[Unruptured cerebral aneurysms]. / Nerazorvavshiesia tserebral'nye anevrizmy.

The problem of high disability and mortality due to subarachnoid hemorrhage (SAH) from cerebral aneurysms has led to the fact that surgical treatment of unruptured aneurysms has been considered in Western Europe, Japan, and the United States as a method for prevention of subarachnoid hemorrhage for many years. The introduction of the so-called surgical SAH prophylaxis in Russia is a challenge that requires defining the principles of patient selection and choosing a treatment technique for unruptured cerebral aneurysms. The article highlights the modern approaches to treatment of asymptomatic unruptured cerebral aneurysms based on the concepts of aneurysm epidemiology and natural progression.

Risk analysis of intracranial aneurysm rupture based on the arterial segment of origin.

Background and objective: The rupture risk of intracranial aneurysms (IAs) is related to their arterial origin, but whether the different segments of the artery have different risks and act as independent risk factors is still unknown. Our study aimed to investigate the rupture risk of IAs in different arterial segments in a large Chinese cohort. Methods: Imaging and clinical data of consecutive patients with IAs diagnosed by Computed Tomography angiography (CTA) from January 2013 to December 2022 were collected. Two neuroradiologists independently identified ruptured and unruptured IAs based on imaging and medical records. The internal carotid artery (ICA), middle cerebral artery (MCA), anterior cerebral artery (ACA), vertebral artery (VA), and posterior cerebral artery (PCA) were segmented according to the Bouthillier and Fischer segmentation methods. Stenoses of the proximal parent vessel were evaluated and documented. The Institutional Review Board (IRB) at Beijing Tiantan Hospital approved this retrospective study. Results: A total of 3,837 aneurysms {median size 3.5 mm [interquartile range (IQR) 2.6-5.1 mm]; 532 ruptured} were included in this study from 2,968 patients [mean age: 57 years (IQR 50-64); male patients: 1,153]. Ruptured aneurysms were most commonly located in the posterior inferior cerebellar artery (PICA) (52.9%), anterior communicating artery (ACoA) (33.8%), other locations (33.3%), ACA (22.4%), and basilar artery (BA) (21.4%). The locations with the highest likelihood of rupture were the C7 ICA (21.3%), M2 MCA (24.0%), distal MCA (25.0%), and A2 ACA (28.1%). IAs originating from the C7 (p < 0.001), dM1 (p = 0.022), and dA1 (p = 0.021) segments were independent risk factors for rupture. IAs without stenosis of the proximal parent vessel were associated with a higher risk of rupture (p = 0.023). Conclusion: There are unique associations between the origins of aneurysms from various arterial segments. Aneurysms originating from the anterior communicating artery (ACoA), BA, PICA, A2, dA, C7, and M2 indicate a higher risk of rupture. Aneurysms originating from C4, C5, and C6 indicate a lower risk of rupture. C7 IAs, ACoA IAs, and PICA IAs seem to be independent risk factors.

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.

How does hemodynamics affect rupture tissue mechanics in abdominal aortic aneurysm: Focus on wall shear stress derived parameters, time-averaged wall shear stress, oscillatory shear index, endothelial cell activation potential, and relative residence time.

An abdominal aortic aneurysm (AAA) is a critical health condition with a risk of rupture, where the diameter of the aorta enlarges more than 50% of its normal diameter. The incidence rate of AAA has increased worldwide. Currently, about three out of every 100,000 people have aortic diseases. The diameter and geometry of AAAs influence the hemodynamic forces exerted on the arterial wall. Therefore, a reliable assessment of hemodynamics is crucial for predicting the rupture risk. Wall shear stress (WSS) is an important metric to define the level of the frictional force on the AAA wall. Excessive levels of WSS deteriorate the remodeling mechanism of the arteries and lead to abnormal conditions. At this point, WSS-related hemodynamic parameters, such as time-averaged WSS (TAWSS), oscillatory shear index (OSI), endothelial cell activation potential (ECAP), and relative residence time (RRT) provide important information to evaluate the shear environment on the AAA wall in detail. Calculation of these parameters is not straightforward and requires a physical understanding of what they represent. In addition, computational fluid dynamics (CFD) solvers do not readily calculate these parameters when hemodynamics is simulated. This review aims to explain the WSS-derived parameters focusing on how these represent different characteristics of disturbed hemodynamics. A representative case is presented for spatial and temporal formulation that would be useful for interested researchers for practical calculations. Finally, recent hemodynamics investigations relating WSS-related parameters with AAA rupture risk assessment are presented. This review will be useful to understand the physical representation of WSS-related parameters in cardiovascular flows and how they can be calculated practically for AAA investigations.

The Ratio of the Size of the Abdominal Aortic Aneurysm to That of the Unchanged Aorta as a Risk Factor for Its Rupture.

