Name: Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model (Video) | JoVE Journal
Uploaded: 2025-11-04T14:45:00.000+00:00
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Description: Discover age-adjusted regression modeling for craniofacial surgical planning using retrospective CT scan analysis. This method improves preoperative evaluation accuracy for children with syndromic craniosynostosis.

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This work was supported by the Universiti&#160;Malaya Oro-Craniomaxillofacial Research and Surgical (OCReS) group, Faculty of Dentistry, Universiti Malaya.

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 &lt;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:
<img alt="Mathematical equations for linear regression analysis, depicting variables SN, SBa, NBa, ZMRZML." src="/files/ftp_upload/69176/69176eq1.jpg" style="margin: auto;" data-altupdatedat="1783396841000" data-alt="Equation 1">
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.

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The authors have no competing interests to declare.

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...

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 complications1,2. These abnormalities can compromise airway patency, visual integrity, and neurodevelopmental outcomes, underscoring the importance of early recognition and precise intervention1,3. 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 exposure1,3,4. 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 intervention5,6. While these approaches provide general reference points, they lack patient-specific predictive accuracy and are influenced by clinical subjectivity7,8. 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.

<table><tbody><tr><td>Compact Dics (CD)</td><td></td><td></td><td></td></tr><tr><td>Materialise Mimics Medical software</td><td>Materialise NV</td><td>Version 21</td><td>3D Medical Image Processing Software</td></tr><tr><td>Microsoft Excel</td><td>Microsoft Inc.</td><td></td><td>Spreadsheet Software</td></tr><tr><td>Personal Computer with CD reader</td><td></td><td></td><td></td></tr><tr><td>Statistical Package for the Social Sciences (SPSS)</td><td>IBM</td><td>G06TFML</td><td>Statistical Analysis Software</td></tr></tbody></table>

midface hypoplasia and cranial base morphology in syndromic craniosynostosis: a comparative analysis study using a predictive regression model

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

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 &#39;Hariri-Ros-Nor regression model&#39; 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.

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 (<strong cl...

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).

Midface hypoplasia is a common anomaly in children with craniofacial disorders such as cleft palate and syndromic synostosis. A major functional issue ...

My research focuses on how age-adjusted midface and cranial-based measurements improve surgical planning for syndromic craniosynostosis. Recent development include morphological modeling and predictive analytics to enable more precise, personalized craniofacial surgical planning. To begin, open the digital imaging archive.Retrieve retrospective cranial CT scan datasets of pediatric patients diagnosed with syndromic craniosynostosis. Select subjects who meet the inclusion criteria, confirmed syndromic craniosynostosis diagnosis by craniofacial surgeons, complete medical records, and are below 12 years. Similarly, select control subjects with complete cranial and facial CT scans and no history of craniofacial anomalies.After receiving non-contrast CT skull datasets in DICOM format, ensure imaging protocols meet departmental standards. Archive all verified dataset securely, and prepare them for import into a three-dimensional medical image processing software for reconstruction and analysis. Select 30 subjects and assign them equally into three cohorts, non-operated syndromic craniosynostosis, operated syndromic craniosynostosis, and normal control with 10 subjects in each group.Display all imported datasets simultaneously in axial, sagittal, and coronal planes, along with a three-dimensional preview. To begin three-dimensional volumetric reconstruction, click New Mask to generate the initial reconstruction. Check the boxes for Fill holes and Keep largest, then press OK.Now, resolve segmentation issues caused by inappropriate Hounsfield unit ranges by applying the standard bone threshold preset.Refine the segmentation mask further using the Edit Masks function to remove artifacts and ensure structural continuity. Navigate to the Analyze tab and click on Measure and Analyze. Mark the landmarks, the sella S at the midpoint of the sella turcica, the nasion N at the frontonasal suture junction, the basion BA at the anterior midpoint of the occipital bone at the spheno-occipital synchondrosis, and right and left zygomaticomaxillary suture ZMR, ZML at the infraorbital rim.To confirm each landmark, view it from multiple angles and compare it with the axial, sagittal, and coronal planes for accuracy. Store all marked landmarks automatically in the Measurement section of the project tree. Export the coordinates and labels of each landmark using the Export measurements function to generate datasets in CSV or XLS format.Select any two anatomical landmarks manually on the 3D reconstruction, allowing the software to automatically calculate and display the Euclidean distance between them in the measurements results window. Next, allow the software to automatically update the measurement list within the project tree upon completion. Export the complete dataset using the Export measurements function and save it in XLS format.Import the exported linear measurement dataset into a spreadsheet program for analysis. Apply the Hariri-Ros-Nor regression formula to calculate predicted values for total cranial base length and maxillary width. Compare the predicted values with the measured values for each subject to evaluate consistency and morphological deviation.Calculate the standard deviation of the differences between measured and predicted values to quantify variability in the dataset. Then prepare the finalized dataset for downstream statistical analysis. After compiling all measured and predicted values into a single dataset, import them into statistical analysis software.Calculate descriptive statistics to summarize cranial base and midfacial measurement across all subject groups. Use the non-parametric Mann-Whitney U test to assess differences in cranial measurements between groups. Conduct a Pearson correlation analysis to determine the relationship between age and cranial measurements within each group.A strong positive correlation was found between NBa and ZMR-ZML in the normal group, as shown in the scatter matrix, supported by a Pearson correlation of 0.992. Measured NBa values were consistently higher than predicted values across all groups with the greatest deviation observed in the operated syndromic craniosynostosis group. Measured ZMR-ZML values were significantly lower than predicted values in all groups, demonstrating considerable variability.In the normal group, Pearson correlation analysis revealed strong associations between age in both NBa and ZMR-ZML. In the SC non-operated group, a strong correlation was observed only for ZMR-ZML, while the SC operated group showed weak correlations across all variables. Greater variability and wider interquartile ranges were observed in both NBa and ZMR-ZML values compared to the normal group in the SC operated cohort.The ratio distributions for the normal control group were clustered around central values, while the operated and non-operated SC groups showed broader distributions with extreme values. The SC operated group displayed a lower median ratio and reduced spread compared to the SC non-operated group. We have established that the age-adjustment improve radiation accuracy of craniofacial morphology.And this is important for customization or individualization in surgical planning for the patient. So we are addressing gaps in predictive accuracy by integrating each of the patient and his cranial-based measurements as a critical factor in our predictive model. Our protocol offers improved individualized prediction by adjusting for age, unlike static cranial-based models.

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Discover age-adjusted regression modeling for craniofacial surgical planning using retrospective CT scan analysis. This method improves preoperative evaluation accuracy for children with syndromic craniosynostosis.

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model (Video) | JoVE Journal

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

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