Rupture risk assessment for multiple intracranial aneurysms: why there is no need for dozens of clinical, morphological and hemodynamic parameters

A model with multiple intracranial aneurysms: possible hemodynamic mechanisms of aneurysmal initiation, rupture and recurrence

BACKGROUND

Hemodynamic factors play an important role in aneurysm initiation, growth, rupture, and recurrence, while the mechanism of the hemodynamic characteristics is still controversial. A unique model of multiple aneurysms (initiation, growth, rupture, and recurrence) is helpful to avoids the confounders and further explore the possible hemodynamic mechanisms of aneurysm in different states.

METHODS

We present a model with multiple aneurysms, and including the states of initiation, growth, rupture, and recurrence, discuss the proposed mechanisms, and describe computational fluid dynamic model that was used to evaluate the likely hemodynamic effect of different states of the aneurysms.

RESULTS

The hemodynamic analysis suggests that high flow impingement and high WSS distribution at normal parent artery was found before aneurysmal initiation. The WSS distribution and flow velocity were decreased in the new sac after aneurysmal growth. Low WSS was the risk hemodynamic factor for aneurysmal rupture. High flow concentration region on the neck plane after coil embolization still marked in recanalized aneurysm.

CONCLUSIONS

Associations have been identified between high flow impingement and aneurysm recanalization, while low WSS is linked to the rupture of aneurysms. High flow concentration and high WSS distribution at normal artery associated with aneurysm initiation and growth, while after growth, the high-risk hemodynamics of aneurysm rupture was occurred, which is low WSS at aneurysm dome.

Application of zone classification in multiple intracranial aneurysmal subarachnoid hemorrhage treatment strategies

Background

The options of surgical approach and treatment stage are two challenging treatment strategy issues with multiple intracranial aneurysmal subarachnoid hemorrhage (MIA-SAH).

Methods

We retrospectively analyzed data from patients with MIA-SAH who underwent surgery in our center between January 1, 2014 and September 1, 2022. To define "zone classification", the cranial cavity was divided into four zones by the planes of cerebral falx and tentorium cerebelli. Aneurysms isolated to one zone were defined as zone classification I; those crossing two zones were defined as zone classification II; those crossing three zones were defined as zone classification III; and those crossing four zones were defined as zone classification IV. General and aneurysmal-related characteristics of patients with different zone classifications were collected and compared between two surgical approaches. Multivariate logistic regression analysis was used to identify factors independently associated with multistage treatment options.

Results

A total of 226 patients with 523 aneurysms were included. The proportion of patients undergoing endovascular treatment increased with higher zone classification (I: 85.4%; II: 94.0%; III: 100.0%; IV: 100.0%). The proportion of patients receiving one-stage treatment decreased with higher zone classification (I: 60.2%; II: 33.6%; III: 0.0%; IV: 0.0%). Compared with patients undergoing microsurgical clipping, more patients undergoing endovascular treatment had zone classification II-IV (56.9% vs. 31.8%, p = 0.025). Zone classification II-IV (odds ratio [OR] = 3.821, 95% confidence interval [CI]: 2.041-7.154, p < 0.001), endovascular treatment (OR = 8.756, 95% CI: 2.589-29.609, p < 0.001), and size of all unruptured aneurysms <3 mm (OR = 4.531, 95% CI: 2.315-8.871, p < 0.001) were each independently associated with multistage treatment.

Conclusions

Zone classification provides a new idea in MIA-SAH treatment strategies, especially regarding surgical approach and treatment stage options.

A Systematic Review and Meta-Analysis of 3-Dimensional Morphometric Parameters for Cerebral Aneurysms

BACKGROUND

Imaging modalities with increased spatial resolution have allowed for more precise quantification of cerebral aneurysm shape in 3-dimensional (3D) space. We conducted a systematic review and meta-analysis to assess the correlation of individual 3D morphometric measures with cerebral aneurysm rupture status.

METHODS

Two independent reviewers performed a PRISMA (preferred reporting items of systematic reviews and meta-analysis)-guided literature search to identify articles reporting the association between 3D morphometric measures of intracranial aneurysms and rupture status.

