肿瘤标志物联合影像学预测肺腺癌EGFR基因突变模型的建立与验证  

Establishment and Validation of EGFR Gene Mutation Model Predicted by Tumor Markers Combined with Imaging in Lung Adenocarcinoma

作　　者：蔡亚男 倪明立 CAI Yanan;NI Mingli(Department of Oncology,Luoyang Central Hospital Affiliated to Zhengzhou University,Luoyang 471099,China)

机构地区：[1]郑州大学附属洛阳中心医院肿瘤科,河南洛阳471099

出　　处：《医学综述》2025年第8期999-1005,共7页Medical Recapitulate

基　　金：河南省医学科技攻关计划联合共建项目(LHGJ20220954)。

摘　　要：目的利用血清肿瘤标志物联合影像学构建肺腺癌患者表皮生长因子受体(EGFR)基因突变状态的风险分层预测模型并进行验证。方法回顾性收集2021年1月至2023年12月郑州大学附属洛阳中心医院经病理确诊的300例肺腺癌患者治疗前的血清肿瘤标志物、胸部CT影像学特征及病理穿刺EGFR基因检测结果,并基于组织学EGFR基因检测结果将研究对象分为野生型组(166例)和突变型组(134例)。采用多元Logistic回归分析筛选影响肺腺癌患者EGFR突变的独立因素,构建相应列线图风险预测模型,并对模型进行严格验证及实施风险分层。结果突变型组和野生型组患者性别、吸烟史、癌胚抗原(CEA)、细胞角蛋白19片段抗原21-1、糖类抗原199及分叶征、毛刺征、胸腔积液、磨玻璃结节、血管集束征占比比较差异有统计学意义(P<0.05或P<0.01)。多因素二元Logistic回归分析结果显示,CEA≥5μg/L、分叶征、血管集束征、磨玻璃结节、胸腔积液是肺腺癌患者发生EGFR突变的独立危险因素(OR=5.206,95%CI 2.504~10.823;OR=2.352,95%CI 1.218~4.541;OR=12.155,95%CI 5.840~25.298;OR=3.129,95%CI 1.634~5.992;OR=2.138,95%CI 1.122~4.075)(P<0.05或P<0.01)。基于上述5个指标构建了EGFR突变发生风险的列线图模型,所得列线图总分数能更有效地预测肺腺癌患者发生EGFR突变的风险(曲线下面积为0.873,95%CI 0.846~0.924)。该模型的最优截断值0.551(列线图得分145分),据此将肺腺癌患者分为低风险和高风险亚组。通过Bootstrap验证法评估,该模型展现出良好的临床性能。结论血清肿瘤标志物联合CT影像学特征能进一步提高肺腺癌EGFR突变的预测价值并对其进行风险分层。Objective To establish a stratified risk prediction model for epidermal growth factor receptor(EGFR)mutation status in patients with lung adenocarcinoma using serum tumor markers combined with imaging indicators,and validate it.Methods Serum tumor markers,chest CT imaging features and pathological EGFR gene detection results of 300 patients with pathologically diagnosed lung adenocarcinoma before treatment in Luoyang Central Hospital Affiliated to Zhengzhou University from Jan.2021 to Dec.2023 were retrospectively collected,and the subjects were divided into a wild-type group(n=166)and a mutant group(n=134)based on the results of histological EGFR gene detection.Multivariate Logistic regression analysis was used to screen out the independent factors affecting EGFR mutation in lung adenocarcinoma patients,and the corresponding nomogram risk prediction model was constructed.In addition,the model was rigorously validated and risk stratification was implemented.Results There were significant differences in gender,smoking history,carcinoembryonic antigen(CEA),cytokeratin 19 fragment antigen 21-1,carbohydrate antigen 199,and the proportions of lobulation sign,burr sign,pleural effusion,ground glass nodule and vascular cluster sign between the mutant group and the wild-type group(P<0.05 or P<0.01).Multivariate binary Logistic regression analysis showed that CEA≥5μg/L,lobulation sign,vascular cluster sign,ground glass nodule and pleural effusion were independent risk factors for EGFR mutation in lung adenocarcinoma patients(OR=5.206,95%CI 2.504-10.823;OR=2.352,95%CI 1.218-4.541;OR=12.155,95%CI 5.840-25.298;OR=3.129,95%CI 1.634-5.992;OR=2.138,95%CI 1.122-4.075)(P<0.05 or P<0.01).Based on the above five indicators,a nomogram model of EGFR mutation risk was constructed,and the total score of the nomogram obtained could more effectively predict the risk of EGFR mutation in lung adenocarcinoma patients(area under curve 0.885,95%CI 0.846-0.924).The optimal cut-off value of 0.551(histogram score 145)of the model was used to

关 键 词：肺腺癌 肿瘤标志物 影像学 表皮生长因子受体 基因突变 

分 类 号：R734.2[医药卫生—肿瘤]