Abstract Body: Mechanical thrombectomy (MT) is an established treatment for anterior circulation stroke due to large vessel occlusion. Multiple endovascular approaches exist to reach the same target, yet determining the best route remains unclear. Operators often rely on subjective and qualitative factors in their decision-making due to a lack of quantitative data. The primary navigation challenge stems from the interaction between devices (catheters, guidewires) and arterial anatomy. This study uses computational modeling to simulate these interactions, aiming to identify anatomical features that contribute most to procedural difficulty. We evaluated navigation tortuosity in anterior MT cases by comparing vessel centerlines derived from imaging (TI-tortuosity index) with computationally simulated device navigation in 107 patient anatomies. Twelve pathways were analyzed by considering femoral and radial approaches targeting the right or left carotid terminus (RCA, LCA) and the right or left vertebral arteries (RVert, LVert). Finite element modeling was employed to simulate device navigation with two primary metrics: local bending energy (LBE—representing the acuteness of a 2 cm segment of the path highlighting local curvature) and device tortuosity index (dTI) to measure overall tortuosity of the simulated device. Cases included surgical notes detailing procedure duration like time to first pass (FP) and Likert scores of operator-reported navigation difficulty. The relationship between case difficulty and computed metrics was assessed using Kendall’s tau. Vessel centerline metrics varied based on approach and target vessel. Time to FP showed the strongest associations with case difficulty, followed by LBE. These associations improved when utilizing the simulated device TI. Device simulation metrics outperformed vessel centerline metrics in predicting case difficulty. However, no metric—whether from imaging or simulation—outperformed time to FP in predicting case difficulty. Our findings underscore the variability in centerline metrics based on anatomy and choice of arterial access. While time to FP remains strongly correlated with procedural difficulty, centerline metrics can be obtained preoperatively to predict navigation challenges. Notably, simulated device metrics demonstrate superior predictive performance compared to traditional vessel centerlines, suggesting that endovascular simulations could be crucial for preoperative planning to assess navigation difficulty.