Objective:To investigate the clinical effect of artificial intelligence(AI)technique in delineating target volume for patients with lung cancer during radiotherapy.Methods:A total of 60 patients with lung cancer who received radiotherapy in Pingxiang People's Hospital from September 2021 to March 2023 were selected,and they were divided into control group and observation group by random envelope method,with 30 cases in each group.The control group outlined target volume as conventional method.The observation group adopted deep learning technique to conduct train,and then,UNet network model was output and was used to complete automatic delineation for the target volume of radiotherapy for patients.The near-term efficacy,planning target region volume,radiation dose of target volume,volume and dose of organ at risk(OAR),survival time and incidence of adverse reactions were compared between two groups.Results:The objective relief rate(ORR)of observation group was 70.0％(21/30)after intervention,which was higher than that[46.67％(14/30)]of control group,and the difference was statistically significant(x2=5.691,P＜0.05).The radiation doses of internal target volume(ITV)and planning target volume in observation group were lower respectively than those in control group(t=4.591,4.934,P＜0.05),and the differences of them were significant,respectively.The volume percentages(V20,V5)of the exposed radiation dose that were higher than 20 Gy and 5 Gy in normal lung tissue,the exposed mean lung dose(MLD)of bilateral lungs and the exposed dose of 1cc volume(D1cc)of bilateral lungs in observation group were all lower than those in control group,the differences were statistically significant(t=5.249,4.571,6.092,5.339,P＜0.05),respectively.There was no statistical significance in the incidence of adverse reaction between two groups(P＞0.05).Conclusion:The application of AI technique in delineating target volume of radiotherapy for lung cancer can improve ORR,which is helpful to decrease the planning target volume,D95 and conformal index,and reduce the volume and dose of OAR.It does not increase the incidence of adverse reactions.

[摘 要] 目的：探究双特异性磷酸酶26（DUSP26）在肺腺癌（LUAD）A549细胞增殖、迁移和侵袭中的作用及其分子机制。方法：检索肿瘤数据库GEPIA2网站DUSP26表达数据，分析DUSP26在LUAD患者和正常人肺组织中的表达差异。收集2022年10月至2023年10月期间萍乡市人民医院手术切除的12例LUAD组织和癌旁组织标本，通过免疫组织化学（IHC）和WB法检测DUSP26在LUAD组织和癌旁组织之间的表达差异；通过WB法检测DUSP26在4种LUAD细胞（A549、SK-LU-1、Calu-3、H1299）和2种正常支气管上皮细胞（BEAS-2B、HBEC）中的表达差异。利用慢病毒转染细胞的方法构建稳定过表达DUSP26（DUSP26-OE）及阴性对照（DUSP26-OENC）的A549细胞，通过克隆形成、划痕愈合实验、Transwell实验分别检测DUSP26过表达对细胞增殖、迁移及侵袭能力的影响，WB法检测各组细胞中TGF-β1/SMAD2/3通路、EMT相关蛋白的表达水平，细胞免疫荧光法检测细胞中Ki-67、cyclin D1表达水平。加入TGF-β1重组蛋白进行回复实验。构建A549细胞裸鼠荷瘤模型，观察DUSP26过表达对移植瘤体内生长的影响，WB法检测移植瘤组织中TGF-β1/SMAD2/3通路、EMT相关蛋白表达水平，免疫荧光染色法检测移植瘤组织中Ki-67、cyclin D1表达水平。结果：DUSP26在LUAD组织和细胞中均呈低表达（P < 0.05或P < 0.01或P < 0.001或P < 0.000 1）。与DUSP26-OENC组相比，DUSP26-OE组A549细胞的增殖、迁移和侵袭能力均显著降低（P < 0.01或P < 0.001），TGF-β1、p-SMAD2/3、vimentin、N-cadherin、snail、Ki-67、cyclin D1表达均降低（P < 0.01或P < 0.001或P < 0.000 1），E-cadherin表达升高（P < 0.000 1）。加入5 ng/mL TGF-β1重组蛋白后，可部分逆转在体外实验中由DUSP26过表达导致的结果。成功构建裸鼠A549细胞荷瘤模型，DUSP26-OE组裸鼠移植瘤生长速度缓慢，体积和质量均减小（均P < 0.001），移植瘤组织中TGF-β1、p-SMAD2/3、vimentin、N-cadherin、snail、Ki-67、cyclin D1表达均降低（P < 0.01或P < 0.001），E-cadherin表达升高（P < 0.000 1）。结论：DUSP26在LUAD组织和细胞中均呈低表达状态，上调DUSP26的表达水平能够通过抑制TGF-β1/SMAD2/3信号通路抑制A549细胞的增殖、迁移和侵袭。