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Diagnosis and prognosis prediction of glioma based on multimodal MRI radiomics and deep learning
WEI Huanhuan  YANG Yan  FU Fangfang  GAO Haiyan  CHEN Lijuan  WU Yaping  BAI Yan  YU Xuan  WANG Meiyun 

Cite this article as: WEI H H, YANG Y, FU F F, et al. Diagnosis and prognosis prediction of glioma based on multimodal MRI radiomics and deep learning[J]. Chin J Magn Reson Imaging, 2023, 14(5): 175-180. DOI:10.12015/issn.1674-8034.2023.05.031.

[Abstract] Glioma is the most common primary malignant tumor of the central nervous system, which has a rapid progression and poor prognosis. Different histopathological classification/grading and molecular phenotype information lead to the diversity and refractory of glioma. Multimodality MRI techniques such as magnetic resonance spectroscopy (MRS), magnetic resonance fingerprint imaging (MRF), chemical exchange saturation transfer (APT), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) can provide information for glioma assessment from a variety of perspectives, and combined artificial intelligence computer-assisted diagnostic techniques can achieve more objective and accurate evaluation and analysis of gliomas and expand the clinical application value of MR techniques. In this paper, the research status of the diagnosis and prognosis prediction of glioma based on multimodal magnetic resonance techniques such as MRS, MRF, APT, DWI, DTI, DKI and radiomics and deep learning were discussed, in order to provide reference for the preoperative evaluation of glioma.
[Keywords] glioma;magnetic resonance;radiomics;multimodality magnetic resonance;radiomics;deep learning;diagnostics;prediction of prognosis

WEI Huanhuan1   YANG Yan1   FU Fangfang2   GAO Haiyan2   CHEN Lijuan2   WU Yaping2   BAI Yan2   YU Xuan2   WANG Meiyun2*  

1 Department of Imagingy, People's Hospital of Zhengzhou University, Zhengzhou 450003, China

2 Department of Imagingy, Henan Provincial People's Hospital, Zhengzhou 450003, China

Corresponding author: Wang MY, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Youth Project of Natural Science Foundation of Henan Province (No. 212300410240); Henan Provincial Science and Technology Research Project (No. SBGJ202101002).
Received  2022-12-07
Accepted  2023-05-05
DOI: 10.12015/issn.1674-8034.2023.05.031
Cite this article as: WEI H H, YANG Y, FU F F, et al. Diagnosis and prognosis prediction of glioma based on multimodal MRI radiomics and deep learning[J]. Chin J Magn Reson Imaging, 2023, 14(5): 175-180. DOI:10.12015/issn.1674-8034.2023.05.031.

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