[1]解治华,路娜,刘金锋,等.儿童全骨髓全淋巴照射靶区和危及器官自动勾画[J].中国医学物理学杂志,2024,41(2):163-168.[doi:DOI:10.3969/j.issn.1005-202X.2024.02.006]
　XIE Zhihua,LU Na,et al.Auto-segmentation of target areas and organs-at-risk for total marrow and lymphoid irradiation in children[J].Chinese Journal of Medical Physics,2024,41(2):163-168.[doi:DOI:10.3969/j.issn.1005-202X.2024.02.006]
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儿童全骨髓全淋巴照射靶区和危及器官自动勾画()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第2期
页码:
163-168
栏目:
医学放射物理
出版日期:
2024-03-13

文章信息/Info

Title:
Auto-segmentation of target areas and organs-at-risk for total marrow and lymphoid irradiation in children
文章编号:
1005-202X（2024）02-0163-06
作者:
解治华1; 2; 路娜1; 刘金锋3; 侯立霞3; 张富利1
1.解放军总医院第七医学中心放射治疗科， 北京 100700； 2.山东省肿瘤医院放射物理技术科， 山东 济南 250000； 3.山东第一医科大学（山东省医学科学院）放射学院， 山东 泰安 271000
Author(s):
XIE Zhihua1;  2;  LU Na1;  LIU Jinfeng3;  HOU Lixia3;  ZHANG Fuli1
1. Department of Radiotherapy, The Seventh Medical Center of the Chinese PLA General Hospital, Beijing 100700, China 2. Department of Radiophysical Technology, Shandong Tumor Hospital, Jinan 250000, China 3. School of Radiology, Shandong First Medical University (Shandong Academy of Medical Sciences), Taian 271000, China
关键词:
儿童; 放射治疗; 全骨髓全淋巴照射; 自动勾画
Keywords:
Keywords: children radiotherapy total marrow and lymphatic irradiation auto-segmentation
分类号:
R318；R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2024.02.006
文献标志码:
A
摘要:
目的：基于AccuLearning自动勾画训练系统探讨儿童全骨髓全淋巴照射（TMLI）靶区和危及器官自动勾画的可行性。方法：选取2018年至2022年期间30例接受TMLI照射的儿童患者进行研究。患者取仰卧位，采用飞利浦大孔径CT获取CT图像，对靶区和危及器官进行手动勾画和修改，将CT图像和勾画的靶区及危及器官轮廓传至AccuLearning系统，进行自动勾画模型的训练、验证和测试。对测试集中的6例TMLI患者自动勾画结果使用Dice相似性系数（DSC）、95%豪斯多夫距离和平均表面距离进行评估。结果：在6例测试集数据中，除晶体难以被自动勾画以外，其它各个靶区和危及器官中仅有1例患者的胃部DSC值为0.59，其余均大于0.70；6例患者胃的平均DSC值为0.76，其余各器官平均DSC值均大于0.80。结论：通过该模型自动勾画的靶区和危及器官经简单修改后可满足临床计划设计要求。
Abstract:
Abstract: Objective To investigate the feasibility of AccuLearning system for the auto-segmentation of target areas and organs-at-risk (OAR) for total marrow and lymphoid irradiation (TMLI) in children. Methods Thirty pediatric patients who underwent TMLI since 2018 to 2022 were selected. The patients were immobilized in the supine position, and their CT images were acquired on the Philips Brilliance Big Bore CT scanner. After the target areas and OAR were manually delineated and modified, the CT images and manually delineated contours were imported into AccuLearning system for training, validation, and testing of the auto-segmentation model. The auto-segmentation results in 6 TMLI patients in the test set were evaluated in terms of Dice similarity coefficient (DSC), 95% Hausdorff distance and average surface distance. Results On the test set with 6 cases, except for the lens that was difficult to be delineated automatically, the DSC values was above 0.70 for all other target areas and OAR, with only one patient having a DSC value of 0.59 for the stomach. The average DSC value for the stomach in all 6 patients was 0.76, and the average DSC values for the other organs were above 0.80. Conclusion The target areas and OAR automatically delineated with the model can meet the requirements of clinical planning after simple modifications.

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备注/Memo

备注/Memo:
【收稿日期】2023-10-19 【作者简介】解治华，技师，研究方向：肿瘤放射治疗物理，E-mail: zhxie2000@163.com 【通信作者】张富利，主任技师，研究方向：多模态影像引导精确放疗、人工智能在肿瘤放射治疗中的应用，E-mail: radiozfli@163.com
更新日期/Last Update: 2024-02-27