A.V. Gorina1

1 I.M. Sechenov First Moscow State Medical University (Sechenovskiy University) (Moscow, Russia)

1 gorina_a_v@staff.sechenov.ru

Intracranial aneurysms are a widespread cerebrovascular disease associated with high risk of death because of aneurysm’s rupture. The treatment strategy is directly determined by rupture risk assessment, while the risk associated with surgical intervention is often comparable to the natural risk of aneurysm rupture. It makes improvement of approaches to assessing the condition of cerebral vessels with aneurysm essential for treatment planning.

Current approaches to the assessment of cerebral vessels with intracranial aneurysms include two complementary domains: clinical and hemodynamic analysis. Clinical methods are based on morphological and demographic risk factors, as well as risk stratification scales used in clinical practice (PHASES scale). These methods are convenient for preliminary assessment. However their predictive value is limited because they don’t account for the patient-specific blood flow characteristics and local biomechanical conditions of the wall. The incorporation of additional morphological markers has improved risk estimation, but morphology alone remains insufficient for reliable rupture prediction, particularly in small aneurysms.

Hemodynamic assessment based on computational fluid dynamics (CFD) provides a quantitative description of blood flow behavior and its interaction with the vascular wall. Parameters such as wall shear stress (WSS), wall shear stress gradient (WSSG), time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT) and HOLMES are widely used to characterize aneurysm. These parameters allow deeper evaluation of the functional state of vessel by identifying recirculation zones, disturbed flow patterns and hemodynamic conditions associated with vascular wall remodeling. At the same time the interpretation of these parameters remains inconsistent because of methodological heterogeneity in model reconstruction, boundary conditions, segmentation accuracy and simplified assumptions in computational models.

The analysis demonstrates that improving rupture risk prediction requires integration of clinical, morphological and hemodynamic data within unified assessment framework. The development of such approach may provide the basis for more reliable rupture risk classification system and contribute to better-informed clinical decisions. Further progress in this field is associated with standardization of patient-specific modeling protocols, refinement of computational methods and validation of predictive models in large clinical cohorts.

Gorina A.V. Modern approaches to the assessment of cerebral vessels with intracranial aneurysms using clinical and hemodynamics methods // Biomedical Radioelectronics. 2026. V. 29. № 6. P. 28−33. DOI: https://doi.org/10.18127/j15604136-202606-06

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