BACKGROUND: A ruptured abdominal aortic aneurysm is a severe condition associated with high mortality. Currently, the most important criterion used to estimate the risk of its rupture is the size of the aneurysm, but due to patients' anatomical variability, many aneurysms have a high risk of rupture with a small aneurysm size. We asked ourselves whether individual differences in anatomy could be taken into account when assessing the risk of rupture. METHODS: Based on the CT scan image, aneurysm and normal aorta diameters were collected from 186 individuals and compared in patients with ruptured and unruptured aneurysms. To take into account anatomical differences between patients, diameter ratios were calculated by dividing the aneurysm diameter by the diameter of the normal aorta at various heights, and then further comparisons were made. RESULTS: It was found that the calculated ratios differ between patients with ruptured and unruptured aneurysms. This observation is also present in patients with small aneurysms, with its maximal size below the level that indicates the need for surgical treatment. For small aneurysms, the ratios help us to estimate the risk of rupture better than the maximum sac size (AUC: 0.783 vs. 0.650). CONCLUSIONS: The calculated ratios appear to be a valuable feature to indicate which of the small aneurysms have a high risk of rupture. The obtained results suggest the need for further confirmation of their usefulness in subsequent groups of patients.

Predicting the risk of rupture for vertebral aneurysm based on geometric features of blood vessels.

A significant proportion of the adult population worldwide suffers from cerebral aneurysms. If left untreated, aneurysms may rupture and lead to fatal massive internal bleeding. On the other hand, treatment of aneurysms also involve significant risks. It is desirable, therefore, to have an objective tool that can be used to predict the risk of rupture and assist in surgical decision for operating on the aneurysms. Currently, such decisions are made mostly based on medical expertise of the healthcare team. In this paper, we investigate the possibility of using machine learning algorithms to predict rupture risk of vertebral artery fusiform aneurysms based on geometric features of the blood vessels surrounding but excluding the aneurysm. For each of the aneurysm images (12 ruptured and 25 unruptured), the vessel is segmented into distal and proximal parts by cross-sectional area and 382 non-aneurysm-related geometric features extracted. The decision tree model using two of the features (standard deviation of eccentricity of proximal vessel, and diameter at the distal endpoint) achieved 83.8% classification accuracy. Additionally, with support vector machine and logistic regression, we also achieved 83.8% accuracy with another set of two features (ratio of mean curvature between distal and proximal parts, and diameter at the distal endpoint). Combining the aforementioned three features with integration of curvature of proximal vessel and also ratio of mean cross-sectional area between distal and proximal parts, these models achieve an impressive 94.6% accuracy. These results strongly suggest the usefulness of geometric features in predicting the risk of rupture.

Perfusion computed tomography imaging of abdominal aortic aneurysms may be of value for patient specific rupture risk estimation.

Abdominal aortic aneurysm (AAA) continues to pose a significant cause of unexpected mortality in the developed countries with its incidence constantly rising. The indication of elective surgical repair is currently based on the maximum diameter and growth rate criteria which represent an oversimplification of the Law of Laplace stating that the stress exerted in a cylinder or sphere is proportional to its radius. These criteria fail to capture the complex pathophysiology of the aneurismal disease thus often leading to therapeutic inaccuracies (treating large AAAs with a very low actual rupture risk while observing smaller ones with a much greater risk). Aneurysmal disease is mainly a degenerative process leading to loss of structural integrity of the diseased aortic wall which cannot withhold the stresses due to systemic pressurization. Moreover aortic wall degeneration has been shown to be a localized phenomenon and rupture depends on the pointwise comparison of strength and stress rather than a global aortic wall weakening. Ex-vivo mechanical studies have related vessel wall hypoxia to loss of structural endurance and reduced wall strength. Therefore a module to capture in vivo variation of aortic wall blood supply and oxygenation would be of value for the evaluation of AAA rupture risk. Perfusion computed tomography (PCT) imaging represents a novel technique which has been already used to estimate tissue vascularity in several clinical conditions but not aneurismal disease. We hypothesize that PCT could be used as an adjunct tool during AAA diagnostics in order to evaluate aortic wall oxygenation in vivo, therefore providing a possible means to identify weak spots making the lesion amenable to rupture.

Evaluation of aortic stiffness (aortic pulse-wave velocity) before and after elective abdominal aortic aneurysm repair procedures: a pilot study.

BACKGROUND: The main clinical criterion for abdominal aortic aneurysm (AAA) repair operations is an AAA diameter >/=5.5 cm. When AAAs increase in size, specific changes occur in the mechanical properties of the aortic wall. Pulse-wave velocity (PWV) has been used as an indicator of vascular stiffness. A low PWV may predict AAA rupture risk and is an early predictor of cardiovascular mortality. METHODS: We investigated the prognostic value of PWV before and after elective AAA repair procedures. Twenty four patients scheduled for an open AAA repair underwent a preoperative carotid-femoral aortic PWV measurement. A second aortic PWV measurement was carried out 6 months postoperatively. RESULTS: The mean aortic PWV increased from 7.84 +/- 1.85 preoperatively to 10.08 +/- 1.57 m/sec 6 months postoperatively (mean change: 2.25; 95% confidence interval 1.4 to 3.1 m/sec; p<0.0001). The preprocedural PWV measurement did not correlate with AAA diameter (Spearman's rank correlation coefficient rho=0.12; p=0.59). CONCLUSIONS: Whether the increase in aortic PWV postoperatively suggests a decreased cardiovascular risk following AAA repair remains to be established. Aortic PWV should also be investigated as an adjunct tool for assessing AAA rupture risk.