[1]潘兴晨,汪冬,胡丽琴.基于靶区与危及器官重叠度的自动治疗计划设计[J].中国医学物理学杂志,2021,38(10):1203-1208.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.004]
　PAN Xingchen,WANG Dong,HU Liqin.Automatic treatment planning based on the degree of overlap between OAR and PTV[J].Chinese Journal of Medical Physics,2021,38(10):1203-1208.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.004]
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基于靶区与危及器官重叠度的自动治疗计划设计()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第10期
页码:
1203-1208
栏目:
医学放射物理
出版日期:
2021-10-27

文章信息/Info

Title:
Automatic treatment planning based on the degree of overlap between OAR and PTV
文章编号:
1005-202X（2021）10-1203-06
作者:
潘兴晨1; 2; 汪冬1; 胡丽琴1
1.中国科学院合肥物质科学研究院， 安徽 合肥 230031； 2.中国科学技术大学研究生院科学岛分院， 安徽 合肥 230026
Author(s):
PAN Xingchen1; 2;  WANG Dong1;  HU Liqin1
1. Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China 2. Science Island Branch, Graduate School of University of Science and Technology of China, Hefei 230026, China
关键词:
前列腺癌; 重叠度; 自动计划; 危及器官; 剂量体积直方图
Keywords:
Keywords: prostate cancer degree of overlap automatic planning organs-at-risk dose-volume histogram
分类号:
R811.1；R730.55
DOI:
DOI:10.3969/j.issn.1005-202X.2021.10.004
文献标志码:
A
摘要:
目的：根据前列腺癌患者的解剖结构建立微分剂量体积直方图（DVH）数学模型来预测危及器官（OAR）子块的DVH，通过精准预测OAR的DVH并自动设计计划来提高治疗计划的质量和效率。方法：从17例前列腺癌计划中随机选择9例作为训练集，其余为测试集。将靶区外扩到覆盖OAR范围并将外扩区域中的OAR依次分隔成层厚3 mm的子块，根据偏正态高斯函数建立子块微分DVH的精确模型，分析靶区和OAR的重叠度与子块微分DVH关系。利用建立在Pinnacle3上的脚本程序获取测试集8例患者OAR各子块，并通过预测各OAR微分DVH来指导计划。结果：靶区与OAR重叠度越小，微分DVH的变化规律越明显。与原手工计划相比，自动计划的均匀性指数和适形度指数稍优于原手工计划（t=-1.871、3.742；P<0.05）。自动计划靶区的V100%降低，V95%提高，差异均无统计学意义（P>0.05）。自动计划的膀胱V70、V60、V50（t=-2.471、-3.439、 -2.376；P<0.05）以及V40（P>0.05）降低。自动计划的直肠V70、V60、V50以及V40（t=-2.540、-3.416、-2.666、-2.777；P<0.05）降低。相关性分析中，自动计划与原手工计划的膀胱V70、V60、V50和V40的差异值与重叠度呈负或正相关（相关系数=-0.357、-0.976、 -0.857、0.381；P=0.385、0.000、0.007、0.352），V70和V40的差异值与重叠度无统计学意义。结论：基于靶区与OAR重叠度的精确DVH预测模型的前列腺癌自动治疗计划设计使得靶区剂量提高，OAR照射剂量降低。
Abstract:
Abstract: Objective To establish a mathematical model of differential dose-volume histogram (DVH) according to the anatomical structure of prostate cancer patients for predicting the DVH of organs-at-risk (OAR) sub-blocks, and to improve the quality and efficiency of treatment plan by the accurate prediction of the DVH of OAR and automatic treatment planning. Methods Nine of 17 prostate cancer patients were randomly selected as training set and the rest as test set. Planning target volume (PTV) was expanded to cover OAR, and the OAR in the expanded area was successively separated into sub-blocks with a thickness of 3 mm. According to the skewed normal Gaussian function, a precise mathematical model of sub-block differential DVH was established for analyzing the relationship between the degree of overlap between OAR and PTV and sub-block differential DVH. The script program based on Pinnacle3 was used to obtain the OAR sub-blocks of 8 patients in test set, and each OAR differential DVH was predicted to guide the plan. Results The smaller the degree of overlap between OAR and PTV was, the more obvious the variation of differential DVH was. The homogeneity index and conformity index of automatic plan were superior to those of original plan (t=-1.871, 3.742 P<0.05). The V100% of PTV in automatic plan was decreased, while V95% was increased, but the differences were not statistically significant (P>0.05). Compared with those in original plan, the V70, V60, V50 (t=-2.471, -3.439, -2.376 P<0.05) and V40 (P>0.05) of the bladder in automatic plan were lower and the V70, V60, V50 and V40 (t=-2.540, -3.416, -2.666, -2.777 P<0.05) of the rectum were also decreased. The correlation analysis showed that the differences of the V70, V60, V50 and V40 of the bladder between automatic plan and original plan were negatively or positively correlated with the degree of overlap (correlation coefficient=-0.357, -0.976, -0.857, 0.381 P=0.385, 0.000, 0.007, 0.352), but there was no statistical significance between the differences of V70 and V40 and the degree of overlap. Conclusion The automatic treatment plan for prostate cancer designed using the precise DVH prediction model based on the degree of overlap between OAR and PTV increases target dose and decreases OAR irradiation dose.

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备注/Memo

备注/Memo:
【收稿日期】2021-05-06 【基金项目】安徽省科技重大专项（18030801135）；中国科协青年人才托举工程 （2017QNRC001） 【作者简介】潘兴晨，硕士研究生，研究方向：放疗计划的自动计划，E-mail: xcppp@mail.ustc.edu.cn 【通信作者】汪冬，助理研究员，研究方向：放疗计划优化、智能计划，E-mail: 547977312@qq.com
更新日期/Last Update: 2021-10-28