实用肿瘤杂志2024，Vol.39Issue(6)：536-541,6.DOI:10.13267/j.cnki.syzlzz.2024.079
基于深度学习的鼻咽癌放疗临床靶区体积和危及器官自动勾画研究
Automatic contouring of clinical target volume and organs at risk in radiotherapy for nasopharyngeal carcinoma based on deep learning
摘要
Abstract
Objective To construct a U-Net model based on deep learning to realize the automatic contouring of clinical target volume(CTV)and organs at risk(OARs)of radiotherapy plan for nasopharyngeal carcinoma(NPC)patients,and analyze its feasibility and superi-ority as compared with atlas-based auto-segmentation(ABAS)method.Methods The CT images of 150 NPC patients undergoing radio-therapy at Northern Jiangsu People's Hospital,from January to September 2022,were selected and preprocessed to construct an automatic segmentation model based on U-Net.Ninety cases were used as a training set,10 cases as a verification set and the remaining 50 cases as a test set.The contouring accuracy of the U-Net automatic contouring model for the CTV and OARs of NPC were calculated and compared with the automatic contouring module of ABAS.Results The Dice similarity coefficients(DSCs)of CTV and OARs including brainstem,spinal cord,left eye,right eye,left lens,right lens,left optic nerve,right optic nerve,left mandible,right mandible,left parotid gland,right parotid gland,left temporal lobe,and right temporal lobe,were(0.76±0.03),(0.93±0.02),(0.92±0.03),(0.93±0.02),(0.94±0.03),(0.90±0.03),(0.91±0.02),(0.78±0.06),(0.77±0.05),(0.95±0.04),(0.95±0.02),(0.80±0.04),(0.81±0.03),(0.77±0.05),and(0.76±0.04),respectively.Except for CTV,optic nerves,parotid glands and temporal lobes,the Hausdorff distances(HDs)of other organs were≤5.60 mm and the overlap ratios(ORs)were≥0.80.Compared to ABAS,U-Net had higher DSCs,lower HDs,and higher ORs for the automatic contouring of CTV and OARs(all P<0.05).U-Net also took less time to delineate each organ,and reduced the overall time con-sumption by(176.73±54.08)seconds(P<0.05).Conclusions U-Net realized the automatic contouring of CTV and OARs in NPC radio-therapy,and improved the contouring efficiency for clinicians.The automatic contouring model based on deep learning had high feasibility and advantages.关键词
鼻咽癌/深度学习/U-Net/放射治疗/自动勾画Key words
nasopharyngeal carcinoma/deep learning/U-Net/radiotherapy/automatic contouring引用本文复制引用
钱杰伟,陈雪梅,李军,程品晶,单国平,张获,桂龙刚,柏正璐..基于深度学习的鼻咽癌放疗临床靶区体积和危及器官自动勾画研究[J].实用肿瘤杂志,2024,39(6):536-541,6.基金项目
浙江省放射肿瘤学重点实验室开放课题(2022ZJCCRAD05) （2022ZJCCRAD05）
江苏省苏北人民医院科研基金项目(yzucms202004) （yzucms202004）
