Radiomics strategy for glioma grading using texture features from multiparametric MRI

Background Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. Purpose/Hypothesis To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps. Study Type Retrospective; radiomics. Population A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively. Field Strength/Sequence 3.0T MRI/T1‐weighted images before and after contrast‐enhanced, T2‐weighted, multi‐b‐value diffusion‐weighted and 3D arterial spin labeling images. Assessment After multiparametric MRI preprocessing, high‐throughput features were derived from patients' volumes of interests (VOIs). The support vector machine‐based recursive feature elimination was adopted to find the optimal features for low‐grade glioma (LGG) vs. high‐grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency. Statistical Tests Student's t‐test or a chi‐square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist. Results Patients' ages between LGG and HGG groups were significantly different (P