摘要
目的建立可预测Ⅱ~Ⅲ期胃癌患者淋巴结转移的神经网络模型, 并探讨其预测价值。方法病例纳入标准:(1)经病理确诊为Ⅱ~Ⅲ期(第8版AJCC分期)胃腺癌;(2)术前胸片、腹部超声及上腹部CT等检查无肝、肺、腹腔等远处转移;(3)行R0切除。病例排除标准:(1)术前接受过新辅助化疗或放疗;(2)一般临床资料不完整;(3)残胃癌。回顾性收集2010年1月至2014年8月期间在福建医科大学附属协和医院胃外科接受根治性切除术的1 231例Ⅱ~Ⅲ期胃癌患者的临床病理资料。全组共1 035例患者经术后证实淋巴结转移, 196例患者未出现淋巴结转移。416例(33.8%)术后病理分期为Ⅱ期, 815例(66.2%)为Ⅲ期。全组患者被随机分为建模组861例(69.9%)和验证组370例(30.1%)。先运用Logistic单因素分析方法, 对建模组的病例样本进行回顾性分析, 筛查影响淋巴结转移的变量, 确定人工神经网络输入节点的变量项目, 再使用多层感知器(MLP)训练N+-ANN。N+-ANN由Logistic单因素分析筛选出的变量构成输入层。人工智能依据输入数据分析患者淋巴结转移状态, 并与真实值进行比较。通过绘制受试者操作特性(ROC)曲线、获取曲线下面积(AUC)来评估模型的准确性。结果建模组与验证组临床资料的比较, 差异均无统计学意义(均P>0.05)。建模组单因素分析结果显示, 术前血小板淋巴细胞比值(PLR)、术前系统性免疫性炎性指数(SII)、肿瘤大小、临床N(cN)分期与患者出现淋巴结转移有关。将以上因素连同术前中性粒细胞淋巴细胞比值(NLR)、术前糖类抗原19-9、术前癌胚抗原、肿瘤位置、临床T(cT)分期作为输入层变量构建N+-ANN。建模组N+-ANN对术后淋巴转移预测准确率为88.4%(761/861), 灵敏度为98.9%(717/725), 特异度为32.4%(44/136), 阳性预测值为88.6%(717/809), 阴性预测值为84.6%(44/52), AUC值为0.748(95%CI:0.717~0.776);而验证组, N+-ANN的预测准确率为88.
Objective To establish a neural network model for predicting lymph node metastasis in patients with stage II-III gastric cancer.Methods Case inclusion criteria:(1)gastric adenocarcinoma diagnosed by pathology as stage II-III(the 8th edition of AJCC staging);(2)no distant metastasis of liver,lung and abdominal cavity in preoperative chest film,abdominal ultrasound and upper abdominal CT;(3)undergoing R0 resection.Case exclusion criteria:(1)receiving preoperative neoadjuvant chemotherapy or radiotherapy;(2)incomplete clinical data;(3)gastric stump cancer.Clinicopathological data of 1231 patients with stage II-III gastric cancer who underwent radical surgery at the Fujian Medical University Union Hospital from January 2010 to August 2014 were retrospectively analyzed.A total of 1035 patients with lymph node metastasis were confirmed after operation,and 196 patients had no lymph node metastasis.According to the postoperative pathologic staging.416 patients(33.8%)were stageⅡand 815 patients(66.2%)were stage III.Patients were randomly divided into training group(861/1231,69.9%)and validation group(370/1231,30.1%)to establish an artificial neural network model(N+-ANN)for the prediction of lymph node metastasis.Firstly,the Logistic univariate analysis method was used to retrospectively analyze the case samples of the training group,screen the variables affecting lymph node metastasis,determine the variable items of the input point of the artificial neural network,and then the multi-layer perceptron(MLP)to train N+-ANN.The input layer of N+-ANN was composed of the variables screened by Logistic univariate analysis.Artificial intelligence analyzed the status of lymph node metastasis according to the input data and compared it with the real value.The accuracy of the model was evaluated by drawing the receiver operating characteristic(ROC)curve and obtaining the area under the curve(AUC).The ability of N+-ANN was evaluated by sensitivity,specificity,positive predictive values,negative predictive values,and AUC values.Resul
作者
薛震
陆俊
林嘉
黄昌明
李平
谢建伟
王家镔
林建贤
陈起跃
郑朝辉
Xue Zhen;Lu Jun;Lin Jia;Huang Changming;Li Ping;Xie Jianwei;Wang Jiabin;Lin Jianxian;Chen Qiyue;Zheng Chaohui(Department of Gastric Surgery,Key Laboratory of Gastrointestinal Cancer(Ministry of Education),Fujian Medical University Union Hospital,Fuzhou 350004,China)
出处
《中华胃肠外科杂志》
CSCD
北大核心
2022年第4期327-335,共9页
Chinese Journal of Gastrointestinal Surgery
关键词
胃肿瘤
淋巴结转移
人工神经网络
预测模型
Gastric cancer
Lymphatic metastasis
Artificial neural network
Predicting model
