group, and the dome-to-neck ratio (D/N; p = 0.009) and aspect ratio (AR; p = 0.003) were higher in the ruptured group than those in the unruptured group. Univariable logistic regression analysis demonstrated that ACoA aneurysm rupture was associated with AR (odds ratio: 8.047; 95% confidence interval: 1.569-41.213; p = 0.012) and D/N (odds ratio: 4.253; 95% confidence interval: 1.228-14.731; p = 0.022). The areas under the receiver operating characteristic curves for AR and D/N were 0.746 and 0.715, respectively.

CONCLUSIONS

After PSM, ACoA aneurysms with higher AR and D/N, and smaller neck size were more likely to rupture. AR may be a much more important predictor of aneurysm rupture than other predictors.

Risk factors of unruptured intracranial aneurysms instability in the elderly

BACKGROUND

Presently, a consistent strategy for determining the stability of unruptured intracranial aneurysms (UIAs) in elderly patients is lacking, primarily due to the unique characteristics of this demographic. Our objective was to assess the risk factors contributing to aneurysm instability (growth or rupture) within the elderly population.

METHODS

In this study, we compiled data from follow-up patients with UIAs spanning from November 2016 to August 2021. We specifically focused on patients aged ≥ 60 years. Clinical histories were gathered, and morphological parameters of aneurysms were measured. The growth of aneurysms was determined using the computer-assisted semi-automated measurement (CASAM). Growth and rupture rates of UIAs were calculated, and both univariate and multivariate Cox regression analyses were conducted. Additionally, Kaplan-Meier survival curves were plotted.

RESULTS

A total of 184 patients with 210 aneurysms were enrolled in the study. The follow-up period encompasses 506.6 aneurysm-years and 401.4 patient-years. Among all the aneurysms, 23 aneurysms exhibited growth, with an annual aneurysm growth rate of 11.0%, and 1 (4.5%) experienced rupture, resulting in an annual aneurysm rupture rate of 0.21%. Multivariate Cox analysis identified poorly controlled hypertension (P = 0.011) and high-risk aneurysms (including anterior cerebral artery (ACA), anterior communicating artery (AcoA), posterior communicating artery aneurysm (PcoA), posterior circulation (PC) > 4 mm or distal internal carotid artery (ICAd), middle cerebral artery (MCA), and PC > 7 mm) (P = 0.006) as independent risk factors for the development of unstable aneurysms.

CONCLUSIONS

In the elderly, poorly controlled hypertension and high-risk aneurysms emerge as significant risk factors for aneurysm instability. This underscores the importance of rigorous surveillance or timely intervention in patients presenting with these risk factors.

Clinical, 3D Morphological, and Hemodynamic Risk Factors for Instability of Unruptured Intracranial Aneurysms

PURPOSE

Neurosurgeons can manage unruptured intracranial aneurysms (UIAs). However, the stability of UIAs under follow-up remains uncertain. This study aimed to examine the risk factors associated with the instability (rupture or growth) of UIAs during follow-up.

METHODS

We obtained information on patients with UIA who underwent ≥ 6 months of the time of flight-magnetic resonance angiography (TOF-MRA) imaging follow-up in two centers. Computer-assisted semi-automated measurement (CASAM) techniques were used for recording morphological parameters and determining the growth of these aneurysms. We also recorded hemodynamic parameters at the beginning of the follow-up. The univariate and multivariate Cox regression analyses were performed to calculate hazard ratios with corresponding 95% confidence intervals for the clinical, morphological, and hemodynamic risk factors for aneurysm instability.

RESULTS

A total of 304 aneurysms from 263 patients (80.4%) were included for analysis. The annual aneurysm growth rate was 4.7%. Significant predictive factors for aneurysm instability in the multivariate analysis were as follows: poorly controlled hypertension (hazard ratio (HR), 2.97 (95% CI, 1.27-6.98), P = 0.012); aneurysms located on posterior circulation (HR, 7.81 (95% CI, 2.28-26.73), P = 0.001), posterior communication artery (HR, 3.01 (95% CI, 1.07-8.46), P = 0.036), and cavernous carotid artery (HR, 3.78 (95% CI, 1.18-12.17), P = 0.026); and size ratio ≥ 0.87 (HR, 2.54 (95% CI, 1.14-5.68), P = 0.023).

