Shape irregularity of the intracranial aneurysm lumen exhibits diagnostic value

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.

MRI of unruptured infectious intracranial aneurysms in infective endocarditis. A case-control study

PURPOSE

To evaluate the usefulness of T2* and FLAIR sequences in the detection of unruptured infectious intracranial aneurysms (UIIAs) in infective endocarditis (IE) including the relationships between the lesion patterns within subarachnoid spaces and the presence of UIIA.

METHODS

Retrospective review of 15 consecutive patients with definite IE undergoing MR imaging (FLAIR, T2*, DWI, CE-MRA, 3D-T1, CE-3DT1 sequences), in whom DSA detected infectious intracranial aneurysms (IIA). Aneurysmal features (diameter, location, morphology on DSA) and signal patterns onT2*, FLAIR and conventional MR sequences at the site of the UIIA, follow-up MRI and IE background, were analyzed. A control-group of 15 IE-patients without IIA at DSA served for comparison.

RESULTS

Among 17 UIIAs studied, T2* sequence displayed a susceptibility vessel sign in 15/17 (88.2%), both distal and proximal, which matched with the IIA visualized on DSA. Three patterns of hyposignal areas were identified: (a) signet-ring or target-sign appearance (n = 7), (b) homogeneous, round-, oval- or pear-shaped area (n = 4), and (c) heterogeneous area (n = 4). A FLAIR hyperintensity of the lumen and of the adjacent cortex was present in 6 (35.3%) and 9 (53%) UIIAs, respectively. On T1 (12 UIIAs) a rounded hyposignal (n = 2), within the UIIA lumen matched with the FLAIR hypersignal. Using both T2* and FLAIR had an incremental value with 100% sensitivity and specificity.

CONCLUSION

The susceptibility vessel sign is an MR imaging pattern frequently observed at the site of UIIAs in IE-patients. Both T2* and FLAIR may have the potential to depict UIIAs, regardless of their location and shape.

Differences Between Ruptured Aneurysms With and Without Blebs: Mechanistic Implications

PURPOSE

Blebs are known risk factors for intracranial aneurysm (IA) rupture. We analyzed differences between IAs that ruptured with blebs and those that ruptured without developing blebs to identify distinguishing characteristics among them and suggest possible mechanistic implications.

METHODS

Using image-based models, 25 hemodynamic and geometric parameters were compared between ruptured IAs with and without blebs (n = 673), stratified by location. Hemodynamic and geometric differences between bifurcation and sidewall aneurysms and for aneurysms at five locations were also analyzed.

RESULTS

Ruptured aneurysms harboring blebs were exposed to higher flow conditions than aneurysms that ruptured without developing blebs, and this was consistent across locations. Bifurcation aneurysms were exposed to higher flow conditions than sidewall aneurysms. They had larger maximum wall shear stress (WSS), more concentrated WSS distribution, and larger numbers of critical points than sidewall aneurysms. Additionally, bifurcation aneurysms were larger, more elongated, and had more distorted shapes than sidewall aneurysms. Aneurysm morphology was associated with aneurysm location (p < 0.01). Flow conditions were different between aneurysm locations.

CONCLUSION

Aneurysms at different locations are likely to develop into varying morphologies and thus be exposed to diverse flow conditions that may predispose them to follow distinct pathways towards rupture with or without bleb development. This could explain the diverse rupture rates and bleb presence in aneurysms at different locations.

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.

Clinical Usage of Different Doses of Cis-Atracurium in Intracranial Aneurysm Surgery and Its Effect on Motor-Evoked Potentials

The objective of this work was to investigate the effect of different doses of cis-atracurium on patients undergoing general anesthesia induction (GAI) during intracranial aneurysm surgery (ICAS). In this work, 90 patients who underwent ICAS under the elective motor-evoked potential (MEP) monitoring in the First Affiliated Hospital of Northwest University (Xi'an No. 1 Hospital) from January 2021 to May 2022 were enrolled as the research objects. Randomly, they were rolled into a S1 group (30 cases, 2 times 95% effective dose (ED95) cis-atracurium), a S2 group (30 cases, 3 times ED95 cis-atracurium), and a S3 group (30 cases, 4 times ED95 cis-atracurium). The endotracheal intubation conditions, the train-of-four (TOF) rate (TOFR), body movement, and spontaneous breathing were compared among the three groups of patients. The results showed that the MEP inhibition time of the patients in the S3 group was much longer than that of the S1 and S2 groups, but it showed no significant difference between the S1 group and S2 group (P > 0.05). The good rates of endotracheal intubation conditions in the S2 group (100%) and S3 group (100%) were obviously higher than the rate in the S1 group (43.33%). The TOFRs of patients in S2 and S3 groups at time t2 and t3 were lower obviously to that at time t0, while the TOFRs of patients in S3 group at time t2 and t3 were still lower in contrast to the S2 group (P < 0.05). The mean arterial pressure (MAP) and heart rate (HR) of patients in all groups were lower at t1, t2, and t3 than at t0 (P < 0.05), while the differences among different groups were not remarkable (P > 0.05). Finally, using 3 times ED95 cis-atracurium for GAI could reduce the risk of intraoperative body movement and spontaneous breathing, as well as the residual degree of muscle relaxation, in patients with ICAS, without affecting MEP monitoring, improving endotracheal intubation conditions, and increasing safety during open neurosurgery operations.

