Risk Factors for Pericallosal Artery Aneurysm Rupture Based on Morphological Computer-Assisted Semiautomated Measurement and Hemodynamic Analysis

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.

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.

Low Wall Shear Stress and High Intra-aneurysmal Pressure are Associated with Ruptured Status of Vertebral Artery Dissecting Aneurysms

PURPOSE

The morphological and hemodynamic features of patients with vertebral artery dissecting aneurysms (VADAs) are yet unknown. This study sought to elucidate morphological and hemodynamic features of patients with ruptured and unruptured VADAs based on computed flow simulation.

METHODS

Fifty-two patients (31 unruptured and 21 ruptured VADAs) were admitted to two hospitals between March 2016 and October 2021. All VADAs were located in the intradural segment, and their clinical, morphological, and hemodynamic parameters were retrospectively analyzed. The hemodynamic parameters were determined through computational fluid dynamics simulations. Univariate statistical and multivariable logistic regression analyses were employed to select significantly different parameters and identify key factors. Receiver operating characteristic (ROC) analysis was used to assess the discrimination for each key factor.

RESULTS

Four hemodynamic parameters were observed to significantly differ between ruptured and unruptured VADAs, including wall shear stress (WSS), low shear area, intra-aneurysmal pressure (IAP), and relative residence time. However, no significant differences were observed in morphological parameters between ruptured and unruptured VADAs. Multivariable logistic regression analysis revealed that low WSS and high IAP were significantly observed in the ruptured VADAs and demonstrated adequate discrimination.

CONCLUSIONS

This research indicates significant hemodynamic differences, but no morphological differences were observed between ruptured and unruptured VADAs. The ruptured group had significantly lower WSS and higher IAP than the unruptured group. To further confirm the roles of low WSS and high IAP in the rupture of VADAs, large prospective studies and long-term follow-up of unruptured VADAs are required.

A predictive hemodynamic model based on risk factors for ruptured mirror aneurysms

Objectives

To identify hemodynamic risk factors for intracranial aneurysm rupture and establish a predictive model to aid evaluation.

Methods

We analyzed the hemodynamic parameters of 91 pairs of ruptured mirror aneurysms. A conditional univariate analysis was used for the continuous variables. A conditional multivariate logistic regression analysis was performed to identify the independent risk factors. Differences where p < 0.05 were statistically significant. A predictive model was established based on independent risk factors. Odds ratios (ORs) were used to score points. The validation cohort consisted of 189 aneurysms. Receiver operating characteristic curves were generated to determine the cutoff values and area under the curves (AUCs) of the predictive model and independent risk factors.

Results

The conditional multivariate logistic analysis showed that the low shear area (LSA) (OR = 70.322, p = 0.044, CI = 1.112–4,445.256), mean combined hemodynamic parameter (CHP) (>0.087) (OR = 3.171, p = 0.034, CI = 1.089–9.236), and wall shear stress gradient (WSSG) ratio (>893.180) (OR = 5.740, p = 0.003, CI = 1.950–16.898) were independent risk factors. A prediction model was established: 23^*LSA + 1^*CHP mean (>0.087: yes = 1, no = 0) + 2 ^* WSSG ratio (>893.180: yes = 1, no = 0). The AUC values of the predictive model, LSA, mean CHP (>0.087), and WSSG ratio (>893.180) were 0.748, 0.700, 0.654, and 0.703, respectively. The predictive model and LSA cutoff values were 1.283 and 0.016, respectively. In the validation cohort, the predictive model, LSA, CHP (>0.087), and WSSG ratio (>893.180) were 0.736, 0.702, 0.689, and 0.706, respectively.

Conclusions

LSA, CHP (>0.087), and WSSG ratio (>893.180) were independent risk factors for aneurysm rupture. Our predictive model could aid practical evaluation.

Different Hemodynamic Characteristics and Resulting in Different Risks of Rupture Between Wide-Neck and Narrow-Neck Aneurysms

Objective

This study aimed to determine the ruptured rate and hemodynamic difference between wide-neck aneurysms (WNAs) and narrow-neck aneurysms (NNAs), as well as the hemodynamic parameters of risk factors for aneurysm rupture.

Methods

A total of 121 cases of intracranial aneurysms (IAs) were studied retrospectively between January 2019 and April 2021 at Renmin Hospital of Wuhan University. Intracranial aneurysms were classified into four types: ruptured wide-neck aneurysms (RWNAs), unruptured wide-neck aneurysms (UWNAs), ruptured narrow-neck aneurysms (RNNAs), and unruptured narrow-neck aneurysms (UNNAs). The Chi-square test was used to compare differences in rupture ratios. The clinical characteristics and hemodynamics were analyzed statistically to reveal the rupture risk factors. Moreover, significant parameters were subjected to binary logistic regression analysis to identify the independent predictive factors. The receiver operating characteristic (ROC) curve was performed to obtain cutoff values.

Results

WNAs ruptured more frequently than NNAs (P = 0.033). Ruptured intracranial aneurysms (RIAs) were characterized by significantly higher intra-aneurysmal pressure (IAP), wall shear stress (WSS), wall shear stress gradient (WSSG), and lower normalized wall shear stress (NWSS) than unruptured intracranial aneurysms (UIAs). RWNAs had higher IAP, WSS, and lower NWSS than UWNAs (P < 0.05). RNNAs had higher IAP, Streamwise WSSG and lower NWSS compared to UNNAs (P < 0.05). Binary logistic regression revealed that IAP and WSS were independent predictive risk factors for WNAs rupture, with cut-off values of 405.5 and 6.66 Pa, respectively. Also, IAP was an independent predictive risk factor for NNA rupture, with a cut-off value of 255.3 Pa.

Conclusions

Wide-neck aneurysms and narrow-neck aneurysms have diverse hemodynamics, which prompts a higher rupture ratio for WNAs. IAP could characterize the rupture risk in both WNAs and NNAs independently, but WSS could only predict the rupture risk in WNAs. This research might assist neurosurgeons with fostering a more sensible strategy for the treatment of IAs.

Case Report: Dynamic Changes in Hemodynamics During the Formation and Progression of Intracranial Aneurysms

Despite the devastating consequences of aneurysmal subarachnoid hemorrhage (SAH), the mechanisms underlying the formation, progression, and rupture of intracranial aneurysms (IAs) are complex and not yet fully clear. In a real-world situation, continuously observing the process of aneurysm development in humans appears unrealistic, which also present challenges for the understanding of the underlying mechanism. We reported the relatively complete course of IA development in two real patients. On this basis, computational fluid dynamics simulation (CFD) was performed to evaluate the changes in hemodynamics and analyze the mechanism underlying the formation, progression, and rupture of IAs. Our results suggested that the formation and progression of IAs can be a dynamic process, with constantly changing hemodynamic characteristics. CFD analysis based on medical imaging provides the opportunity to study the hemodynamic conditions over time. From these two rare cases, we found that concentrated high-velocity inflow jets, flows with vortex structures, extremely high WSS, and a very steep WSSG were correlated with the formation of IAs. Complex multi-vortex flows are possibly related to IAs prior to growth, and the rupture of IAs is possibly related to low WSS, extreme instability and complexity of flow patterns. Our findings provide unique insight into the theoretical hemodynamic mechanism underlying the formation and progression of IAs. Given the small sample size the findings of this study have to be considered preliminary and exploratory.