RESULTS

A total of 15,122 articles were identified. After screening, 39 studies were included. We identified 17 3D morphometric measures, with 11 eligible for the meta-analysis. The meta-analysis showed a significant association with rupture status for the following measures: nonsphericity index (standardized mean difference [SMD], 0.66; 95% confidence interval [CI], 0.53-0.79; P < 0.0001; I2 = 55.2%), undulation index (SMD, 0.55; 95% CI, 0.26-0.85; P = 0.0017; I2 = 68.1%), ellipticity index (SMD, 0.53; 95% CI, 0.29-0.77; P = 0.0005; I2 = 70.8%), volume (SMD, 0.18; 95% CI, 0.02-0.35; P = 0.0320; I2 = 82.3%), volume/ostium ratio (SMD, 0.43; 95% CI, 0.16-0.71; P = 0.0075; I2 = 90.4%), elongation (SMD, -0.94; 95% CI, -1.12 to -0.76; P = 0.0005; I2 = 0%), flatness (SMD, -0.87; 95% CI, -1.04 to -0.71; P = 0.0005; I2 = 0%), and sphericity (SMD, -0.62; 95% CI, -1.06 to -0.17; P = 0.0215; I2 = 67.9%). A significant risk of publication bias was estimated for the ellipticity index (P = 0.0360) and volume (P = 0.0030).

CONCLUSIONS

Based on the results of a meta-analysis containing 39 studies, the nonsphericity index, undulation index, elongation, flatness, and sphericity demonstrated the most consistent correlation with rupture status.

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.

Assessing the risk of intracranial aneurysm rupture using computational fluid dynamics: a pilot study

Objective

This study compared 2 representative cases with ruptured aneurysms to explore the role of hemodynamic and morphological parameters in evaluating the rupture risk of intracranial aneurysms (IAs).

Methods

CTA and 3-dimensional rotational angiography (3DRA) of 3 IAs in 2 patients were retrospectively analyzed in this study. Hemodynamics and morphological parameters were compared between a ruptured IA and an unruptured IA in case1, and between before and after aneurysm rupture in case 2.

Results

In case 1, the ruptured aneurysm had larger morphological parameters including size ratio (SR), aspect ratio (AR), aneurysm vessel angle (θF), Aneurysm inclination angle (θA), Undulation index (UI), Ellipticity index (EI), and Non-sphericity Index (NSI) than the unruptured aneurysm. And oscillatory shear index (OSI) is also larger. Higher rupture resemblance score (RRS) was shown in the ruptured aneurysm. In case 2, the aneurysm had one daughter sac after 2 years. Partial morphological and hemodynamic parameters including SR, AR, θF, θA, UI, EI, NSI, OSI, and relative residence time (RRT) increased, and normalized wall shear stress (NWSS) was significantly reduced. RRS increased during this period.

Conclusion

SR and OSI may have predictive values for the risk of intracranial aneurysm rupture. It is possible that WSS Changes before and after IA rupture, yet the influence of high or low WSS on growth and rupture of IA remains unclear. RRS is promising to be used in the clinical assessment of the rupture risk of IAs and to guide the formulation of treatment plans.

Endovascular treatment of multiple intracranial aneurysms in patients with subarachnoid hemorrhage: one or multiple sessions?

Objective

This study aimed to compare the safety and efficacy of single- and multiple-stage endovascular treatment in aneurysmal subarachnoid hemorrhage (SAH) patients with multiple intracranial aneurysms.

Methods

We retrospectively analyzed the clinical and imaging data of 61 patients who harbored multiple aneurysms and presented to our institution with aneurysmal subarachnoid hemorrhage. Patients were grouped according to endovascular treatment strategy: one-stage or multiple-stage.

