Value of Ktrans, Ve, and Kep as predictors of high-grade glioma: a study using dynamic contrast-enhanced magnetic resonance imaging
Abstract Background Glioma is the most common malignant tumor histologically. Multiparametric MRI helps differentiate high-grade glioma (HGG) and low-grade glioma (LGG). Neoangiogenesis of glioma causes increased permeability which can be measured quantitatively with the parameters of volume transfe...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-05-01
|
| Series: | The Egyptian Journal of Radiology and Nuclear Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43055-025-01475-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Background Glioma is the most common malignant tumor histologically. Multiparametric MRI helps differentiate high-grade glioma (HGG) and low-grade glioma (LGG). Neoangiogenesis of glioma causes increased permeability which can be measured quantitatively with the parameters of volume transfer constant (K trans), extravascular extracellular volume (V e), and mean return flow (K ep) through dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The aim of this study was to analyze the values of Ktrans, Ve, and Kep which are quantitative parameters of DCE-MRI as a predictor of high-grade glioma. Methods Thirty-five patients with intracranial space-occupying lesions with conventional MRI results of intracranial neoplasms underwent DCE-MRI. Permeability parameters such as Ktrans, Ve, and Kep from DCE-MRI were compared with histopathological grading. Receiver operating characteristic (ROC) curves were used to assess the diagnostic test of Ktrans, Ve, and Kep in glioma grading. Results ROC curve analysis showed that Ktrans is good to be used as a predictor of HGG, while Ve and Kep are good enough to be used as predictor of HGG. The cutoff value Ktrans was 1.002 with sensitivity 84.6%, specificity 78%, positive predictive value 91.6%, negative predictive value 63.6%, and odds ratio 2.52. The cutoff value Ve was 0.546 with sensitivity 92.3%, specificity 56%, positive predictive value 85.7%, negative predictive value 71.4%, and odds ratio 3. The cutoff value Kep was 2.766 with sensitivity 61.5%, specificity 89%, positive predictive value 94.1%, negative predictive value 44%, and odds ratio 1.69. Conclusions DCE-MRI can be used as a predictor of HGG through permeability parameters such as Ktrans and Ve. Among the three DCE-MRI quantitative parameters, Ktrans is the best parameter in this study. By measuring the degree of tumor permeability using DCE-MRI, it is expected to distinguish between HGG and LGG through a noninvasive method. |
|---|---|
| ISSN: | 2090-4762 |