CONCLUSIONS

The management of UIAs should focus on the control of hypertension during the follow-up. Aneurysms on the posterior communicating artery, posterior circulation, and cavernous carotid arteries require intensive surveillance or timely treatment.

Clinical and Morphological Factors for Ruptured Anterior Communicating Artery Aneurysms

Introduction

The anterior communicating artery (ACoA) aneurysm, the most frequent cerebral aneurysm to rupture, carries a significant clinical burden, yet the factors influencing its rupture are limited in Indonesia. This study aims to determine the clinical and morphological features associated with ruptured ACoA compared to non-AcoA aneurysms among Indonesians.

Patients and Methods

We retrospectively reviewed our center's aneurysm patient registry from January 2019 to December 2022, and compared the clinical and morphological features between ruptured ACoA aneurysms and ruptured aneurysms elsewhere with univariate and multivariate analyses.

Results

Of the 292 patients with 325 ruptured aneurysms, 89 were from ACoA. The mean age of patients was 54.99 years, with female preponderance in the non-ACoA group (non-ACoA: 73.31%, ACoA: 46.07%). On univariate analysis, ages ≥60 [ages 60-69: OR = 0.311 (0.111-0.869), p=0.026; ages ≥70: OR = 0.215 (0.056-0.819), p=0.024], female gender [OR = 0.311 (0.182-0.533), p<0.001], and smoking [OR=2.069 (1.036-4.057), p=0.022] exhibited significant association with ruptured ACoA aneurysm. On multivariate analysis, only the female gender was independently associated with ruptured ACoA aneurysm (aOR 0.355 [0.436-1.961], p=0.001).

Conclusion

In our study, ruptured ACoA aneurysm was inversely associated with advanced age, female gender, presence of daughter aneurysm, and directly associated with smoking. After multivariate adjustment, the female gender showed an independent association with ruptured ACoA aneurysm.

A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely and appropriate treatment because the fatality rate after rupture is 50 % . Existing methods relying on morphological features (e.g., height-width ratio) measured manually by neuroradiologists are labor intensive and have limited use for risk assessment. Therefore, we propose an end-to-end deep-learning method, called TransIAR net, to automatically learn the morphological features from 3D computed tomography angiography (CTA) data and accurately predict the status of IA rupture. We devise a multiscale 3D convolutional neural network (CNN) to extract the structural patterns of the IA and its neighborhood with a dual branch of shared network structures. Moreover, we learn the spatial dependence within the IA neighborhood with a transformer encoder. Our experiments demonstrated that the features learned by TransIAR are more effective and robust than handcrafted features, resulting in a 10 % - 15 % improvement in the accuracy of rupture status prediction.

Prediction of cerebral aneurysm rupture using a point cloud neural network

OBJECTIVE

Accurate prediction of cerebral aneurysm (CA) rupture is of great significance. We intended to evaluate the accuracy of the point cloud neural network (PC-NN) in predicting CA rupture using MR angiography (MRA) and CT angiography (CTA) data.

METHODS

418 CAs in 411 consecutive patients confirmed by CTA (n=180) or MRA (n=238) in a single hospital were retrospectively analyzed. A PC-NN aneurysm model with/without parent artery involvement was used for CA rupture prediction and compared with ridge regression, support vector machine (SVM) and neural network (NN) models based on radiomics features. Furthermore, the performance of the trained PC-NN and radiomics-based models was prospectively evaluated in 258 CAs of 254 patients from five external centers.

