Quantitative Assessment of Abdominal Aortic Aneurysm Geometry
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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.
KeywordsRupture risk Geometry quantification Abdominal aortic aneurysm Machine learning Wall thickness
The authors would like to acknowledge the research funding from the Bill and Melinda Gates Foundation, Carnegie Mellon University’s Biomedical Engineering Department, the John and Claire Bertucci Graduate Fellowship program, and NIH grants R21EB007651, R21EB008804, and R15HL087268. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr. Carolyn Rose’s assistance in performing the statistical analysis is also gratefully acknowledged.
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