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Research status of application of artificial intelligence technology based on magnetic resonance imaging in pituitary adenomas
YANG Qiuyuan  KE Tengfei  YANG Bin 

Cite this article as: Yang QY, Ke TF, Yang B. Research status of application of artificial intelligence technology based on magnetic resonance imaging in pituitary adenomas[J]. Chin J Magn Reson Imaging, 2022, 13(7): 160-163. DOI:10.12015/issn.1674-8034.2022.07.032.


[Abstract] Pituitary adenoma is a common benign intracranial tumor, but it can show high invasiveness and recurrence rate, and the incidence rate is increasing year by year. Radiomics and deep learning are important research directions of artificial intelligence in the field of medical imaging. They are widely used in tumor imaging research, and play an important role in the heterogeneous diagnosis, efficacy evaluation, and prognosis prediction of pituitary adenomas. This article reviews the application and research progress of radiomics and deep learning in pituitary adenomas.
[Keywords] pituitary adenoma；magnetic resonance imaging；artificial intelligence；radiomics；deep learning；texture feature；preoperative evaluation；prognosis

YANG Qiuyuan1   KE Tengfei2   YANG Bin3*  

1 School of Clinical Medical, Dali University, Dali 671000, China

2 Department of Medical Imaging, Yunnan Cancer Hospital, Kunming 650018, China

3 Department of Medical Imaging, the First People's Hospital of Kunming, Kunming 650051, China

Yang B, E-mail: yangbinapple@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82160348); China International Medical Foundation (No. Z-2014-07-2101); Cultivating Reserve Talents in Medical Disciplines from the Health Committee of Yunnan Province (No. H-2018008).
Received  2022-03-29
Accepted  2022-07-05
DOI: 10.12015/issn.1674-8034.2022.07.032
Cite this article as: Yang QY, Ke TF, Yang B. Research status of application of artificial intelligence technology based on magnetic resonance imaging in pituitary adenomas[J]. Chin J Magn Reson Imaging, 2022, 13(7): 160-163. DOI:10.12015/issn.1674-8034.2022.07.032.

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