Scholarlink

Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Is a standardised severity index needed in unicoronal craniosynostosis? Challenges in developing an objective metric

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

查看全文
  • Robin van der Straeten,
  • Hanna Lif,
  • Maya Geoffroy,
  • Maxime Taverne,
  • Giovanna Paternoster,
  • Sébastien Laporte,
  • Roman Hossein Khonsari

摘要

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.