[1]闫凤,牛振洋,费振乐,等.机器学习在肺癌VMAT计划中对危及器官剂量预测的可行性[J].中国医学物理学杂志,2020,37(7):934-939.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.025]
　YAN Feng,NIU Zhenyang,FEI Zhenle,et al.Feasibility of machine learning in OAR dosimetric prediction in VMAT plan for lung cancer[J].Chinese Journal of Medical Physics,2020,37(7):934-939.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.025]
点击复制

机器学习在肺癌VMAT计划中对危及器官剂量预测的可行性()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第7期
页码:
934-939
栏目:
医学人工智能
出版日期:
2020-07-25

文章信息/Info

Title:
Feasibility of machine learning in OAR dosimetric prediction in VMAT plan for lung cancer
文章编号:
1005-202X（2020）07-0934-06
作者:
闫凤1; 牛振洋1; 费振乐1; 吴先想2; 崔相利3; 刘苓苓3
1.联勤保障部队第901医院放疗科， 安徽 合肥 230031； 2.蚌埠医学院第一附属医院放疗科， 安徽 蚌埠 233004； 3.中国科学院合肥肿瘤医院， 安徽 合肥 230031
Author(s):
YAN Feng1;  NIU Zhenyang1;  FEI Zhenle1;  WU Xianxiang2;  CUI Xiangli3;  LIU Lingling3
1. Department of Radiation Oncology, No.901 Hospital of PLA, Hefei 230031, China 2. Department of Radiation Oncology, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233004, China 3. Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China
关键词:
肺癌; 容积旋转调强; 人工神经网络模型; 机器学习; 剂量体积直方图
Keywords:
Keywords: lung cancer volumetric modulated arc therapy artificial neural network model machine learning dose-volume histogram
分类号:
R815.6；R318
DOI:
DOI:10.3969/j.issn.1005-202X.2020.07.025
文献标志码:
A
摘要:
目的：探讨机器学习在肺癌容积旋转调强（VMAT）治疗计划对心脏和肺的剂量体积直方图（DVH）预测的可行性。方法：选取51例肺癌VMAT计划，随机选取其中43例为训练组，剩余8例为验证组。分析训练组中患者的解剖信息与两侧肺V5、V20和心脏V30、V40的相关性。采用机器学习方法，以解剖信息为输入、危及器官（OAR）的DVH为输出，分别构建并训练关于两侧肺以及心脏的人工神经网络模型。将验证组中8例VMAT计划中的解剖信息分别输入到已经构建好的人工神经网络模型，分别预测OAR的DVH。结果：两侧肺V5、V20和心脏V30、V40受自身体积大小影响可忽略，受OAR与靶区的空间相对位置关系影响较大。患侧肺、对侧肺、心脏的人工神经网络结构模型中隐藏层分别含有41、38、34个神经结点，线性回归系数分别为0.994、0.975、0.986。对验证组中患侧肺和对侧肺的V5、V20的预测误差分别为2.70%[±]1.83%、2.84[%±]1.97%和13.7%[±]7.8%、0.72[%±]0.75%，对心脏V30、V40的预测误差分别为3.20[%±]0.63%、2.1[%±]1.5%，仅对侧肺V5的预测值和实际值差异有统计学意义（P<0.05）。结论：采用人工神经网络方法可以对肺癌VMAT计划中解剖信息与OAR的DVH数据进行学习，构建的人工神经网络模型可预测出患侧肺、心脏V25[~]V60和对侧肺V20的DVH数据，可为临床计划设计提供参考。
Abstract:
Abstract: Objective To investigate the feasibility of machine learning for dose-volume histogram (DVH) predictions of the heart and the lungs in volumetric modulated arc therapy (VMAT) plan for lung cancer. Methods Among the VMAT plans of 51 cases of lung cancer, 43 VMAT plans were randomly selected as training group, and the other 8 plans were taken as validation group. The anatomical information of patients in training group was analyzed, and the relationships between the V5, V20 of bilateral lungs and the V30, V40 of the heart were investigated. With the anatomical information as the input and the DVH of organs-at-risk (OAR) as the output, machine learning method was adopted to construct and train the artificial neural network models for bilateral lungs and the heart, separately. The anatomical information of 8 VMAT plans in validation group was input into the constructed artificial neural network model for predicting the DVH of OAR. Results The V5, V20 of bilateral lungs and the V30, V40 of the heart were affected by the relative spatial relationship between OAR and target areas, but didnt affected by the volume of OAR itself. In the artificial neural network structure models of the affected lung, the contralateral lung and the heart, the hidden layers contained 41, 38 and 34 neural nodes, respectively, and the linear regression coefficients were 0.994, 0.975 and 0.986, respectively. In validation group, the prediction errors for the V5, V20 of the affected lung were 2.70%±1.83% and 2.84%±1.97%, and those for the V5, V20 of the contralateral lung were 13.7%±7.8% and 0.72%±0.75% and the prediction errors for the V30 and V40 of the heart were 3.20%±0.63% and 2.1%±1.5%, respectively. There was statistically significant difference between the predicted and actual values of the V5 of the contralateral lung. Conclusion Artificial neural network method can learn the anatomical information in the lung cancer VMAT plan and the DVH data of OAR. The constructed artificial neural network model can be used to accurately predict the DVH of the affected lung, the V25-V60 of the heart and the V20 of the contralateral lung, providing reference for clinical treatment planning.

