[1]黄仕雄,杨松华,王亮,等.基于预测剂量引导的宫颈癌自动计划研究[J].中国医学物理学杂志,2020,37(9):1101-1106.[doi:10.3969/j.issn.1005-202X.2020.09.004]
 HUANG Shixiong,YANG Songhua,WANG Liang,et al.Automatic planning of radiotherapy for cervical carcinoma based on dose prediction[J].Chinese Journal of Medical Physics,2020,37(9):1101-1106.[doi:10.3969/j.issn.1005-202X.2020.09.004]
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基于预测剂量引导的宫颈癌自动计划研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第9期
页码:
1101-1106
栏目:
医学人工智能
出版日期:
2020-09-25

文章信息/Info

Title:
Automatic planning of radiotherapy for cervical carcinoma based on dose prediction
文章编号:
1005-202X(2020)09-1101-06
作者:
黄仕雄杨松华王亮余功奕倪千喜
湖南省肿瘤医院放射物理技术部,湖南长沙410013
Author(s):
HUANG Shixiong YANG Songhua WANG Liang YU Gongyi NI Qianxi
Department of Radiation Physics and Technology, Hunan Cancer Hospital, Changsha 410013, China
关键词:
宫颈癌重叠体积直方图机器学习自动计划PythonMonaco
Keywords:
cervical cancer overlap volume histogram machine learning automatic planning Python Monaco
分类号:
R815
DOI:
10.3969/j.issn.1005-202X.2020.09.004
文献标志码:
A
摘要:
目的:使用机器学习方法建立宫颈癌计划剂量预测回归模型,并将预测剂量引导生成Monaco 计划系统(TPS)可调用 的优化模板文件,实现宫颈癌的自动计划设计。方法:对50例宫颈癌术后调强治疗计划中的危及器官采集基于重叠体积直 方图的几何特征值和基于剂量直方图的剂量目标值,建模后将模型预测剂量结果自动生成Monaco TPS模板文件,进而由TPS 调用优化。使用该方法对另外10例未参与模型训练的测试病例进行自动计划设计,并和人工设计的计划进行对比分析。 结果:自动计划比手动计划的平均设计时间减少了40 min(P<0.05),且平均调优次数降低了3次(P<0.05),剂量学指标和计 划执行效率上两者无明显差异(P>0.05)。结论:基于预测剂量引导的宫颈癌自动计划可以达到临床要求,并且提高了计划 设计效率。
Abstract:
Objective To establish a regressionmodel for cervical cancer planning dose prediction usingmachine learningmethod, and to realize the automatic planning of radiotherapy for cervical cancer by guiding the predicted dose to generate an optimization template file that can be called byMonaco treatment planning system (TPS).Methods The geometric characteristic values based on overlap volume histogram and the dose target values based on dose volume histogram of organs-at-risk in the postoperative intensity-modulated radiotherapy plans of 50 cervical cancer patients were collected.Aftermodeling, the dose predicted by themodel was automatically generated into aMonaco TPS template file which was then optimized and called by TPS. The proposedmethod was used for automatic planning in 10 test cases that did not participate inmodel training, and the obtained plans were then compared with themanually designed plans. Results Compared with those ofmanual planning, the average design time of automatic planning was reduced by 40min (P<0.05), and there were 3 optimization times less in automatic planning (P<0.05). No significant difference was found in dosimetry indexes and plan execution efficiency (P>0.05). Conclusion The automatic planning of radiotherapy for cervical cancer based on dose prediction canmeet clinical requirements and improve planning efficiency.

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备注/Memo

备注/Memo:
【收稿日期】2020-04-25 【基金项目】湖南省卫健委科研计划课题(B2017092);长沙市科技局科 技计划(kq1801106) 【作者简介】黄仕雄,硕士,工程师,研究方向:放射治疗,E-mail: huangshixiong@hnca.org.cn 【通信作者】倪千喜,E-mail: niqianxi@hnca.org.cn
更新日期/Last Update: 2020-09-25