RESULTS

In the internal test data, the area under the curve (AUC) of the PC-NN model trained with parent artery (AUC=0.913) was significantly higher than that of the PC-NN model trained without parent artery (AUC=0.851; p=0.041) and of the ridge regression (AUC=0.803; p=0.019), SVM (AUC=0.788; p=0.013) and NN (AUC=0.805; p=0.023) radiomics-based models. Additionally, the PC-NN model trained with MRA source data achieved a higher prediction accuracy (AUC=0.936) than that trained with CTA source data (AUC=0.824; p=0.043). In external data of prospective cohort patients, the AUC of PC-NN was 0.835, significantly higher than ridge regression (0.692; p<0.001), SVM (0.701; p<0.001) and NN (0.681; p<0.001) models.

CONCLUSION

PC-NNs can achieve more accurate CA rupture prediction than traditional radiomics-based models. Furthermore, the performance of the PC-NN model trained with MRA data was superior to that trained with CTA data.

Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling

Objective

Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.

Methods

From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression.

Results

A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms.

Conclusion

Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.

Rupture Risk Assessment for Anterior Communicating Artery Aneurysms Using Decision Tree Modeling

Background

Although anterior communicating artery (ACoA) aneurysms have a higher risk of rupture than aneurysms in other locations, whether to treat unruptured ACoA aneurysms incidentally found is a dilemma because of treatment-related complications. Machine learning models have been widely used in the prediction of clinical medicine. In this study, we aimed to develop an easy-to-use decision tree model to assess the rupture risk of ACoA aneurysms.

Methods

This is a retrospective analysis of rupture risk for patients with ACoA aneurysms from two medical centers. Morphologic parameters of these aneurysms were measured and evaluated. Univariate analysis and multivariate logistic regression analysis were performed to investigate the risk factors of aneurysm rupture. A decision tree model was developed to assess the rupture risk of ACoA aneurysms based on significant risk factors.

Results

In this study, 285 patients were included, among which 67 had unruptured aneurysms and 218 had ruptured aneurysms. Aneurysm irregularity and vessel angle were independent predictors of rupture of ACoA aneurysms. There were five features, including size ratio, aneurysm irregularity, flow angle, vessel angle, and aneurysm size, selected for decision tree modeling. The model provided a visual representation of a decision tree and achieved a good prediction performance with an area under the receiver operating characteristic curve of 0.864 in the training dataset and 0.787 in the test dataset.

Conclusion

The decision tree model is a simple tool to assess the rupture risk of ACoA aneurysms and may be considered for treatment decision-making of unruptured intracranial aneurysms.

Irregular Pulsation of Intracranial Aneurysm Detected by Four-Dimensional CT Angiography and Associated With Small Aneurysm Rupture: A Single-Center Prospective Analysis

Objectives

Predicting the risk of rupture of small intracranial aneurysms remains challenging. The irregular pulsation of aneurysms detected by four-dimensional CT angiography (4D-CTA) could be an imaging marker of aneurysm vulnerability. We aimed to investigate the association of irregular pulsation with small aneurysm rupture.

Materials and Methods

This was a prospective study on intracranial aneurysms detected by 4D-CTA from October 2017 to January 2020. A total of 242 consecutive patients with 316 aneurysms were enrolled. Irregular pulsation was defined as a temporary focal protuberance on more than 3 consecutive frames of the 20 phases in the RR interval. Small aneurysms were defined as those <7 mm. Univariate and multivariate analyses were performed to determine the independent predictors of small aneurysm rupture.

