Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

Summary

This study proposed an age-adjusted regression modeling using midface and cranial base morphology as a potential tool for preoperative evaluation and individualized surgical planning for children with syndromic craniosynostosis (SC).

Abstract

Midface hypoplasia is a common anomaly in children with craniofacial disorders such as cleft palate and syndromic synostosis. A major functional issue associated with this condition is the narrowing of the nasopharyngeal airway, leading to respiratory disorders. A pilot study involving 30 computed tomography (CT) scan datasets across three distinct groups (normal, operated syndromic craniosynostosis, and non-operated syndromic craniosynostosis) was conducted to evaluate a published midface formula, namely 'Hariri-Ros-Nor regression model' for predictive cranial base measurement in syndromic craniosynostosis. The results obtained demonstrated that while cranial base (NBa) predictions were relatively consistent, midface width (ZMR-ZML) showed significant discrepancies from predicted values, particularly in operated syndromic craniosynostosis patients. Age was found to significantly influence measurement accuracy and prediction reliability. In conclusion, the current study suggests that age-adjusted modeling may enhance predictive accuracy in craniofacial assessment. It offers a potential tool for the preoperative evaluation and individualized surgical planning in syndromic craniosynostosis.

Introduction

Syndromic craniosynostosis (SC) is a complex congenital condition caused by the premature fusion of cranial sutures, resulting in restricted skull growth, cranial base deformities, and midface hypoplasia that often leads to functional and aesthetic complications^1,2. These abnormalities can compromise airway patency, visual integrity, and neurodevelopmental outcomes, underscoring the importance of early recognition and precise intervention^1,3. Surgical management in SC is particularly time-sensitive, as delayed correction increases the risk of raised intracranial pressure, obstructive sleep apnea (OSA), and ophthalmic complications such as proptosis and corneal exposure^1,3,4. Therefore, determining the optimal timing of cranial and midfacial advancement remains one of the most critical and challenging aspects of clinical management¹.

Conventional assessment methods for syndromic craniosynostosis often rely on descriptive growth analyses and functional thresholds to guide surgical intervention^5,6. While these approaches provide general reference points, they lack patient-specific predictive accuracy and are influenced by clinical subjectivity^7,8. In recent years, interest has grown in developing quantitative models that can anticipate craniofacial growth trajectories and inform individualized surgical planning. Among these, the Hariri-Ros-Nor regression model offers a reproducible and quantitative framework by establishing predictive relationships between cranial base landmarks and midfacial dimensions, providing clinicians with an objective tool to forecast growth and optimize surgical timing⁹.

The predictive strength of this model is particularly relevant to the most common syndromic subtypes, such as Apert and Crouzon syndromes, which exhibit distinct growth patterns and surgical requirements⁹. By using standard craniofacial CT imaging and reproducible anatomical landmarks, namely Sella, Nasion, Basion, ZMR, and ZML, the model can quantify midfacial growth with efficiency and interpretability, even in clinical settings where advanced geometric morphometric or finite-element analyses are unavailable⁹. It is especially valuable in early to mid-childhood (approximately 4-12 years), when facial growth is active and surgical timing decisions have lasting developmental implications⁹. However, the model's applicability is limited by its reliance on CT imaging, its validation within a single cohort, and potential variation in very young or late-adolescent patients, highlighting the need for its integration alongside multidisciplinary evaluation¹⁰.

Building on this foundation, the Hariri-Ros-Nor regression model provides a reproducible, patient-specific approach to predicting midfacial and cranial base growth, addressing the limitations of current descriptive frameworks. Quantifying anatomical relationships allows earlier identification of patients at risk of functional deterioration and supports proactive, data-driven surgical planning. These capabilities hold particular significance in preventing irreversible complications associated with delayed intervention, such as airway obstruction, intracranial hypertension, and ophthalmic morbidity. Therefore, this study aims to evaluate the accuracy and clinical applicability of the Hariri-Ros-Nor regression model in assessing midfacial and cranial base growth patterns among syndromic and non-syndromic pediatric populations.

Protocol

This study was approved by the Medical Ethics Committee, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur (DF OS1921/0082(P)). Since the research involved retrospective and anonymized computed tomography (CT) scan data without the use of any biological tissues or fluids, patient consent was not required. The methodology of this study followed the procedures outlined by Hariri et al.⁹ with several modifications. The equipment and software used are listed in the Table of Materials.

1. Subject selection and CT scan data retrieval

Retrospective cranial CT scans of pediatric patients diagnosed with syndromic craniosynostosis (SC) were retrieved from the digital imaging archive of the Craniofacial Clinic at University Malaya Medical Centre (UMMC), covering the period from November 2015 to December 2022. Additionally, prospective CT scans obtained between May 2023 and January 2024 were included.

Potential subjects were identified from the Craniofacial Clinic records and screened manually by the investigator according to predefined eligibility criteria. The inclusion criteria comprised (1) a confirmed diagnosis of syndromic craniosynostosis by craniofacial surgeons, (2) availability of a complete medical record, and (3) age <12 years. Control subjects were also under 12 years of age and required to have complete cranial and facial CT scans without any history of craniofacial anomalies. The exclusion criteria applied to all cohorts included age ≥12 years, non-syndromic or isolated craniosynostosis, incomplete clinical or imaging documentation, a history of craniofacial surgery, or midface hypoplasia associated with other syndromes. Following screening, a finalized list of eligible subjects was submitted to the Research Unit of Biomedical Imaging to obtain non-contrast CT skull datasets. Imaging data were provided on compact discs (CDs) in Digital Imaging and Communications in Medicine (DICOM) format, with acquisition protocols standardized according to departmental guidelines (axial slice thickness ≤1.00 mm, full craniofacial coverage, and consistent voxel resolution). The Biomedical Imaging unit verified dataset completeness, absence of significant artifacts, and compliance with DICOM standards prior to release. All verified datasets were securely archived and subsequently prepared for import into a 3D medical image processing software for three-dimensional reconstruction and analysis. Thirty subjects were selected in total and divided equally into three cohorts: non-operated SCgroup (SCNO), operated SC group (SCO), and normal control group (n = 10 per group).

