Landmark-Based Shape Analysis on Middle Cerebral Intracranial Aneurysms: A Geometric Morphometrics Approach to Infer...

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 anatomists' use of geometric morphometrics to build evolutionary trees, reconstructing species inter-relationships based on morphologic landmarks.

We adopted these tools to analyze cross-sectional image data and infer relationships between IA morphologies.

On 3D reconstructions of 52 middle cerebral arteries (MCA) IAs (9 ruptured) and 10 IAfree 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.

Principal component analysis showed separation of IA-free MCA geometries and grouping of ruptured IAs from unruptured IAs. Hierarchical clustering delineated a cluster of only unruptured 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, indicating greater shape derivation. Groups of unruptured IAs were observed, but ruptured IAs were invariably found in mixed lineages with unruptured IAs, suggesting that some pathways of shape change may be benign while others are more associated with rupture.

Geometric morphometric analyses of larger datasets may indicate particular pathways of shape change leading toward aneurysm rupture versus stabilization.