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Research progress of deep learning and radiomics in glioma
LI Jie  LIU Guangyao  FAN Fengxian  HU Wanjun  BAI Yuping  ZHANG Jing 

Cite this article as: Li J, Liu GY, Fan FX, et al. Research progress of deep learning and radiomics in glioma[J]. Chin J Magn Reson Imaging, 2022, 13(4): 158-161. DOI:10.12015/issn.1674-8034.2022.04.035.


[Abstract] Deep learning is a branch of artificial intelligence. It has developed rapidly in disease detection and prognosis evaluation, and has become a popular research method, especially in the field of medical image in recent years. Radiomics is a very considerable method in the study of glioma. Deep learning and radiomics based on MRI can make differential diagnosis and classification of glioma, predict the genotype change status before operation, evaluate the treatment effect and predict the progression free survival and overall survival after operation, which provides a important basis for clinical treatment and postoperative follow-up. It is a research hotspot of glioma at present. This paper is to review the research progress of deep learning and radiomics based on MRI in the differential diagnosis, preoperative grading, genotyping and prognosis of glioma.
[Keywords] glioma；deep learning；radiomics；magnetic resonance imaging；differential diagnosis；preoperative classification；genotyping；survival prediction

LI Jie1, 2, 3   LIU Guangyao1, 2, 3   FAN Fengxian1, 3   HU Wanjun1, 3   BAI Yuping1, 2, 3   ZHANG Jing1, 3*  

1 Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China

2 Second Clinical School, Lanzhou University, Lanzhou 730030, China

3 Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China

Zhang J, E-mail: lztong2001@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Gansu Province (No. 21JR1RA129); Science and Technology Project of Gansu Province (No. 21JR7RA438); Talent innovation and entrepreneurship project of Lanzhou Chengguan District (No. 2020RCCX0034).
Received  2021-12-31
Accepted  2022-04-02
DOI: 10.12015/issn.1674-8034.2022.04.035
Cite this article as: Li J, Liu GY, Fan FX, et al. Research progress of deep learning and radiomics in glioma[J]. Chin J Magn Reson Imaging, 2022, 13(4): 158-161. DOI:10.12015/issn.1674-8034.2022.04.035.

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