title: Beyond fusiform and saccular: a novel quantitative tortuosity index may help classify aneurysm shape and predict aneurysm rupture potential.
abstract: While saccular abdominal aortic aneurysms (AAAs) are thought to be more prone to rupture than fusiform aneurysms, attempts to validate this observation have been limited by the inability to quantitatively define the three-dimensional shape of an aorta. A quantitative three-dimensional shape model may distinguish among shape classes and ultimately be useful in identifying aneurysms at risk for rupture. Three-dimensional luminal surface data of AAAs were generated from computed tomographic (CT) images of 15 patients with small aneurysms (< or =5.5 cm maximal transverse diameter). The centerline was used to construct a shape classification based upon the orthographic projection of the centerline about its central axis. The extent and direction of the individual deviations were quantified as areas on the plane of projection to create a shape classification. Hierarchical cluster analysis was used to verify distinct shape classes. A tortuosity index was calculated as a function of the centerline projection. AAA shape was calculated as a tortuosity index and classified into distinct classes of minimal or increased three-dimensional tortuosity. Thrombus could change the tortuosity index or shape classification of an aneurysm. In several patients with serial CT scans, the tortuosity index changed over time and was correlated with rupture; in three AAAs that ruptured the mean tortuosity increased 29% whereas the mean transverse diameter increased 3.3%. Expanding AAAs develop specific, quantifiable shapes that can be expressed as a quantitative tortuosity index that may be relevant to their natural history. The three-dimensional features of this shape model provide a novel and potentially clinically relevant adjunct to maximal transverse diameter. Larger studies are needed to correlate the tortuosity index with finite element models and the ability to predict aneurysm rupture.
authors.affiliation: Department of Surgery, Yale University School of Medicine, New Haven, CT, USA.
authors.forename: Suguna
authors.initials: S
authors.lastname: Pappu
authors.forename: Alan
authors.initials: A
authors.lastname: Dardik
authors.forename: Hemant
authors.initials: H
authors.lastname: Tagare
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Gusberg
mesh_terms: D000465:Algorithms; D017544:Aortic Aneurysm, Abdominal / Q000145:classification / Q000150:complications / Q000000981:diagnostic imaging*; D001019:Aortic Rupture / Q000000981:diagnostic imaging / Q000209:etiology*; D001027:Aortography / Q000379:methods*; D016000:Cluster Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional; D008137:Longitudinal Studies; D008955:Models, Cardiovascular; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D014057:Tomography, X-Ray Computed