Multiple diffusion metrics in differentiating solid glioma from brain inflammation

Kai Zhao; Ankang Gao; Eryuan Gao; Jinbo Qi; Ting Chen; Guohua Zhao; Gaoyang Zhao; Peipei Wang; Weijian Wang; Jie Bai; Yong Zhang; Huiting Zhang; Guang Yang; Xiaoyue Ma; Jingliang Cheng

doi:10.3389/fnins.2023.1320296

Multiple diffusion metrics in differentiating solid glioma from brain inflammation

Front Neurosci. 2024 Jan 30:17:1320296. doi: 10.3389/fnins.2023.1320296. eCollection 2023.

Authors

Kai Zhao^#¹, Ankang Gao^#¹, Eryuan Gao¹, Jinbo Qi¹, Ting Chen¹, Guohua Zhao¹, Gaoyang Zhao¹, Peipei Wang¹, Weijian Wang¹, Jie Bai¹, Yong Zhang¹, Huiting Zhang², Guang Yang³, Xiaoyue Ma¹, Jingliang Cheng¹

Affiliations

¹ Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
² MR Research Collaboration, Siemens Healthineers Ltd., Wuhan, China.
³ Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.

^# Contributed equally.

Abstract

Background and purpose: The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models.

Materials and methods: Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm²) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated.

Results: 57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758).

Conclusion: Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.

Keywords: brain inflammation; diffusion-weighted imaging; glioma; magnetic resonance imaging; non-Gaussian.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study has received funding from the Joint Construction Project of Henan Province Medical Science and Technology Research Program (Grant No. LHGJ20230181), the Youth Project of Henan Medical Science and Technology Research Project (Grant No. SBGJ202103078), the 2021 Henan Province key research and development and promotion of special projects (scientific and technological research) (Grant No. 212102310699), the Joint construction project of Health Commission of Henan Province, China (Grant No. LHGJ20200384), and the Beijing Health Alliance Charitable Foundation, China (Grant No. HN-20201017-004).