[1]刘炜焜,周凌宏.基于双层感知机网络的乳腺癌调强放疗计划设计[J].中国医学物理学杂志,2023,40(6):661-666.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.001]
　LIU Weikun,ZHOU Linghong.Intensity-modulated radiotherapy planning for breast cancer based on two-layer perceptron neural network[J].Chinese Journal of Medical Physics,2023,40(6):661-666.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.001]
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基于双层感知机网络的乳腺癌调强放疗计划设计()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第6期
页码:
661-666
栏目:
医学放射物理
出版日期:
2023-06-27

文章信息/Info

Title:
Intensity-modulated radiotherapy planning for breast cancer based on two-layer perceptron neural network
文章编号:
1005-202X（2023）06-0661-06
作者:
刘炜焜; 周凌宏
南方医科大学生物医学工程学院， 广东 广州 510515
Author(s):
LIU Weikun;  ZHOU Linghong
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
关键词:
乳腺癌; 调强放射治疗; 机器学习; 自动计划; 感知机; 剂量预测
Keywords:
Keywords: breast cancer intensity-modulated radiotherapy machine learning automatic planning perceptron dose prediction
分类号:
R318；R815
DOI:
DOI:10.3969/j.issn.1005-202X.2023.06.001
文献标志码:
A
摘要:
目的：利用左乳腺癌靶区的解剖结构几何特性与对应放疗计划的计划设计参数、部分关键剂量体积直方图（DVH）信息建立双层感知机网络，实现新患者放疗计划的自动设计以及部分关键DVH信息的预测。方法：采集50例左乳腺癌IMRT计划，提取靶区的重叠体积直方图特征、靶区切线角度特征作为输入特征，射野角度、目标函数参数以及部分关键DVH信息作为输出特征，训练双层感知机网络，预测10例新患者IMRT计划的射野角度和目标函数参数用于设计自动计划，经配对t检验分析自动计划、手动计划的剂量学结果与部分关键DVH信息预测结果，以验证左乳腺癌IMRT计划自动设计方法的可行性，以及对部分关键DVH信息预测的准确性。结果：自动计划基本满足临床要求；自动计划中靶区的Dmin、Dmax、Dmean、Vprescription、D5、D95、CI、HI，患侧肺的V20、V5，心脏的V20与手动计划比较差异没有统计学意义（P>0.05）；自动计划设计耗时远小于手动计划设计耗时；预测的部分关键DVH信息包括PTV的Dmin、Dmax、Dmean、Vprescription，患侧肺的V20、V5，心脏的V20，分别与手动计划和自动计划相比，仅在PTV的Dmean、Vprescription和患侧肺的V20有统计学意义（P<0.05），但符合临床放疗需求和期望。结论：通过双层感知机网络设计的自动计划与手动计划质量相当，并且大大减少了设计的耗时，预测的关键剂量学信息也能为计划质量评估提供参考。
Abstract:
Abstract: Objective To establish a two-layer perceptron neural network based on the anatomical characteristics of the left-sided breast cancer target area, the planning parameters of the corresponding radiotherapy plan and some key dose-volume histogram (DVH) information for realizing the automatic radiotherapy planning and the prediction of some key DVH information. Methods The two-layer perceptron neural network was trained with the overlapping volume histogram features and tangential angles in the intensity-modulated radiotherapy (IMRT) plans of 50 cases of left-sided breast cancer as the input, and the field angles, objective function parameters and some key DVH information as output. The predicted field angles and objective function parameters for IMRT plans of 10 new cases were used for automatic planning. The dosimetric results of automatic plans and manual plans and some key DVH information predicted by the network were analyzed using paired t-test to verify the feasibility of the method for automatic IMRT planning for left-sided breast cancer and test the accuracy of prediction on some key DVH information. Results The automatic plans basically met the clinical requirements. The differences between automatic plans and manual plans in the Dmin, Dmax, Dmean, Vprescription, D5, D95, CI, HI of PTV, the V20, V5 of ipsilateral lung and the V20 of heart were trivial (P>0.05). The time required for automatic planning was much less than that required for manual planning. The predicted key DVH information included the Dmin, Dmax, Dmean, Vprescription of PTV, the V20, V5 of ipsilateral lung and the V20 of heart. Only the Dmean, Vprescription of PTV and the V20 of ipsilateral lung in automatic plans were different from those in manual plans, but all of them conformed to the needs and expectations of clinical radiotherapy. Conclusion The automatic planning with the two-layer perceptron neural network can result in the same plan quality as manual plans, and greatly shorten planning time. The predicted dosimetric information can also provide reference for the plan quality evaluation.

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备注/Memo

备注/Memo:
【收稿日期】2023-01-20 【基金项目】国家自然科学基金（82272131） 【作者简介】刘炜焜，硕士研究生，研究方向：肿瘤放射物理，E-mail: 13068822686@163.com 【通信作者】周凌宏，教授，研究方向：肿瘤放射物理、医学放射成像方法、肿瘤大数据与人工智能医学应用，E-mail: smart@smu.edu.cn
更新日期/Last Update: 2023-06-28