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Grading of glioma is crucial for treatment decision making as well as prognostic assessments. In clinical routines, radiologists grade gliomas with multiple complementary magnetic resonance imaging (MRI) sequences, which is yet challenging for glioma prediction models. In this paper, we take full advantages of four commonly used MRI sequences to propose non-invasive grading of glioma based on a variant of maximum entropy discrimination (MED) and decision tree. First, radiomics features calculation is, respectively, performed on T1-weighted imaging, T2-weighted imaging, fluid attenuation inversion recovery imaging, and contrast-enhanced T1-weighted imaging. Then, radiomics features are integrated to build a glioma prediction model named four-sequence MED (FSMED) according to the assumption that the classification margin of different sequences is consistent. Finally, we propose a multi-MED decision tree (MMEDT) model to obtain the grading of gliomas based on the output of FSMED and the results of MED on each sequence. Validation experiments are conducted on a data set collected from Henan Provincial People’s Hospital (GliomaHPPH2018) and Multimodal Brain Tumor Image Segmentation Benchmark 2017 (BraTS2017). The results of these two data sets demonstrate the high-prediction performance of our method. The average areas under the curve (AUC) of MMEDT are 0.9119, 0.8184, and 0.9084 for GliomaHPPH2018, BraTS2017, and their merged set, respectively, with the corresponding average sensitivities of 92.55%, 87.85%, and 87.91%, and average specificities of 92.57%, 81.36%, and 87.39%.

Four-Sequence Maximum Entropy Discrimination Algorithm for Glioma Grading