Results

A total of 169 patients with 217 small intracranial aneurysms were included. Fourteen (6.5%) of the aneurysms had ruptured, and 77 (35.5%) had irregular pulsation. There were no significant differences in age, sex, hypertension, smoking, diabetes, drinking, or hyperlipidemia between the ruptured and unruptured aneurysm groups. The univariate analysis showed that smaller vessel size (p = 0.008), larger size ratio (p = 0.003), larger aspect ratio (p = 0.006), larger flow angle (p = 0.001), large vessel angle (p = 0.004), middle cerebral artery aneurysms (p = 0.046), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysm (p = 0.006), irregular aneurysm (p = 0.001), and t presence of irregular pulsation (p = 0.001) were associated with small aneurysm rupture. The multivariate analysis showed that the presence of irregular pulsation (p = 0.003), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysms (p = 0.014), and larger flow angle (p = 0.006) was independently associated with aneurysm rupture. Multivariate analysis of predictors of the irregular pulsation of small aneurysms showed that the aneurysm rupture (p = 0.022), irregular aneurysm (p < 0.001), and large size ratio (p = 0.005) were independently associated with the presence of irregular pulsation.

Conclusions

The ruptured small aneurysms more often had irregular pulsation. The irregular pulsation was independently associated with aneurysm rupture and may help evaluate the risk of rupture of small intracranial aneurysms.

Risk factors for anterior communicating artery aneurysm rupture: A protocol for systematic review and meta-analysis

BACKGROUND

Although the research on the risk factors of anterior communicating artery (AComA) aneurysm has made great progress, the independent effect of each risk factor on the rupture of AComA aneurysm is controversial among different studies. We will perform a protocol for systematic review and meta-analysis to investigate risk factors for AComA aneurysm rupture and quantify their independent effects.

METHODS

A systematic search according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines in PubMed, Embase, and the Cochrane Library databases was conducted from inception to August 31, 2021 for published studies concerning risk factors for AComA aneurysm rupture. In the absence of statistical heterogeneity (ie, P > .10 and I2 < 50%), we will use a fixed-effects model to pool the results across sufficient studies. Otherwise, we will present the results employing the random-effects model. Quality assessment of the included studies will be evaluated using the Newcastle-Ottawa Scale. Statistical analyses will be performed using Stata16 (Stata Corporation, College Station, TX, USA) software.

RESULTS

The findings of this study will be submitted to peer-reviewed journals for publication.

CONCLUSION

This systematic review will provide evidence to determine the risk factors that affect the rupture of the AComA aneurysm and quantify their independent effects.

ETHICS AND DISSEMINATION

Since the proposed study uses pre-published data, ethical approval is not required.

REVIEW REGISTRATION NUMBER

CRD42021284262. (https://www.crd.york.ac.uk/PROSPERO/).

Aneurysm morphological prediction of intracranial aneurysm rupture in elderly patients using four-dimensional CT angiography

OBJECTIVE

The natural history of unruptured intracranial aneurysms (UIAs) in elderly patients remains poorly understood, and the treatment of UIAs is controversial. The presence of irregular pulsation detected by four-dimensional CT angiography (4D-CTA) is associated with ruptured aneurysms. We aimed to investigate the morphological predictors of irregular pulsation of aneurysms in elderly patients.

PATIENTS AND METHODS

We performed a prospective study of intracranial aneurysms detected by 4D-CTA. Elderly patients were defined as those more than 60 years of age. The irregular pulsation was defined as a focal protuberance during a cardiac cycle. We performed multivariate analyses to determine the associations of clinical characteristics and aneurysm morphologies with the irregular pulsation of aneurysms.

RESULTS

A total of 128 elderly patients with 166 intracranial aneurysms was included. The irregular pulsation occurred in 71 (42.8%) aneurysms. The multivariate analysis showed that a large size ratio (p = 0.006), posterior circulation aneurysms (p = 0.033), the presence of a daughter dome (p = 0.006), and aneurysm rupture (p = 0.032) were independently associated with the irregular pulsation. The multivariate analysis of predictors of irregular pulsation of unruptured aneurysms showed that size ratio (p = 0.01) and the presence of a daughter dome (p = 0.016) were independent predictors of irregular pulsation.

CONCLUSION

A large size ratio, posterior circulation aneurysms, the presence of a daughter dome, and aneurysm rupture were independent predictors of the irregular pulsation of aneurysms in elderly patients. The morphological characteristics detected by 4D-CTA may be helpful to evaluate the risk of rupture of aneurysms.