相似文献/References:

[1]孙小喆,孟慧鹏,史孝伟,等.胸部肿瘤容积旋转调强放射治疗摆位误差分析[J].中国医学物理学杂志,2015,32(01):34.[doi:10.3969/j.issn.1005-202X.2015.01.009]
[2]史贵连,叶福丽.肺癌调强放疗计划的设计[J].中国医学物理学杂志,2015,32(03):361.[doi:10.3969/j.issn.1005-202X.2015.03.013]
[3]孙晓欢,周咏春,谭丽娜,等.容积旋转调强与固定野调强在宫颈癌术后放疗中的剂量学比较[J].中国医学物理学杂志,2014,31(01):4604.[doi:10.3969/j.issn.1005-202X.2014.01.003]
[4]朱夫海,吴伟章,王 勇,等.肺癌螺旋断层放疗计划设计的初步研究[J].中国医学物理学杂志,2014,31(04):4979.[doi:10.3969/j.issn.1005-202X.2014.04.002]
[5]翁邓胡,王 建,尹中明,等.基于锥形束CT研究肺癌图像引导放疗的内靶区外放边界值[J].中国医学物理学杂志,2014,31(04):5012.[doi:10.3969/j.issn.1005-202X.2014.04.009]
[6]彭莹莹,张书旭,谭剑明,等.基于PCNN的PET/CT图像分割在肺癌靶区勾画中的应用[J].中国医学物理学杂志,2014,31(04):5022.[doi:10.3969/j.issn.1005-202X.2014.04.011]
[7]王 涛,王运来.基于4D-CT和Mimics软件模拟分析肺癌肿瘤的呼吸运动规律[J].中国医学物理学杂志,2014,31(05):5132.[doi:10.3969/j.issn.1005-202X.2014.05.008]
[8]张俊俊,邱小平,李奇欣,等.ArcCheck系统在鼻咽癌容积旋转调强剂量验证中的应用[J].中国医学物理学杂志,2014,31(05):5136.[doi:10.3969/j.issn.1005-202X.2014.05.009]
[9]周 琼,周剑良,张一戈,等.基于锥形束CT肺癌放射治疗两种体位固定技术摆位误差的研究[J].中国医学物理学杂志,2014,31(06):5258.[doi:10.3969/j.issn.1005-202X.2014.06.008]
[10]谭丽娜,孙晓欢,马奎,等.三维剂量验证系统Delta4在容积旋转调强计划剂量验证中的应用[J].中国医学物理学杂志,2013,30(06):4497.[doi:10.3969/j.issn.1005-202X.2013.06.007]
[11]张矛,金海国,苏清秀,等.肺癌静态调强与容积旋转调强放射治疗间比较[J].中国医学物理学杂志,2013,30(05):4364.[doi:10.3969/j.issn.1005-202X.2013.05.006]
[12]邵凯南,杜锋磊,李剑龙.RayArc在胸部旋转调强放射治疗计划设计中的应用[J].中国医学物理学杂志,2017,34(2):131.[doi:10.3969/j.issn.1005-202X.2017.02.005]
　Application of RayArc in volumetric modulated arc therapy planning of chest cancer[J].Chinese Journal of Medical Physics,2017,34(7):131.[doi:10.3969/j.issn.1005-202X.2017.02.005]
[13]雷伟杰,曹瑞芬,贾婧,等. 基于几何不确定性鲁棒性优化在肺癌容积旋转调强计划中的应用[J].中国医学物理学杂志,2017,34(9):865.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.001]
　[J].Chinese Journal of Medical Physics,2017,34(7):865.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.001]
[14]魏敏,牛振洋,刘苓苓,等.最小子野宽度对肺癌容积调强弧形治疗计划质量的影响[J].中国医学物理学杂志,2020,37(3):277.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.004]
　WEI Min,NIU Zhenyang,LIU Lingling,et al.Effect of minimum segment width on the quality of volumetric modulated arc therapy plan for lung cancer[J].Chinese Journal of Medical Physics,2020,37(7):277.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.004]

备注/Memo

备注/Memo:
【收稿日期】2020-01-19 【基金项目】安徽省公益性技术应用研究联动计划项目（1704f0804051） 【作者简介】闫凤，初级职称，研究方向：肿瘤放射技术，E-mail: 1846759583@qq.com 【通信作者】吴先想，初级职称，研究方向：医学物理，E-mail: 1225880316@qq.com
更新日期/Last Update: 2020-07-28