Abstract Purpose Morphological severity of unicoronal synostosis (UCS) is highly variable. Yet, there is no consensus on an objective and standardised metric to quantify 3D severity to evaluate preoperative severity and surgical outcomes. This study aimed to investigate previously described severity indices and compared these to a statistical shape model (SSM) of the cranial vault and expert rating. Methods Computed tomography of 77 patients with non-syndromic UCS and 75 healthy controls were included. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to identify latent axes of shape variation and derived severity estimates, thereby defining an SSM and UCS Skull Shape-based Severity Index (UCS-SI). Expert-based severity ranking served as an independent reference standard. Correlation analyses assessed associations of severity with skull vault shape and expert rankings. Results The UCS-SI strongly correlated with expert severity ranking and outperformed classical severity indices. PCA and PLS-DA robustly discriminated UCS from controls, with high variable-importance scores localised to the frontal, supraorbital and parietal regions. Conclusion UCS-SI might be a promising severity index in UCS regarding expert perception and objective morphology, offering a clinically interpretable and scalable approach for severity stratification, phenotypic characterisation, and evaluation of surgical outcome trajectories. However, the development of a severity algorithm rather than a severity score might be needed to fully characterise UCS severity. The optimal objective severity metric in UCS is yet to be defined.
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Robin Van der Straeten
Hanna Lif
Maya Geoffroy
Child s Nervous System
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Straeten et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69fd7fa1bfa21ec5bbf082cd — DOI: https://doi.org/10.1007/s00381-026-07290-0
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