Research progress on application of machine learning in quality assurance of intensity-modulated radiotherapy
Li Jiaqi1,Zhang Shuming1,Wang Hao1,Zhang Xile1,Li Jun1,Shi Chengyu2,Sui Jing3,Yang Ruijie1
1Department of Radiation Oncology,Peking University Third Hospital,Beijing 100191,China; 2Memorial Sloan—Kettering Cancer Center,New York NYl0065,United States of America; 3National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijng 100190,China
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.
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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