Quantitative Assessment of Abdominal Aortic Aneurysm Geometry

• Published in: Annals of biomedical engineering Vol. 39; no. 1; pp. 277 - 286
• Main Authors: Shum, Judy, Martufi, Giampaolo, Di Martino, Elena, Washington, Christopher B, Grisafi, Joseph, Muluk, Satish C, Finol, Ender A
• Format: Journal Article
• Language: English
• Published: Boston Boston : Springer US 01.01.2011; Springer US; Springer Nature B.V
• Subjects: Abdominal aortic aneurysm; Algorithms; Aorta, Abdominal - anatomy & histology; Aorta, Abdominal - diagnostic imaging; Aortic Aneurysm, Abdominal - diagnostic imaging; Aortic Aneurysm, Abdominal - pathology; Aortic Rupture - diagnostic imaging; Aortic Rupture - pathology; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Computed tomography; Computer Simulation; Engineering mechanics; Fastkroppsmekanik; Female; Geometry quantification; Humans; Machine learning; Male; Medical engineering; Medicinsk teknik; Models, Anatomic; Models, Cardiovascular; Other technology; Radiography; Rupture risk; Solid mechanics; TECHNOLOGY; TEKNIKVETENSKAP; Teknisk mekanik; Wall thickness; Övriga teknikvetenskaper; Rupture risk; Wall thickness; Geometry quantification; Abdominal aortic aneurysm; Machine learning
• ISSN: 0090-6964; 1573-9686; 1573-9686; 1521-6047
• DOI: 10.1007/s10439-010-0175-3

Recent studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) and expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that aneurysm morphology and wall thickness are more predictive of rupture risk and can be the deciding factors in the clinical management of the disease. A non-invasive, image-based evaluation of AAA shape was implemented on a retrospective study of 10 ruptured and 66 unruptured aneurysms. Three-dimensional models were generated from segmented, contrast-enhanced computed tomography images. Geometric indices and regional variations in wall thickness were estimated based on novel segmentation algorithms. A model was created using a J48 decision tree algorithm and its performance was assessed using ten-fold cross validation. Feature selection was performed using the χ²-test. The model correctly classified 65 datasets and had an average prediction accuracy of 86.6% (κ = 0.37). The highest ranked features were sac length, sac height, volume, surface area, maximum diameter, bulge height, and intra-luminal thrombus volume. Given that individual AAAs have complex shapes with local changes in surface curvature and wall thickness, the assessment of AAA rupture risk should be based on the accurate quantification of aneurysmal sac shape and size.