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Supratentorial glioma grading in children by using apparent diffusion coefficient map: application of histogram
V K Ngan1,2, N D Hung2, N D Hung3
1Department of Radiology, Hanoi Medical University, Hanoi, Vietnam.
La Clinica Terapeutica
|June 17, 2025
Summary
Histogram analysis of diffusion-weighted imaging aids in grading pediatric gliomas. Excluding necrotic portions improves diagnostic accuracy for differentiating low-grade from high-grade gliomas, with ADCmin showing the highest predictive performance.
Area of Science:
- Radiology
- Pediatric Oncology
- Neuroimaging
Background:
- Pediatric low-grade gliomas (pLGGs) and pediatric high-grade gliomas (pHGGs) have distinct clinical outcomes and treatment requirements.
- Accurate preoperative grading of pediatric gliomas is crucial for effective treatment planning and patient management.
- Diffusion-weighted imaging (DWI) and histogram analysis (HA) are emerging tools for non-invasive tumor characterization.
Purpose of the Study:
- To evaluate the efficacy of histogram analysis (HA) of diffusion-weighted imaging (DWI) in differentiating pediatric low-grade gliomas (pLGGs) from pediatric high-grade gliomas (pHGGs).
- To compare the diagnostic performance of HA using a manually segmented 3D volume of interest (VOI) excluding cystic/necrotic portions versus an entire tumor VOI.
- To determine if excluding non-necrotic portions enhances the correlation between HA parameters and tumor characteristics for improved preoperative evaluation.
Main Methods:
- A prospective study enrolled 31 pediatric patients with pathologically confirmed gliomas.
- Patients underwent baseline DWI, and apparent diffusion coefficient (ADC) maps were generated.
- Histogram analysis was performed on both entire tumor VOIs (VOI 1) and VOIs excluding cystic/necrotic portions (VOI 2) to extract parameters like mean, min, max, and standard deviation.
Main Results:
- Significant differences in ADC parameters were observed between pLGGs and pHGGs in both VOI methods, with more parameters showing significance in VOI 2.
- VOI 2 (excluding cystic/necrotic portions) demonstrated higher diagnostic ability (AUC = 0.904 for ADCmin) compared to VOI 1 (AUC = 0.838 for ADCmin).
- ADCmin, ADCmean, ADCmedian, and ADC MPP showed the highest diagnostic performance in VOI 2 for predicting histological glioma grading.
Conclusions:
- Histogram analysis of DWI is a valuable tool for grading pediatric gliomas.
- Segmenting VOIs to exclude cystic and necrotic portions significantly enhances the diagnostic accuracy of HA compared to using the entire tumor volume.
- ADCmin, derived from segmented VOIs excluding non-viable tissue, exhibits the highest performance in predicting histological glioma grade in pediatric supratentorial gliomas.
