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Research progress of deep learning in glioblastoma
ZHENG Fei  CHEN Xuzhu 

Cite this article as: Zheng F, Chen XZ. Research progress of deep learning in glioblastoma[J]. Chin J Magn Reson Imaging, 2022, 13(3): 115-117. DOI:10.12015/issn.1674-8034.2022.03.028.


[Abstract] Deep learning, a method of artificial intelligence, has been used for glioblastoma (GBM) in recent years. This method is mainly used in the clinical, pathological, and methodological aspects of GBM. The clinical usage includes the prediction of patient prognosis, differential diagnosis, and tumor radiotherapy. The pathological studies includes the prediction of tumor molecular and genetic expression status, and the identification of pathological tissue. As for the method itself, the most frequently used algorithm is convolutional neural network. Other studies included the comparison among different deep learning models and establishment of deep learning models based on different MRI sequences. This paper is to review the application of deep learning in GBM in detail.
[Keywords] glioblastoma；artificial intelligence；deep learning；magnetic resonance imaging；machine learning

ZHENG Fei   CHEN Xuzhu*  

Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

CHEN XZ, E-mail: radiology888@aliyun.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81772005); Collaborative Innovative Major Special Project Supported by Beijing Municipal Science & Technology Commission (No. Z191100006619088).
Received  2021-07-26
Accepted  2022-02-14
DOI: 10.12015/issn.1674-8034.2022.03.028
Cite this article as: Zheng F, Chen XZ. Research progress of deep learning in glioblastoma[J]. Chin J Magn Reson Imaging, 2022, 13(3): 115-117. DOI:10.12015/issn.1674-8034.2022.03.028.

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