AI-Powered Radiomics Algorithm Based on Slice Pooling for the Glioma Grading

Zhao, Guohua; Man, Panpan; Bai, Jie; Li, Longfei; Wang, Peipei; Yang, Guan; Shi, Lei; Tao, Yongcai; Lin, Yusong; Cheng, Jingliang

doi:10.1109/tii.2021.3105665

Cited by 5 publications

(1 citation statement)

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“…For instance, T1 can better reflect the anatomical structure, T1CE can distinguish the enhanced tumor and nonenhanced tumor according to the degree of blood flow, T2 provides the degree of peritumoral edema, and FLAIR can better reflect the anatomical information of the tumor by inhibiting the edema information [ 4 ]. Previous studies have shown that the information contained in mpMRI of patients with glioma has the potential for auxiliary grading diagnosis; still, it is difficult for clinicians to effectively quantify this information [ 5 , 6 ]. Therefore, it is necessary to carry out a comprehensive analysis of glioma mpMRI, as well as fully explore and utilize the diagnostic information in the data.…”

Section: Introductionmentioning

confidence: 99%

Multiparametric Magnetic Resonance Imaging Information Fusion Using Graph Convolutional Network for Glioma Grading

Guo

et al. 2022

Journal of Healthcare Engineering

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Accurate preoperative glioma grading is essential for clinical decision-making and prognostic evaluation. Multiparametric magnetic resonance imaging (mpMRI) serves as an important diagnostic tool for glioma patients due to its superior performance in describing noninvasively the contextual information in tumor tissues. Previous studies achieved promising glioma grading results with mpMRI data utilizing a convolutional neural network (CNN)-based method. However, these studies have not fully exploited and effectively fused the rich tumor contextual information provided in the magnetic resonance (MR) images acquired with different imaging parameters. In this paper, a novel graph convolutional network (GCN)-based mpMRI information fusion module (named MMIF-GCN) is proposed to comprehensively fuse the tumor grading relevant information in mpMRI. Specifically, a graph is constructed according to the characteristics of mpMRI data. The vertices are defined as the glioma grading features of different slices extracted by the CNN, and the edges reflect the distances between the slices in a 3D volume. The proposed method updates the information in each vertex considering the interaction between adjacent vertices. The final glioma grading is conducted by combining the fused information in all vertices. The proposed MMIF-GCN module can introduce an additional nonlinear representation learning step in the process of mpMRI information fusion while maintaining the positional relationship between adjacent slices. Experiments were conducted on two datasets, that is, a public dataset (named BraTS2020) and a private one (named GliomaHPPH2018). The results indicate that the proposed method can effectively fuse the grading information provided in mpMRI data for better glioma grading performance.

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Section: Introductionmentioning

confidence: 99%