CT联合肿瘤标志物预测肺结节低分化腺癌的模型构建  

Construction of a predictive model for poorly differentiated adenocarcinoma in pulmonary nodules using CT combined with tumor markers

作　　者：蒋捷 刘锋 王波 王芹 钟健 JIANG Jie;LIU Feng;WANG Bo;WANG Qin;ZHONG Jian(Department of Thoracic Surgery,The Affiliated Brain Hospital of Nanjing Medical University,Nanjing,210029,P.R.China)

机构地区：[1]南京医科大学附属脑科医院胸外科,南京210029

出　　处：《中国胸心血管外科临床杂志》2025年第1期73-79,共7页Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

基　　金：江苏省南京市卫生科技发展专项资金项目(YKK23148)。

摘　　要：目的根据CT影像及肿瘤标志物结果,建立低分化腺癌预测模型,并进行内部验证。方法选择2023年在南京医科大学附属脑科医院胸外科行手术治疗的实性与部分实性肺结节患者,按照7∶3比例将患者随机分为训练集和验证集。收集患者CT特征,包括结节平均密度值、最大直径、胸膜牵拉征和支气管充气征,以及肿瘤标志物结果。依据术后病理结果,将患者分为低分化腺癌组和非低分化腺癌组。对训练集采用单因素分析和logistic回归分析,并以此建立预测模型,采用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型区分度,校准曲线评估模型的一致性,绘制决策曲线评估该模型的临床价值,并在验证集中校验模型。结果共纳入299例患者,其中男103例、女196例,中位年龄57.00(51.00,67.25)岁;训练集211例,验证集88例。多因素分析显示癌胚抗原(CEA)值[OR=1.476,95%CI(1.184,1.983),P=0.002]、细胞角蛋白19片段抗原(CYFRA21-1)值[OR=1.388,95%CI(1.084,1.993),P=0.035]、肿瘤最大直径[OR=6.233,95%CI(1.069,15.415),P=0.017]、肿瘤平均密度[OR=1.083,95%CI(1.020,1.194),P=0.040]是实性与部分实性肺结节低分化腺癌的独立危险因素。基于此构建预测模型,ROC曲线下面积为0.896[95%CI(0.810,0.982)],最大约登指数对应截点值为0.103,灵敏度为0.750,特异度为0.936。Bootstrap法抽样1000次,校准曲线图预测概率与实际风险一致。决策曲线分析表明,在全预测概率下均有正收益,有较好临床价值。结论对于实性与部分实性肺结节患者,术前使用CT测量肿瘤平均密度值、最大直径,同时联合肿瘤标志物CEA、CYFRA21-1值可有效预测结节是否为低分化腺癌,以尽早予以干预。Objective To establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results.Methods Patients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery,the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3.Patients'CT features,including average density value,maximum diameter,pleural indentation sign,and bronchial inflation sign,as well as patient tumor marker results,were collected.Based on postoperative pathological results,patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group.Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model.The receiver operating characteristic(ROC)curve was used to evaluate the model's discriminability,the calibration curve to assess the model's consistency,and the decision curve to evaluate the clinical value of the model,which was then validated in the validation set.Results A total of 299 patients were included,with 103 males and 196 females,with a median age of 57.00(51.00,67.25)years.There were 211 patients in the training set and 88 patients in the validation set.Multivariate analysis showed that carcinoembryonic antigen(CEA)value[OR=1.476,95%CI(1.184,1.983),P=0.002],cytokeratin 19 fragment antigen(CYFRA21-1)value[OR=1.388,95%CI(1.084,1.993),P=0.035],maximum tumor diameter[OR=6.233,95%CI(1.069,15.415),P=0.017],and average density[OR=1.083,95%CI(1.020,1.194),P=0.040]were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma.Based on this,a predictive model was constructed with an area under the ROC curve of 0.896[95%CI(0.810,0.982)],a maximum Youden index corresponding cut-off value of 0.103,sensitivity of 0.750,and specificity of 0.936.Using the Bootstrap m

关 键 词：计算机断层显像 肿瘤标志物 肺结节 低分化腺癌 预测模型 列线图 

分 类 号：R734.2[医药卫生—肿瘤] R730.44[医药卫生—临床医学]