Result

The 61 study patients harbored 136 aneurysms. One aneurysm in each patient had ruptured. In the one-stage treatment group, all 66 aneurysms in 31 patients were treated in one session. The mean follow-up was 25.8 months (range, 12-47). At the last follow-up, the modified Rankin scale was ≤2 in 27 patients. In total, 10 complications occurred (cerebral vasospasm, six patients; cerebral hemorrhage, two patients; and thromboembolism, two patients). In the multiple-stage treatment group, only the ruptured aneurysm (30 in total) was treated at the time of presentation, and the remaining aneurysms (40 in total) were treated later. The mean follow-up was 26.3 months (range, 7-49). At the last follow-up, the modified Rankin scale score was ≤2 in 28 patients. In total, five complications occurred (cerebral vasospasm, four patients; and subarachnoid hemorrhage, one patient). During the follow-up period, there was one recurrence of aneurysm with subarachnoid hemorrhage in the single-stage treatment group and four recurrences in the multiple-stage treatment group.

Conclusion

Both single- and multiple-stage endovascular treatment is safe and effective in aneurysmal subarachnoid hemorrhage patients who harbor multiple aneurysms. However, multiple-stage treatment is associated with a lower rate of hemorrhagic and ischemic complications.

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management

PURPOSE

Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps. Advanced tools for automatic aneurysm analysis are needed to transfer current research into clinical routine.

METHODS

We propose a pipeline for intracranial aneurysm analysis using deep learning-based mesh segmentation, automatic centerline and outlet detection and automatic generation of a semantic vessel graph. We use the semantic vessel graph for morphological analysis and an automatic rupture state classification.

RESULTS

The deep learning-based mesh segmentation can be successfully applied to aneurysm surface meshes. With the subsequent semantic graph extraction, additional morphological parameters can be extracted that take the whole vascular domain into account. The vessels near ruptured aneurysms had a slightly higher average torsion and curvature compared to vessels near unruptured aneurysms. The 3D surface models can be further employed for rupture state classification which achieves an accuracy of 83.3%.

CONCLUSION

The presented pipeline addresses several aspects of current research and can be used for aneurysm analysis with minimal user effort. The semantic graph representation with automatic separation of the aneurysm from the parent vessel is advantageous for morphological and hemodynamical parameter extraction and has great potential for deep learning-based rupture state classification.

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.

Identification of ruptured intracranial aneurysms using the aneurysm-specific prediction score in patients with multiple aneurysms with subarachnoid hemorrhages- a Chinese population based external validation study

Background

For patients with aneurysmal subarachnoid hemorrhages (SAHs) and multiple intracranial aneurysms (MIAs), a simple and fast imaging method that can identify ruptured intracranial aneurysms (RIAs) may have great clinical value. We sought to use the aneurysm-specific prediction score to identify RIAs in patients with MIAs and evaluate the aneurysm-specific prediction score.

Methods

Between May 2018 and May 2021, 134 patients with 290 MIAs were retrospectively analyzed. All patients had an SAH due to IA rupture. CT angiography (CTA) was used to assess the maximum diameter, shape, and location of IAs to calculate the aneurysm-specific prediction score. Then, the aneurysm-specific prediction score was applied to RIAs in patients with MIAs.

Results

The IAs with the highest aneurysm-specific prediction scores had not ruptured in 17 (12.7%) of the 134 patients with 290 MIAs. The sensitivity, specificity, false omission rate, diagnostic error rate, and diagnostic accuracy of the aneurysm-specific prediction score were higher than those of the maximum diameter, shape, and location of IAs.

Conclusions

The present study suggests that the aneurysm-specific prediction score has high diagnostic accuracy in identifying RIAs in patients with MIAs and SAH, but that it needs further evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02727-w.

Numerical Assessment of the Risk of Abnormal Endothelialization for Diverter Devices: Clinical Data Driven Numerical Study

Numerical modeling is an effective tool for preoperative planning. The present work is devoted to a retrospective analysis of neurosurgical treatments for the occlusion of cerebral aneurysms using flow-diverters and hemodynamic factors affecting stent endothelization. Several different geometric approaches have been considered for virtual flow-diverters deployment. A comparative analysis of hemodynamic parameters as a result of computational modeling has been carried out basing on the four clinical cases: one successful treatment, one with no occlusion and two with in stent stenosis. For the first time, a quantitative assessment of both: the limiting magnitude of shear stresses that are necessary for the occurrence of in stent stenosis (MaxWSS > 1.23) and for conditions in which endothelialization is insufficiently active and occlusion of the cervical part of the aneurysm does not occur (MaxWSS < 1.68)—has been statistacally proven (p < 0.01).

