人工智能技术在鼻咽癌放疗危及器官自动勾画中的应用研究

doi:10.11904/j.issn.1002-3070.2021.02.007

Abstract

Abstract: Objective The aim of this study was to evaluate the clinical value of the software for automatic delineation of organs at risk in radiotherapy based on artificial intelligence technology. Methods Thirty cases of nasopharyngeal carcinoma treated in the department of Oncology at Zigong First people's Hospital from April 2019 and April 2020 were selected for the study.First,the AccuContour software was used to delineate the organs at risk(OARs)of 30 nasopharyngeal cancer patients,and then the Varian Eclipse v13.6 treatment planning system was used to manually delineate the OARs.Finally,the OARs automatically sketched and manually sketched were compared on the Eclipse system,and the geometric accuracy of the automatic sketching software was evaluated by comparing the volume difference,location difference,coincidence difference,similarity difference and other parameters,so as to verify its feasibility in the clinical application. Results Compared with manual delineation,the volume difference of small-size organs was larger,reaching 0.8,but the location difference was smaller.The coincidence and similarity of large size organs were larger,basically above 0.7.There was a big gap between automatic delineation and manual one in some CT sections of OARs. Conclusion The software based on artificial intelligence technology has a high accuracy in the delineation of organs at risk in nasopharyngeal carcinoma,and can be used in clinical work with slight modification,which can improve the efficiency of clinical work.

Key words: Nasopharynx carcinoma;Automatic segmentation;Organs at risk;Radiotherapy;Accuracy

CLC Number:

R730.55

WU Zhe, PANG Ya, MING Zhi, LIU Ke, WANG Dong, YAN Jun. Application of artificial intelligence technology in automatic delineation of organs at risk in nasopharyngeal carcinoma radiotherapy[J]. Journal of Practical Oncology, 2021, 35(2): 137-141.

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URL: http://journal09.magtechjournal.com/Jwk3_syzlxzz/EN/10.11904/j.issn.1002-3070.2021.02.007

http://journal09.magtechjournal.com/Jwk3_syzlxzz/EN/Y2021/V35/I2/137

References

1 Barton MB,Frommer M,Shafiq J.Role of radiotherapy in cancer control in low-income and middle-income countries[J].Lancet Oncol,2006,7(7):584-595.
2 Chen AM,Chin R,Beron P,et al.Inadequate target volume delineation and local-regional recurrence after intensity-modulated radiotherapy for human papillomavirus-positive oropharynx cancer[J].Radiother Oncol,2017,23(3):412-418.
3 Kosmin M,Ledsam J,Romera-Paredes B,et al.Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer[J].Radiother Oncol,2019,135:130-140.
4 Velker VM,Rodrigues GB,Dinniwell R,et al.Creation of RTOG compliant patient CT-atlases for automated atlas based contouring of local regional breast and high-risk prostate cancers[J].Radiat Oncol,2013,8(1):188.
5 Barley S,Antoine C,Webster G,et al.Atlas-based auto-contouring-balancing accuracy with efficiency in onQ rts[J].Eur Oncol and Haematol,2014,10(2):98-101.
6 张玉海,李月敏.人工智能在肿瘤放射治疗中的研究进展[J].实用肿瘤学杂志,2019,33(6):570-574.
7 李华玲,王沛沛,李金凯,等.体积对自动勾画软件勾画危及器官准确性的影响[J].中国医学物理学杂志,2020,37(7):797-802.
8 王沛沛,李金凯,李彩虹,等.基于人工智能技术的危及器官自动勾画在胸部肿瘤中的应用[J].中国医学物理学杂志,2019,36(11):1346-1349.
9 Macchia ML,Fellin F,Maurizio A.Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck,prostate and pleural cancer[J].Radiat Oncol,2012,7(1):160.
10 Ju ZJ,Wu QN,Yang W,et al.Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples[J].Acta Oncol,2020,59(8):933-939.
11 Wang J,Chen W,Studenski M,et al.A semi-automated tool for treatment plan-quality evaluation and clinical trial quality assurance[J].Phys Med Biol,2013,58(13):181-187.
12 Tong N,Gou S,Yang S,et al.Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks[J].Med Phys,2018,45(10):4558-4567.
13 Lin L,Dou Q,Jin YM,et al.Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma[J].Radiology,2019,291(3):677-686.
14 Li S,Xiao J,He L,et al.The tumor target segmentation of nasopharyngeal cancer in CT images based on deep learning methods[J].Technol Cancer Res Treat,2019,18(2):1-8.
15 Stapleford LJ,Lawson J,Perkins C,et al.Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer[J].Int J Radiat Oncol Biol Phys,2010,77(3):959-966.
16 Sims R,Isambert A,Gregoire V,et al.A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck[J].Radiother Oncol,2009,93(3):474-478.
17 Hvid CA,Elstrom UV,Jensen K,et al.Accuracy of softwareassisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy[J].Acta Oncol,2016,55(11):1324-1330.
18 Ciardo D,Gerardi MA,Vigorito S,et al.Atlas-based segmentation in breast cancer radiotherapy:evalution of specific and generic-purposed atlases[J].Breast,2017,4(32):44-52.

