Jiang, Jingfeng

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.

Introduction: In seniors, the prevalence of abdominal aortic aneurysms (AAA) is high (9-10%). We aim to characterize AAA growth rates (slow vs. rapid) to help inform management strategies, including individualized imaging surveillance frequency. Hypothesis: Modeling thrombosis may improve the characterization of AAAs' growth status. Methods: 3D geometrical AAA models with and without thrombosis (vessel lumen only) were generated for 70 human subjects using available contrast-enhanced CTA data. AAA growth rates were categorized as slow (< 5 mm/year) or rapid (≥ 5 mm/year) based on serial imaging. Python scripts were used to calculate geometrical parameters (83) with and without thrombosis. Patient-specific relevant health information was retrieved through a review of medical records. Support vector machine (SVM), a well-established machine learning method, was run with 10-fold cross-validation (100 iterations) to assess the predictive accuracy. Results: Among 70 AAAs studied, the ratio between rapidly-growing and slowly-growing was nearly 1:2. The combination of antihypertensive medication, coronary artery disease, juxta-aneurysm aortic size, and five geometrical parameters quantifying the extent of thrombosis provided the best accuracy for AAA's growth status. The area under receiving operating curve (AUROC) was 0.85, and total accuracy was 0.81, with 57% and 93% of rapidly- and slowly-growing AAAs correctly identified, respectively. Excluding patients with aneurysm thrombus, the AUROC and accuracy in predicting rapidly-growing AAAs decreased to 0.82 and 48%, respectively. Conclusions: machine-learning-based predictive modeling appears feasible for characterizing AAA's growth rate status. Manual aortic image segmentation, required for data analytics, is labor-intensive (on average, 1 hour). Leveraging artificial intelligence-based segmentation methodology may provide an automated solution to improve workflow and feasibility.