机器学习在调强放疗质量保证中的应用研究进展

doi:10.3760/cma.j.issn.1004-4221.2019.04.012

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Abstract In recent years, the application of machine learning in the field of radiotherapy has been gradually increased along with the development of big data and artificial intelligence technology. Through the training of previous plans, machine learning can predict the results of plan quality and dose verification. It can also predict the multi-leaf collimator (MLC) positioning error and linear accelerator performance. In addition, machine learning can be applied in the quality assurance of intensity-modulated radiotherapy to improve the quality and efficiency of treatment plan and implementation, increase the benefits to the patients and reduce the risk. However, there are many problems, such as difficulty in the selection, extraction and calculation of characteristic value, requirement for large training sample size and insufficient prediction accuracy, which impede its clinical translation and application. In this article, research progress on the application of machine learning in the quality assurance of IMRT was reviewed.

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	Articles by authors
	Li Jiaqi
	Zhang Shuming
	Wang Hao
	Zhang Xile
	Li Jun
	Shi Chengyu
	Sui Jing
	Yang Ruijie

Key words： Intensity-modulated radiotherapy Quality assurance Machine learning

Received: 27 December 2017

Corresponding Authors: Yang Ruijie,Email:ruijyang@yahoo.com

Cite this article:

Li Jiaqi,Zhang Shuming,Wang Hao et al. Research progress on application of machine learning in quality assurance of intensity-modulated radiotherapy[J]. Chinese Journal of Radiation Oncology, 2019, 28(4): 309-313.

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http://journal12.magtechjournal.com/Jweb_fszlx/EN/10.3760/cma.j.issn.1004-4221.2019.04.012 OR http://journal12.magtechjournal.com/Jweb_fszlx/EN/Y2019/V28/I4/309

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