Zhang, Xiaoming

Introduction: Abdominal aortic aneurysms (AAA) are common among individuals over age 65 with a prevalence of 9%. Most AAAs are small and require surveillance imaging to monitor size and growth rates. Tools capable of predicting growth rates would be clinically useful. Hypothesis: We investigated how computational hemodynamics derived from CT angiography (CTA) differentiated slow and rapid-growing AAAs. Methods: Using readily available computing power in the clinical workflow, 3D models were generated for 25 subjects with AAA from available 3D CTA data. AAA growth rates were categorized as slow ( less than 5 mm/year) or rapid (greater than 5 mm/year) based on serial imaging. A geometrical analysis was performed using 7 indices (Volume, Surface Area, Length, Max/Min radius, Expansion Ratio [Mean radius/vessel radius], Proximal vessel area, Voronoi diagram-based metrics) for each AAA. Computational hemodynamics were performed using commercial software (ANSYS-Fluent). Computational hemodynamics were completed for all cases (see Fig. 1). In-house software assessed wall shear stress-(WSS) and vortex-based hemodynamics. Support vector machine (SVM) was run with cross-validation (50 iterations) to assess predictive strength using these 16 parameters. Results: Among 25 AAAs studied, 7 were rapid and 18 were slow-growing. The combination of proximal vessel area, spatiotemporally averaged WSS, and spatially averaged OSI provided the best accuracy for growth differentiation. The area under receiving operating curve (AUROC) and total accuracy are 0.770 and 0.765, respectively, with 48% and 86% of rapid and slow-growing AAAs correctly identified, respectively. Conclusions: Computational hemodynamics appears feasible with reasonable growth rate prediction. With optimization, this tool may enable resource utilization refinement for imaging surveillance strategies by distinguishing rapid from slow-growing AAAs.