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Liu J, Froelicher JH, French B, Linguraru MG, Porras AR. Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis. Sci Rep 2023; 13:20557. [PMID: 37996454 PMCID: PMC10667230 DOI: 10.1038/s41598-023-47622-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
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.
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Affiliation(s)
- Jiawei Liu
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
| | - 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
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, 20010, USA
- Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, 20052, USA
| | - Antonio R Porras
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, Aurora, CO, 80045, USA
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Pediatric Neurosurgery, Children's Hospital Colorado, Aurora, CO, 80045, USA
- Departments of Pediatrics and Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
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Data-driven Normative Reference of Pediatric Cranial Bone Development. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2022; 10:e4457. [PMID: 35983543 PMCID: PMC9377678 DOI: 10.1097/gox.0000000000004457] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/10/2022] [Indexed: 11/26/2022]
Abstract
Available normative references of cranial bone development and suture fusion are incomplete or based on simplified assumptions due to the lack of large datasets. We present a fully data-driven normative model that represents the age- and sex-specific variability of bone shape, thickness, and density between birth and 10 years of age at every location of the calvaria.
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