Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis

Authors

Jiawei Liu, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. jiawei.liu@cuanschutz.edu.
Joseph H. Froelicher, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
Brooke French, Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, Aurora, CO, 80045, USA.
Marius George Linguraru, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, 20010, USA.
Antonio R. Porras, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.

Document Type

Journal Article

Publication Date

11-23-2023

Journal

Scientific reports

Volume

13

Issue

1

DOI

10.1038/s41598-023-47622-7

Abstract

We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0-10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.

APA Citation

Liu, Jiawei; Froelicher, Joseph H.; French, Brooke; Linguraru, Marius George; and Porras, Antonio R., "Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis" (2023). GW Authored Works. Paper 3764.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/3764

Department

Radiology