Literature DB >> 32228297

Combining Volumetric and Wall Shear Stress Analysis from CT to Assess Risk of Abdominal Aortic Aneurysm Progression.

Olivier Meyrignac1, Laurence Bal1, Charline Zadro1, Adrien Vavasseur1, Anou Sewonu1, Marine Gaudry1, Bertrand Saint-Lebes1, Mariangela De Masi1, Paul Revel-Mouroz1, Agnès Sommet1, Jean Darcourt1, Anne Negre-Salvayre1, Alexis Jacquier1, Jean-Michel Bartoli1, Philippe Piquet1, Hervé Rousseau1, Ramiro Moreno1.   

Abstract

Background Despite known limitations, the decision to operate on abdominal aortic aneurysm (AAA) is primarily on the basis of measurement of maximal aneurysm diameter. Purpose To identify volumetric and computational fluid dynamics parameters to predict AAAs that are likely to progress in size. Materials and Methods This study, part of a multicenter prospective registry (NCT01599533), included 126 patients with AAA. Patients were sorted into stable (≤10-mL increase in aneurysm volume) and progression (>10-mL increase in aneurysm volume) groups. Initial AAA characteristics of the derivation cohort were analyzed (maximal diameter and surface, thrombus and lumen volumes, maximal wall pressure, and wall shear stress [WSS]) to identify relevant parameters for a logistic regression model. Model and maximal diameter diagnostic performances were assessed in both cohorts and for AAAs smaller than 50 mm by using area under the receiver operating characteristic curve (AUC). Results Eighty-one patients were included (mean age, 73 years ± 7 years [standard deviation]; 78 men). The derivation and validation cohorts included, respectively, 50 and 31 participants. In the derivation cohort, there was higher mean lumen volume and lower mean WSS in the progression group compared with the stable group (60 mL ± 14 vs 46 mL ± 18 [P = .005] and 66% ± 6 vs 53% ± 9 [P = .02], respectively). Mean lumen volume and mean WSS at baseline were correlated to total volume growth (r = 0.47 [P = .002] and -0.42 [P = .006], respectively). In the derivation cohort, a regression model including lumen volume and WSS to predict aneurysm enlargement was superior to maximal diameter alone (AUC, 0.78 vs 0.52, respectively; P = .003); although no difference was found in the validation cohort (AUC, 0.79 vs 0.71, respectively; P = .51). For AAAs smaller than 50 mm, a regression model that included both baseline WSS and lumen volume performed better than maximal diameter (AUC, 0.79 vs 0.53, respectively; P = .01). Conclusion Combined analysis of lumen volume and wall shear stress was associated with enlargement of abdominal aortic aneurysms at 1 year, particularly in aneurysms smaller than 50 mm in diameter. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Mitsouras and Leach in this issue.

Entities:  

Year:  2020        PMID: 32228297     DOI: 10.1148/radiol.2020192112

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  5 in total

1.  Hemostatic Biomarkers and Volumetry Help to Identify High-Risk Abdominal Aortic Aneurysms.

Authors:  Sebastian Fernandez-Alonso; Esther Martinez-Aguilar; Susana Ravassa; Josune Orbe; Jose A Paramo; Leopoldo Fernandez-Alonso; Carmen Roncal
Journal:  Life (Basel)       Date:  2022-05-31

2.  Enlarged Lumen Volume of Proximal Aortic Segment and Acute Type B Aortic Dissection: A Computer Fluid Dynamics Study of Ideal Aortic Models.

Authors:  Yuan Peng; Xuelan Zhang; Jiehua Li; Xiaolong Zhang; Hao He; Xin Li; Kun Fang; Liancun Zheng; Chang Shu
Journal:  Int J Gen Med       Date:  2022-01-13

3.  Expanding the Radiologist's Arsenal against Abdominal Aortic Aneurysms, a Versatile Adversary.

Authors:  Dimitrios Mitsouras; Joseph R Leach
Journal:  Radiology       Date:  2020-03-31       Impact factor: 29.146

4.  Numerical simulation of time-resolved 3D phase-contrast magnetic resonance imaging.

Authors:  Thomas Puiseux; Anou Sewonu; Ramiro Moreno; Simon Mendez; Franck Nicoud
Journal:  PLoS One       Date:  2021-03-26       Impact factor: 3.240

5.  Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms.

Authors:  Moritz Lindquist Liljeqvist; Marko Bogdanovic; Antti Siika; T Christian Gasser; Rebecka Hultgren; Joy Roy
Journal:  Sci Rep       Date:  2021-09-10       Impact factor: 4.379

  5 in total

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