[1]	CHEN Hongbo, YU Zengrong, DING Xiaofan, XIAO Lin. Progress of correlation between bone marrow dose-volume parameters and acute hematologic toxicity in pelvic malignant tumor [J]. Journal of Practical Oncology, 2019, 33(4): 361-366.
[2]	DU Yesheng, HU Songliu, XU Xiangying. Study of oligodendrocyte metastasis in non-small cell lung cancer by stereotactic radiotherapy [J]. PRACTICAL ONCOLOGY JOURNAL, 2018, 32(2): 154-158.
[3]	YANG Meijian, BAO Yingna, ZHAO Jianguo. Advances in neoadjuvant therapy of rectal cancer [J]. PRACTICAL ONCOLOGY JOURNAL, 2016, 30(5): 453-457.
[4]	LIN Quan, E Mingyan. Research progress on the treatment of reducing cardiac injury related to breast cancer radiotherapy [J]. PRACTICAL ONCOLOGY JOURNAL, 2016, 30(2): 189-192.
[5]	WANG Li, XU Qingyong. Research progress of decorin gene in oncology [J]. PRACTICAL ONCOLOGY JOURNAL, 2016, 30(1): 44-47.
[6]	WANG Di,HE Yuehan,WANG Xinghua,YU Hongbo,WANG Yujun,CUI Yali. Clinical observation of ¹³¹I in treatment of adolescents with differentiated thyroid carcinoma [J]. PRACTICAL ONCOLOGY JOURNAL, 2015, 29(3): 193-196.
[7]	TENG Kai,ZHANG Yong,LIU Li. Present situation and advances on the nimotuzumab related radiosensitivity research [J]. PRACTICAL ONCOLOGY JOURNAL, 2015, 29(3): 245-248.
[8]	DONG Wenlei,LIU Lili,CHEN Lin,HU Hongtao,BAI Yanling,HU Songliu,FENG Lina,LI Yang,XU Weiwei. 3DCRT and IGRT therapy technology for liver carcinoma function and evaluation [J]. PRACTICAL ONCOLOGY JOURNAL, 2012, 26(3): 209-213.
[9]	LI Shouguo,ZHANG Qingluo,HOU Rurong,WANG Jiezhong,PAN Jianji. Dosimetric comparison in intensity-modulated radiotherapy(IMRT)with 5-field for thoracic middle esophageal carcinoma [J]. PRACTICAL ONCOLOGY JOURNAL, 2012, 26(3): 251-255.
[10]	. [J]. PRACTICAL ONCOLOGY JOURNAL, 2011, 25(3): 246-248.
[11]	LI Lupeng, ZHANG Jingyan, YU youtao, HE Dongfeng. Application of 3-dimensional digital subtraction in the diagnosis and treatment of the obstructive jaundice caused by the malignant tumours [J]. PRACTICAL ONCOLOGY JOURNAL, 2010, 24(5): 439-442.
[12]	. [J]. PRACTICAL ONCOLOGY JOURNAL, 2010, 24(5): 458-460.
[13]	WANG Shun, ZHANG Dashui, LIU Sha, WANG Mingkun, PAN Tao. Clinical analysis on postoperative radiation induced lung injury in patient with breast cancer [J]. PRACTICAL ONCOLOGY JOURNAL, 2010, 24(4): 332-336.
[14]	JIANG Ji, RONG Guoyan, WANG Xinghua, CUI Yali. Therapeutic evaluation on bremsstrahlung imaging after ⁸⁹Sr administration in patients with bone metastases [J]. PRACTICAL ONCOLOGY JOURNAL, 2010, 24(3): 234-237.
[15]	. [J]. PRACTICAL ONCOLOGY JOURNAL, 2010, 24(3): 274-276.

Application of artificial intelligence technology in automatic delineation of organs at risk in nasopharyngeal carcinoma radiotherapy

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