Related Experiment Video
Updated: Dec 22, 2025
Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography
Published on: January 17, 2025
MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma
Fatma Ceren Sarioglu1, Orkun Sarioglu2, Handan Guleryuz3
1Department of Radiology, Division of Pediatric Radiology, Dokuz Eylul University School of Medicine, Balcova, 35340, Izmir, Turkey. drcerenunal@gmail.com.
Objectives:
To evaluate the diagnostic performance of MRI texture analysis (TA) for differentiation of pediatric craniofacial rhabdomyosarcoma (RMS) from infantile hemangioma (IH).
Methods:
This study included 15 patients with RMS and 42 patients with IH who underwent MRI before an invasive procedure. All patients had a solitary lesion. T2-weighted and fat-suppressed contrast-enhanced T1-weighted axial images were used for TA. Two readers delineated the tumor borders for TA independently and evaluated the qualitative MRI characteristics in consensus. The differences of the texture features' values between the groups were assessed and ROC curves were calculated. Logistic regression analysis was used to analyze the value of TA with and without the combination of the qualitative MRI characteristics. A p value < 0.05 was considered statistically significant.
Results:
Thirty-eight texture features were calculated for each tumor. Eighteen features on T2-weighted images and 25 features on contrast-enhanced T1-weighted images were significantly different between the RMSs and IHs. On contrast-enhanced T1-weighted images, the short-zone emphasis (SZE), which was a gray-level zone length matrix (GLZLM) parameter, had the largest area under the curve: 0.899 (sensitivity 93%, specificity 87%). The independent predictor for the RMS among the qualitative MRI characteristics was heterogeneous contrast enhancement (p < 0.001). Using only a GLZLM_SZE value of lower than 0.72 was found to be the best diagnostic parameter in predicting RMS (p < 0.001; 95% CI, 8.770-992.4).
Conclusion:
MRI-based TA may contribute to differentiate RMS from IH without invasive procedures.
Key Points:
• Texture analysis may help to distinguish between rhabdomyosarcoma and infantile hemangioma without invasive procedures. • The gray-level zone length matrix parameters, especially the short-zone emphasis, may be a potential predictor for rhabdomyosarcoma. • Using contrast-enhanced T1-weighted images may be superior to T2-weighted images to differentiate rhabdomyosarcoma from infantile hemangioma in texture analysis.
Insights
MRI texture analysis can differentiate pediatric rhabdomyosarcoma (RMS) from infantile hemangioma (IH) without invasive procedures. Gray-level zone length matrix parameters, particularly short-zone emphasis on contrast-enhanced T1-weighted images, show diagnostic potential.
Area of Science:
- Radiology
- Oncology
- Pediatric Imaging
Background:
- Distinguishing pediatric craniofacial rhabdomyosarcoma (RMS) from infantile hemangioma (IH) is crucial for appropriate treatment.
- Current diagnostic methods often involve invasive procedures, posing risks to pediatric patients.
Purpose of the Study:
- To evaluate the diagnostic performance of MRI texture analysis (TA) in differentiating pediatric craniofacial RMS from IH.
- To identify specific texture features that can reliably distinguish between these two pediatric tumors.
Main Methods:
- Retrospective analysis of MRI data from 15 patients with RMS and 42 with IH.
- Texture analysis performed on T2-weighted and contrast-enhanced T1-weighted images.
- Comparison of texture features between RMS and IH groups using ROC curve analysis and logistic regression.
Main Results:
- Significant differences in 18 texture features on T2-weighted and 25 on contrast-enhanced T1-weighted images between RMS and IH.
- Short-zone emphasis (SZE), a gray-level zone length matrix (GLZLM) parameter, demonstrated the highest AUC (0.899) on contrast-enhanced T1-weighted images.
- GLZLM_SZE value < 0.72 was the optimal predictor for RMS (sensitivity 93%, specificity 87%).
Conclusions:
- MRI-based texture analysis offers a non-invasive method for differentiating pediatric craniofacial RMS from IH.
- GLZLM parameters, especially SZE, show promise as potential imaging biomarkers for RMS.
- Contrast-enhanced T1-weighted images appear superior to T2-weighted images for texture-based tumor differentiation.
