[1]刘劲光,刘国才.基于先验知识的头颈部肿瘤调强放疗计划多目标优化方法[J].中国医学物理学杂志,2021,38(3):265-276.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.001]
　LIU Jingguang,LIU Guocai.Prior knowledge-based multi-objective optimization method for intensity-modulated radiotherapy planning of head and neck cancer[J].Chinese Journal of Medical Physics,2021,38(3):265-276.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.001]
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基于先验知识的头颈部肿瘤调强放疗计划多目标优化方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第3期
页码:
265-276
栏目:
医学放射物理
出版日期:
2021-03-30

文章信息/Info

Title:
Prior knowledge-based multi-objective optimization method for intensity-modulated radiotherapy planning of head and neck cancer
文章编号:
1005-202X（2021）03-0265-12
作者:
刘劲光1; 2; 刘国才1
1.湖南大学电气与信息工程学院， 湖南 长沙 410082； 2.湖南工程学院计算科学与电子学院， 湖南 湘潭 411104
Author(s):
LIU Jingguang1;  2;  LIU Guocai1
1. School of Electrical and Information Engineering, Hunan University, Changsha 410082, China 2. School of Computational Science and Electronics, Hunan Institute of Engineering, Xiangtan 411104, China
关键词:
头颈部肿瘤; 调强放射治疗; 多目标优化; Pareto前沿逼近
Keywords:
Keywords: head and neck cancer intensity modulated radiotherapy multi-criteria optimization Pareto frontier approximation
分类号:
R318；R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2021.03.001
文献标志码:
A
摘要:
头颈部肿瘤调强放射治疗计划本质上是目标函数个数大于20、优化变量数大于1 000的复杂多目标优化问题，目前没有精确求其Pareto最优解集的有效方法。本文提出了一种新的有效近似求解方法。首先，根据71例头颈部肿瘤患者的放疗剂量先验知识，对各危及器官的线性EUD按相关系数进行R型聚类分析，将所有危及器官聚为3类；其次，将所有属于同类危及器官的线性EUD均值看作目标函数，联合肿瘤靶区的约束条件，构造一个维度较低的多目标优化模型；最后，通过基于角解的增强夹心算法逼近该低维、局部的Pareto前沿，并通过可视化导航方法快速选出一个调强放疗计划。对比传统的多目标调强放疗计划优化方法，该方法能显著减少目标函数的个数，极大程度地简化Pareto前沿。实验结果表明，本文方法可以将头颈部肿瘤调强放疗计划多目标优化模型中的20~21个目标函数减少到6~7个，并能有效确定其近似的Pareto前沿，找到与临床放疗计划质量相当、部分指标甚至更优的放疗计划。
Abstract:
Abstract: Intensity-modulated radiotherapy (IMRT) planning of head and neck cancer is a complex multi-criteria optimization with more than 20 criteria and thousands of decision variables. So far, there is no effective method that can determine the accurate Pareto optimal solution set. Herein an effective approximate solution method is proposed. Based on the prior knowledge of the dose distribution of 71 patients with head and neck cancer, R-mode clustering analysis is conducted on the linear EUD of all organs-at-risk according to correlation coefficients, and all organs-at-risk are classified into 3 categories. Secondly, the mean value of linear EUD of organs-at-risk of the same category is regarded as the criteria, and then is combined with the constraints of tumor target to construct a multi-criteria optimization model with a lower dimension. Finally, the enhancement sandwich algorithm based on corner solutions is used to approximate the low-dimensional and partial Pareto frontier, and an IMRT plan is selected through the visual navigation method quickly. Compared with the traditional multi-criteria optimization method, this method can significantly reduce the number of criteria and remarkably simplify Pareto frontier. Experiment results show that the number of criteria for the multi-criteria optimization model of IMRT plan for head and neck cancer is reduced from 20-21 to 6-7, and that the approximate Pareto frontier can be determined effectively, an IMRT plan can be derived with the same quality as the clinical plan, with some indexes even better.

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备注/Memo

备注/Memo:
【收稿日期】2020-10-16 【基金项目】国家自然科学基金（62071176, 61671204）；湖南省科技计划重点研发项目（2016WK2001）；湖南省教育厅项目（18C0702） 【作者简介】刘劲光，博士，研究方向：智能医疗系统与多目标优化，E-mail: 8875700@163.com 【通信作者】刘国才，教授，研究方向：智能医疗系统、智能医学影像分析，E-mail: lgc630819@hnu.edu.cn
更新日期/Last Update: 2021-03-30