Research progress on application of machine learning in quality assurance of intensity-modulated radiotherapy
10.3760/cma.j.issn.1004-4221.2019.04.012
- VernacularTitle:机器学习在调强放疗质量保证中的应用研究进展
- Author:
Jiaqi LI
1
;
Shuming ZHANG
;
Hao WANG
;
Xile ZHANG
;
Jun LI
;
Chengyu SHI
;
Jing SUI
;
Ruijie YANG
Author Information
1. 北京大学第三医院放疗科 100191
- Keywords:
Intensity-modulated radiotherapy;
Quality assurance;
Machine learning
- From:
Chinese Journal of Radiation Oncology
2019;28(4):309-313
- CountryChina
- Language:Chinese
-
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.
