Glioma classification via MR images radiomics analysis

Abstract

Accurate glioma classification before surgery is of the utmost important in clinical decision making and prognosis prediction. In this paper, we investigate the impact of multi-modal MR image fusion for the differentiation of low-grade gliomas (LGG) versus high-grade gliomas (HGG) via integrative analyses of radiomic features and machine learning approaches. A set of 80 histologically confirmed gliomas patients (40 HGG and 40 LGG) obtained from the MICCAI BraTS 2019 data were involved in this study. To achieve this work, we propose to combine T1 with T2 or FLAIR modality in the non-subsampled shearlet domain. Firstly, the pre-processed source MR images are decomposed into low-frequency (LF) and several high-frequency (HF) sub-images. LF sub-images are fused using the proposed weight local features fusion rule while HF sub-images are combined based on the novel sum-modified-laplacian technique. Experimental results demonstrate that the proposed fusion approach outperformed the recent state-of-the-art approaches in terms of entropy and feature mutual information. Subsequently, a key radiomics signature was retrieved by the least absolute shrinkage and selection operator regression algorithm. Five machine learning classifiers were established and evaluated with the retrieved dataset, then with the fused dataset using tenfold cross-validation scheme. As a result, the random forest had the highest accuracy of 96.5% with 21 features selected from the raw data and 96.1% with 16 features selected from the fused data. Finally, the experimental findings confirm that the proposed aided diagnosis framework represents a promising tool to aid radiologists in differentiating HGG and LGG.