Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

Geronzi, Leonardo; Martínez, Antonio Beltrán; Rochette, Michel; Yan, Kexin; Bel-Brunon, Aline; Haigron, Pascal; Escrig, Pierre; Tomasi, Jacques; Daniel, M Neumark; Lalande, Alain; Lin, Shih-Yang; Marín-Castrillón, Diana M.; Bouchot, Olivier; Porterie, Jean; Valentini, P; Biancolini, Marco Evangelos

doi:10.1016/j.compbiomed.2023.107052

Cited by 4 publications

(1 citation statement)

References 68 publications

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“…Multiple studies have identified aortic centerline tortuosity, mean centerline curvature, and cross-sectional eccentricity as risk factors for deleterious outcomes [9][10][11][12][13][14]. Others have applied statistical shape analysis (SSA), a mathematical approach to modeling shape variation in a population, to replicating clinical diagnoses, predicting surgical outcomes, and modeling rupture risk in aortic disease [15][16][17][18]. In a recent publication, we showed that projection of aortic anatomy into a space defined by size and the fluctuation in total curvature (δK), a measure of shape, differentiates aortas along the spectrum of growth and pathology [19].…”

Section: Introductionmentioning

confidence: 99%

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution

Khabaz,

Kim,

Milner

et al. 2024

Preprint

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The human aorta undergoes complex morphologic changes that indicate the evolution of disease. Finite element analysis enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into finite element analysis (FEA) to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing pathologic growth drives morphologic heterogeneity. A shape-size geometric feature space plotting the variance of the shape index versus the inverse square root of aortic surface area (δ𝒮 vs.) quantitatively demonstrates the simulated breakdown in aortic shape. An increase in δ𝒮 closely parallels the true geometric progression of aortic disease patients.

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Section: Introductionmentioning

confidence: 99%

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution

Khabaz,

Kim,

Milner

et al. 2024

Preprint

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Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics

Rezaeitaleshmahalleh,

Lyu,

et al. 2023

Sci Rep

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Aneurysm hemodynamics is known for its crucial role in the natural history of abdominal aortic aneurysms (AAA). However, there is a lack of well-developed quantitative assessments for disturbed aneurysmal flow. Therefore, we aimed to develop innovative metrics for quantifying disturbed aneurysm hemodynamics and evaluate their effectiveness in predicting the growth status of AAAs, specifically distinguishing between fast-growing and slowly-growing aneurysms. The growth status of aneurysms was classified as fast (≥ 5 mm/year) or slow (< 5 mm/year) based on serial imaging over time. We conducted computational fluid dynamics (CFD) simulations on 70 patients with computed tomography (CT) angiography findings. By converting hemodynamics data (wall shear stress and velocity) located on unstructured meshes into image-like data, we enabled spatial pattern analysis using Radiomics methods, referred to as "Hemodynamics-informatics" (i.e., using informatics techniques to analyze hemodynamic data). Our best model achieved an AUROC of 0.93 and an accuracy of 87.83%, correctly identifying 82.00% of fast-growing and 90.75% of slowly-growing AAAs. Compared with six classification methods, the models incorporating hemodynamics-informatics exhibited an average improvement of 8.40% in AUROC and 7.95% in total accuracy. These preliminary results indicate that hemodynamics-informatics correlates with AAAs' growth status and aids in assessing their progression.

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Computational fluid dynamics in cardiac surgery and perfusion: A review

Catalano,

Crascì,

Puleo

et al. 2024

Perfusion

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Cardiovascular diseases persist as a leading cause of mortality and morbidity, despite significant advances in diagnostic and surgical approaches. Computational Fluid Dynamics (CFD) represents a branch of fluid mechanics widely used in industrial engineering but is increasingly applied to the cardiovascular system. This review delves into the transformative potential for simulating cardiac surgery procedures and perfusion systems, providing an in-depth examination of the state-of-the-art in cardiovascular CFD modeling. The study first describes the rationale for CFD modeling and later focuses on the latest advances in heart valve surgery, transcatheter heart valve replacement, aortic aneurysms, and extracorporeal membrane oxygenation. The review underscores the role of CFD in better understanding physiopathology and its clinical relevance, as well as the profound impact of hemodynamic stimuli on patient outcomes. By integrating computational methods with advanced imaging techniques, CFD establishes a quantitative framework for understanding the intricacies of the cardiac field, providing valuable insights into disease progression and treatment strategies. As technology advances, the evolving synergy between computational simulations and clinical interventions is poised to revolutionize cardiovascular care. This collaboration sets the stage for more personalized and effective therapeutic strategies. With its potential to enhance our understanding of cardiac pathologies, CFD stands as a promising tool for improving patient outcomes in the dynamic landscape of cardiovascular medicine.

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Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

Cited by 4 publications

References 68 publications

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution

Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics

Computational fluid dynamics in cardiac surgery and perfusion: A review

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