2. 3D reconstruction and landmark identification

All imported datasets were displayed in axial, sagittal, and coronal planes, together with a three-dimensional preview. Metadata within the DICOM files allowed automatic extraction of acquisition parameters (slice thickness, voxel resolution, and number of slices). To generate the three-dimensional volumetric model, a bone mask was first created and subsequently reconstructed by selecting Segmentation → Calculate 3D in Mimics. Segmentation was further refined using the thresholding function, accessible via Segmentation → Thresholding in the main toolbar. Variability in bone definition or the presence of excessive noise can be observed when inappropriate Hounsfield Unit (HU) ranges were applied during segmentation. This issue can be resolved by applying the standard "Bone (CT)" threshold preset available in 3D medical imaging software and refining the segmentation masks using the Edit Mask function to exclude artifacts and improve structural continuity. This process ensured that only osseous structures were highlighted in the mask, thereby excluding adjacent soft tissues and reducing image noise or artifacts.

Anatomical landmarks were identified and marked using the point creation tool, located under Measurements → Create Point. For each landmark, the investigator rotated and magnified the 3D reconstruction to optimize visibility and then placed a point directly on the anatomical site with a single mouse click. The following landmarks were recorded: the Sella (S) at the midpoint of the sella turcica, the Nasion (N) at the junction of the frontonasal suture, the Basion (Ba), defined as the anterior midpoint of the occipital bone at the spheno-occipital synchondrosis, and the right and left zygomaticomaxillary sutures (ZMR, ZML) at the infraorbital rim. Minor variability in landmark positioning may occur when points are defined from a single viewing perspective. To enhance accuracy, each landmark was confirmed across multiple viewing angles by rotating the three-dimensional reconstruction and cross-referencing with axial, sagittal, and coronal planes prior to final placement. Each landmark was automatically stored within the project tree under the "Measurements" list. The complete set of landmark coordinates and labels was exported using the Export Measurements function, producing datasets in .csv or .xls format. These landmarks were subsequently used as reference points for linear measurements of the cranial base and midfacial skeleton.

3. Linear measurement of cranial and midfacial variables

Linear craniofacial measurements were performed in 3D medical image processing software, using the distance measurement function. After three-dimensional reconstruction and landmark placement, the measurement tool was accessed through the Analysis → Measurements → Distance between points menus. In this function, two anatomical landmarks were manually selected on the 3D reconstruction, and the software automatically calculated the Euclidean distance between the selected coordinates and the results were displayed within the Measurements Results window.

The following linear measurements were obtained: SN (anterior cranial base length), defined as the distance between the sella (S) and nasion (N); SBa (posterior cranial base length), defined as the distance between the sella (S) and basion (Ba); NBa (total cranial base length), defined as the distance between the nasion (N) and basion (Ba); and ZMR - ZML (maxillary width), defined as the interzygomatic distance between the right and left zygomaticomaxillary sutures. Each measurement was performed directly on the 3D model with zoom and rotation tools applied as necessary to ensure accurate point placement.

Upon completion, the measurement list was automatically updated within the software's project tree. The complete dataset was exported using the Export Measurements function and saved in .xls format, including all landmark labels with their corresponding linear values. These exported values were subsequently used for statistical analysis.

4. Regression model application

The exported measurements were imported into a spreadsheet program for further analysis. Using the Hariri-Ros-Nor regression formula, predicted values were calculated for both the total cranial base length and maxillary width. The following equations were applied:

The predicted values derived from these formulas were then compared with the corresponding measured values for each subject to assess consistency and deviation in cranial morphology by calculating the standard deviation of the dataset. Following that, the dataset is now ready for statistical analysis. 

5. Statistical analysis

All measured and predicted values were compiled into a single dataset and analyzed using statistical analysis software. Descriptive statistics were first calculated to summarize cranial base and midfacial parameters across all groups. Inter-group differences were assessed using the non-parametric Mann–Whitney U test. Additionally, Pearson correlation analysis was conducted to evaluate the relationship between age and cranial measurements within each group. Visual representations of the data, including boxplots for group comparisons and line graphs for correlation trends, were generated to support and clarify statistical interpretations. 

Results

According to Table 1, the measured NBa values were consistently higher than their predicted counterparts across all groups, with the greatest deviation observed in the operated syndromic craniosynostosis (SCO) cohort. In contrast, ZMR-ZML (maxillary width) values were significantly lower than predicted across all categories, demonstrating considerable variability.

Following Pearson correlation analysis (Table 2) and matrix scatter plot distribution (

Discussion

This pilot study demonstrates a structured and reproducible imaging protocol that integrates landmark-based analysis with predictive modeling for quantitative craniofacial evaluation. The protocol emphasizes critical methodological steps such as standardized segmentation, precise landmark placement, and consistent measurement procedures to ensure accuracy and reproducibility. Image segmentation and three-dimensional reconstruction were performed in the 3D medical image processing software using consistent thresholding pa...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Compact Dics (CD)
Materialise Mimics Medical software	Materialise NV	Version 21	3D Medical Image Processing Software
Microsoft Excel	Microsoft Inc.		Spreadsheet Software
Personal Computer with CD reader
Statistical Package for the Social Sciences (SPSS)	IBM	G06TFML	Statistical Analysis Software