Abstract
An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allowed to reduce the complex ATAA anatomy to a few shape modes, which were correlated to shear stress and aortic strain, as determined by computational analysis. Findings demonstrated that shape modes are associated to specific morphological features of aneurysmal aorta as the vessel tortuosity and local bulging of the ATAA. A predictive model, built with principal shape modes of the ATAA wall, achieved better performance in stratifying surgically operated ATAAs versus monitored ATAAs, with respect to a baseline model using the maximum aortic diameter. Using current imaging resources, this study demonstrated the potential of SSA to investigate the association between shape and function in ATAAs, with the goal of developing a personalized approach for the treatment of the severity of aneurysmal aorta.
Highlights
Diagnosis and risk stratification of ascending thoracic aortic aneurysms (ATAA) are primarily based on medical imaging analysis, predisposing factors and patient familiarities [1]
This study demonstrated the potential of statistical shape analysis (SSA) to investigate the association between shape and function in ATAAs, with the goal of developing a personalized approach for the treatment of the severity of aneurysmal aorta
These patterns of aortic dilatations are associated to different bicuspid aortic valve (BAV) phenotype, each leading to ATAAs with distinct clinical outcome
Summary
Diagnosis and risk stratification of ascending thoracic aortic aneurysms (ATAA) are primarily based on medical imaging analysis, predisposing factors and patient familiarities [1]. Clinical evidence has shown that BAV ATAA is a markedly heterogeneous entity, with aortic dilatation occurring in the aortic root, the tubular ascending aorta, the proximal aortic arch or any combination of these types of dilatations [6,7]. These patterns of aortic dilatations are associated to different BAV phenotype (i.e., the anterior-posterior cusp fusion or the right-left cusp fusion), each leading to ATAAs with distinct clinical outcome. Novel principles for risk stratification are emerging to overcome the paradox of the current clinical criterion, based on the maximum aortic diameter of the ATAA. Among novel approaches for risk stratification, computational modeling has shown promise in the estimation of ATAA by means of prediction of intramural stress [11] and shear stress [12,13,14]
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