An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs

Background

The rupture risk assessment of intracranial aneurysms (IAs) is clinically relevant. How to accurately assess the rupture risk of IAs remains a challenge in clinical decision-making.

Purpose

We aim to build an integrated model to improve the assessment of the rupture risk of IAs.

Materials and Methods

A total of 148 (39 ruptured and 109 unruptured) IA subjects were retrospectively computed with computational fluid dynamics (CFDs), and the integrated models were proposed by combining machine learning (ML) and deep learning (DL) algorithms. ML algorithms that include random forest (RF), k-nearest neighbor (KNN), XGBoost (XGB), support vector machine (SVM), and LightGBM were, respectively, adopted to classify ruptured and unruptured IAs. A Pointnet DL algorithm was applied to extract hemodynamic cloud features from the hemodynamic clouds obtained from CFD. Morphological variables and hemodynamic parameters along with the extracted hemodynamic cloud features were acted as the inputs to the classification models. The classification results with and without hemodynamic cloud features are computed and compared.

Results

Without consideration of hemodynamic cloud features, the classification accuracy of RF, KNN, XGB, SVM, and LightGBM was 0.824, 0.759, 0.839, 0.860, and 0.829, respectively, and the AUCs of them were 0.897, 0.584, 0.892, 0.925, and 0.890, respectively. With the consideration of hemodynamic cloud features, the accuracy successively increased to 0.908, 0.873, 0.900, 0.926, and 0.917. Meanwhile, the AUCs reached 0.952, 0.881, 0.950, 0.969, and 0.965 eventually. Adding consideration of hemodynamic cloud features, the SVM could perform best with the highest accuracy of 0.926 and AUC of 0.969, respectively.

Conclusion

The integrated model combining ML and DL algorithms could improve the classification of IAs. Adding consideration of hemodynamic cloud features could bring more accurate classification, and hemodynamic cloud features were important for the discrimination of ruptured IAs.

Shape Trumps Size: Image-Based Morphological Analysis Reveals That the 3D Shape Discriminates Intracranial Aneurysm Disease Status Better Than Aneurysm Size

Background

To date, it remains difficult for clinicians to reliably assess the disease status of intracranial aneurysms. As an aneurysm's 3D shape is strongly dependent on the underlying formation processes, it is believed that the presence of certain shape features mirrors the disease status of the aneurysm wall. Currently, clinicians associate irregular shape with wall instability. However, no consensus exists about which shape features reliably predict instability. In this study, we present a benchmark to identify shape features providing the highest predictive power for aneurysm rupture status.

Methods

3D models of aneurysms were extracted from medical imaging data (3D rotational angiographies) using a standardized protocol. For these aneurysm models, we calculated a set of metrics characterizing the 3D shape: Geometry indices (such as undulation, ellipticity and non-sphericity); writhe- and curvature-based metrics; as well as indices based on Zernike moments. Using statistical learning methods, we investigated the association between shape features and aneurysm disease status. This processing was applied to a clinical dataset of 750 aneurysms (261 ruptured, 474 unruptured) registered in the AneuX morphology database. We report here statistical performance metrics [including the area under curve (AUC)] for morphometric models to discriminate between ruptured and unruptured aneurysms.

Results

The non-sphericity index NSI (AUC = 0.80), normalized Zernike energies ZNsurf (AUC = 0.80) and the modified writhe-index W¯meanL1 (AUC = 0.78) exhibited the strongest association with rupture status. The combination of predictors further improved the predictive performance (without location: AUC = 0.82, with location AUC = 0.87). The anatomical location was a good predictor for rupture status on its own (AUC = 0.78). Different protocols to isolate the aneurysm dome did not affect the prediction performance. We identified problems regarding generalizability if trained models are applied to datasets with different selection biases.

Conclusions

Morphology provided a clear indication of the aneurysm disease status, with parameters measuring shape (especially irregularity) being better predictors than size. Quantitative measurement of shape, alone or in conjunction with information about aneurysm location, has the potential to improve the clinical assessment of intracranial aneurysms.

Intracranial aneurysm wall (in)stability-current state of knowledge and clinical perspectives

Intracranial aneurysm (IA), a local outpouching of cerebral arteries, is present in 3 to 5% of the population. Once formed, an IA can remain stable, grow, or rupture. Determining the evolution of IAs is almost impossible. Rupture of an IA leads to subarachnoid hemorrhage and affects mostly young people with heavy consequences in terms of death, disabilities, and socioeconomic burden. Even if the large majority of IAs will never rupture, it is critical to determine which IA might be at risk of rupture. IA (in)stability is dependent on the composition of its wall and on its ability to repair. The biology of the IA wall is complex and not completely understood. Nowadays, the risk of rupture of an IA is estimated in clinics by using scores based on the characteristics of the IA itself and on the anamnesis of the patient. Classification and prediction using these scores are not satisfying and decisions whether a patient should be observed or treated need to be better informed by more reliable biomarkers. In the present review, the effects of known risk factors for rupture, as well as the effects of biomechanical forces on the IA wall composition, will be summarized. Moreover, recent advances in high-resolution vessel wall magnetic resonance imaging, which are promising tools to discriminate between stable and unstable IAs, will be described. Common data elements recently defined to improve IA disease knowledge and disease management will be presented. Finally, recent findings in genetics will be introduced and future directions in the field of IA will be exposed.