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 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 K trans , V e , and K ep from DCE-MRI were compared with histopathological grading. Receiver operating characteristic (ROC) curves were used to assess the diagnostic test of K trans , V e , and K ep in glioma grading. Results ROC curve analysis showed that K trans is good to be used as a predictor of HGG, while V e and K ep are good enough to be used as predictor of HGG. The cutoff value K trans 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 V e 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 K ep 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 K trans and V e . Among the three DCE-MRI quantitative parameters, K trans 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.