临床联合CT影像学特征预测非小细胞肺癌EGFR基因突变的价值  

Prediction of EGFR gene mutations in non-small cell lung cancer using clinic combined with CT imaging features

作　　者：左利华 常江丽 赵琪瑞 朱勇华[2] 吉红波 韩丹[1] ZUO Lihua;CHANG Jiangli;ZHAO Qirui;ZHU Yonghua;JI Hongbo;HAN Dan(Department of Medical Imaging,The First Affiliated Hospital of Kunming Medical University,Kunming 650032,China;Medical Imaging Center,The First People's Hospital of Qujing City,Qujing 655000,China;Department of Thoracic Surgery,The First People's Hospital of Qujing City,Qujing 655000,China)

机构地区：[1]昆明医科大学第一附属医院医学影像科,云南昆明650032 [2]云南省曲靖市第一人民医院医学影像中心,云南曲靖655000 [3]云南省曲靖市第一人民医院胸外科,云南曲靖655000

出　　处：《医学影像学杂志》2025年第2期33-38,共6页Journal of Medical Imaging

基　　金：国家自然科学基金项目(编号:82360344)。

摘　　要：目的探讨临床、CT影像学特征对非小细胞肺癌(NSCLC)表皮生长因子受体(EGFR)基因突变的预测作用。方法选取病理确诊为NSCLC且具有临床资料、基因检测结果、CT图像资料的患者412例(分为训练集292例,验证集120例)。单因素分析训练集突变阳性组及阴性组的临床及CT影像特征之间差别,差异有统计学意义的特征纳入多因素分析,筛选预测EGFR突变独立预测因子,建立训练集及验证集Logistic回归模型,绘制诺莫图使模型可视化,利用曲线下面积(AUC)判断模型在预测EGFR基因突变中的效能,校准曲线及决策曲线评价模型的实用性。结果1)EGFR突变阳性组及阴性组间性别、吸烟史、病理类型、病灶类型(混合磨玻璃病灶)、晕征、液化坏死、空气支气管征、血管集束征、胸膜凹陷征比较,差异均有统计学意义(P<0.05);2)多因素分析显示无吸烟史、晕征、无液化坏死、空气支气管征、血管集束征是EGFR突变的独立预测因子;3)训练集ROC曲线预测EGFR基因突变AUC为0.746,敏感度和特异度、准确率分别为71.2%、66.2%、64.2%,验证集AUC为0.708,敏感度及特异度、准确率分别为74.6%、62.3%、65.0%。校准曲线显示训练集及验证集预测模型与观察结果具有良好一致性。结论预测NSCLC患者EGFR突变的临床-CT影像学模型具有一定价值,有望作为一种无创预测NSCLC患者EGFR突变的方法。Objective To explore the predictive role of clinical and CT imaging features in epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A retrospective collection was performed on 412 patients with pathologically confirmed NSCLC who had clinical data,genetic testing results,and CT imaging data were conducted,including 292 cases in the training set and 120 cases in the validation set.Univariate analysis was performed to assess differences in clinical and imaging features between the mutation-positive and mutation-negative groups in the training set.Features with statistical significance in univariate analysis were included in multivariate analysis to identify independent predictive factors for EGFR mutations.A logistic regression model was established,and a nomogram was created to visualize the model.The area under the curve(AUC)was used to evaluate the model's effectiveness in predicting EGFR gene mutations,while calibration curves and decision curves were used to assess the model's practicality.Results 1)Significant differences were found between the EGFR mutation-positive and negative groups regarding gender,smoking history,pathological type,lesion type,halo sign,liquefactive necrosis,air bronchogram,vascular bundle sign,and pleural indentation sign(P<0.05);2)Multivariate analysis identified no smoking history,halo sign,liquefactive necrosis,air bronchogram,vascular bundle sign as independent predictive factors for EGFR mutations;and 3)The AUC value for predicting EGFR gene mutations in the training set ROC curve was 0.746,with sensitivity,specificity,and accuracy of 71.2%,66.2%,and 64.2%,respectively;the AUC value for the validation set was 0.708,with sensitivity,specificity,and accuracy of 74.6%,62.3%,and 65.0%.Calibration curves indicated good consistency between the predicted model and observed results in both the training and validation sets.Conclusion The clinical-CT imaging model for predicting EGFR mutations in NSCLC patients holds certain value,which can serve

关 键 词：非小细胞肺癌 表皮生长因子受体 预测 体层摄影术 X线计算机 

分 类 号：R734.2[医药卫生—肿瘤] R814.42[医药卫生—临床医学]