Noninvasively Evaluating the Grading of Glioma by Multiparametric Magnetic Resonance Imaging

Rationale and Objective: To investigate the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading. Materials and Methods: Seventy consecutive patients with histopathologically confirmed glioma were retrospectively evaluated by conventional MRI, dynamic susceptibility-weighted contrast-enhanced, multiple diffusion-weighted imaging signal models including mono-exponential, bi-exponential, stretched exponential, and diffusion kurtosis imaging. One-way analysis of variance and independent-samples t test were used to compare the MR parameter values between low and high grades as well as among all grades of glioma. Receiver operating characteristic analysis, Spearman's correlation analysis, and binary logistic regression analysis were used to assess their diagnostic performance. Results: The diagnostic performance (the optimal thresholds, area under the receiver operating characteristic curve, sensitivity, and specificity) was achieved with normalized relative cerebral blood flow (rCBV) (2.240 ml/100 g, 0.844, 87.8%, and 75.9%, respectively), mean kurtosis (MK) (0.471, 0.873, 92.7%, and 79.3%), and water molecular diffusion heterogeneity index (alpha) (1.064, 0.847, 79.3% and 78.0%) for glioma grading. There were positive correlations between rCBV and MK and the tumor grades and negative correlations between alpha and the tumor grades (p < 0.01). The parameter of a yielded alpha diagnostic accuracy of 85.3%, the combination of MK and alpha yielded a diagnostic accuracy of 89.7%, while the combination of rCBV, MK, and alpha were more accurate (94.2%) in predicting tumor grade. Conclusion: The most accurate parameters were rCBV, MK, and alpha in dynamic susceptibility-weighted contrast, diffusion kurtosis imaging, and Multi-b diffusion-weighted imaging for glioma grading, respectively. Multiparametric MRI can increase the accuracy of glioma grading.

Automated summary

The study investigated the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading, and found that normalized relative cerebral blood flow (rCBV), mean kurtosis (MK), and water molecular diffusion heterogeneity index (alpha) were the most accurate parameters for assessing glioma grading. Multiparametric MRI can increase the accuracy of glioma grading.