机器学习在调强放疗质量保证中的应用研究进展  

Research progress on application of machine learning in quality assurance of intensity-modulated radiotherapy

作　　者：李佳奇 张书铭 王皓[1] 张喜乐[1] 李君[1] 石成玉 隋婧[3] 杨瑞杰[1] Li Jiaqi;Zhang Shuming;Wang Hao;Zhang Xile;Li Jun;Shi Chengyu;Sui Jing;Yang Ruijie(Department of Radiation Oncology,Peking University Third Hospital,Beijing 100191,China;Memorial Sloan-Kettering Cancer Center,New York NY10065,United States of America;National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijng 100190,China)

机构地区：[1]北京大学第三医院放疗科,100191 [2]美国纽约市纪念斯隆-凯特琳癌症中心,10065 [3]中国科学院自动化研究所模式识别国家重点实验室,北京100190

出　　处：《中华放射肿瘤学杂志》2019年第4期309-313,共5页Chinese Journal of Radiation Oncology

基　　金：国家自然科学基金(81071237、81372420).

摘　　要：近年来随着大数据分析与人工智能技术的发展,机器学习在放疗领域的应用研究逐渐增多。通过既往计划训练,机器学习可预测计划质量及剂量验证结果。机器学习也可以预测MLC位置误差、加速器性能。机器学习用于调强放疗质量保证能提高治疗计划和实施的质量和效率,增加患者获益并降低风险。机器学习用于调强放疗质量保证目前尚存在特征值选择、提取和计算复杂,要求训练样本量大,预测精度不够等问题,阻碍了其临床转化和应用。本文综述其研究进展。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.

关 键 词：调强放射疗法 质量保证 机器学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R730.55[自动化与计算机技术—控制科学与工程]