Landmark-based shape analysis on middle cerebral intracranial aneurysms: A geometric morphometrics approach to infer natural history

Background: Due to the scarcity of longitudinal data, the morphologic development of intracranial aneurysms (IAs) during their natural history remains poorly understood. However, longitudinal information can often be inferred from cross-sectional datasets as demonstrated by ana-tomists’ use of geometric morphometrics to build evolutionary trees, reconstructing species inter-re-lationships based on morphologic landmarks. Objective: We adopted these tools to analyze cross-sectional image data and infer relationships between IA morphologies. Methods: On 3D reconstructions of 52 middle cerebral arteries (MCA) IAs (9 ruptured) and 10 IA-free MCAs (baseline geometries), 7 semi-automated landmarks were placed at the proximal parent artery and maximum height. From these, 64 additional landmarks were computationally generated to create a 71-landmark point cloud of 213 xyz coordinates. This data was normalized by Procrustes transformation and used in the principal component analysis, hierarchical clustering, and phylogenetic analyses. Results: Principal component analysis showed separation of IA-free MCA geometries and group-ing of ruptured IAs from unruptured IAs. Hierarchical clustering delineated a cluster of only unrup-tured IAs that were significantly smaller and more spherical than clusters that had ruptured IAs. Phylogenetic classification placed ruptured IAs more distally in the tree than unruptured IAs, indi-cating greater shape derivation. Groups of unruptured IAs were observed, but ruptured IAs were in-variably found in mixed lineages with unruptured IAs, suggesting that some pathways of shape change may be benign while others are more associated with rupture. Conclusion: Geometric morphometric analyses of larger datasets may indicate particular pathways of shape change leading toward aneurysm rupture versus stabilization.