Can Endovascular Treatment of Fusiform Intracranial Aneurysms Restore the Healthy Hemodynamic Environment?–A Virtual Pilot Study

Numerous studies assess intracranial aneurysm rupture risk based on morphological and hemodynamic parameter analysis in addition to clinical information such as aneurysm localization, age, and sex. However, intracranial aneurysms mostly occur with a saccular shape located either lateral to the parent artery or at a bifurcation. In contrast, fusiform intracranial aneurysms (FIAs), i.e., aneurysms with a non-saccular, dilated form, occur in approximately 3–13% of all cases and therefore have not yet been as thoroughly studied. To improve the understanding of FIA hemodynamics, this pilot study contains morphological analyses and image-based blood flow simulations in three patient-specific cases. For a precise and realistic comparison to the pre-pathological state, each dilation was manually removed and the time-dependent blood flow simulations were repeated. Additionally, a validated fast virtual stenting approach was applied to evaluate the effect of virtual endovascular flow-diverter deployment focusing on relevant hemodynamic quantities. For two of the three patients, post-interventional information was available and included in the analysis. The results of this numerical pilot study indicate that complex flow structures, i.e., helical flow phenomena and the presence of high oscillating flow features, predominantly occur in FIAs with morphologically differing appearances. Due to the investigation of the individual healthy states, the original flow environment could be restored which serves as a reference for the virtual treatment target. It was shown that the realistic deployment led to a considerable stabilization of the individual hemodynamics in all cases. Furthermore, a quantification of the stent-induced therapy effect became feasible for the treating physician. The results of the morphological and hemodynamic analyses in this pilot study show that virtual stenting can be used in FIAs to quantify the effect of the planned endovascular treatment.

The Analysis of Morphoradiological Parameters in Predicting Risk of Basilar Artery Tip Aneurysm Rupture: A Retrospective Cohort Study

Purpose

Basilar artery tip aneurysms can have fatal consequences and poor prognostic outcomes in case of rupture. We investigated the standard parameters used to evaluate aneurysmal morphology to predict aneurysm rupture.

Patients and Methods

We measured the differences between ruptured and unruptured basilar bifurcation aneurysms in terms of morphological features, including aneurysm size, size ratio, aneurysm height, perpendicular height, aneurysm width, bottleneck ratio, aspect ratio, and present daughter domes, using 3D angiography.

Results

Among 34 patients, 21 (61.8%) and 13 (38.2%) had ruptured and unruptured basilar tip aneurysms, respectively. In the ruptured group, the mean aneurysm size, and aspect, size, and bottleneck ratios were 6.8 ± 1.9 mm, 3.2 ± 1.0, 2.3 ± 0.5, and 2.2 ± 0.4, respectively, compared to 4.5 ± 1.5 mm, 4.4 ± 2.0, 1.6 ± 0.4, and 2.7 ± 0.7, respectively, in the non-ruptured group. Univariate analysis showed a larger aneurysmal cross-sectional diameter (odds ratio [OR], 0.50; 95% confidence interval [CI], 0.5-5.392; p < 0.05), a larger size ratio (OR, 21.375; 95% CI, 3.283-139.177; p < 0.05), and presence of a daughter dome (OR, 72.0; 95% CI, 6.7-776.5; p < 0.05) with ruptured basilar artery tip aneurysms.

Conclusion

A larger aneurysm size and size ratio, and the presence of a daughter dome were significantly associated with increased rupture risk in basilar tip aneurysms.

Deep Shape Features for Predicting Future Intracranial Aneurysm Growth

Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support.

Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model.

Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively.

Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging.