基于CT影像组学特征联合临床特征预测非小细胞肺癌EGFR突变状态  

Prediction of EGFR mutation status in non-small cell lung cancer based on CT radiomic features combined with clinical characteristics

作　　者：杨涛涛 王显棋 陈灿灿 闫婉莹 王大为 熊坤林[3] 孙志远 陈伟[1] YANG Taotao;WANG Xianqi;CHEN Cancan;YAN Wanying;WANG Dawei;XIONG Kunlin;SUN Zhiyuan;CHEN Wei(Department of Radiology,First Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing;Infervision Medical Technology Co.,Ltd,Beijing;Department of Radiology,Army Medical Center of PLA/Daping Hospital of Third Military Medical University,Chongqing;Department of Diagnostic Radiology,Eastern Theatre General Hospital,Nanjing,Jiangsu,China)

机构地区：[1]陆军军医大学(第三军医大学)第一附属医院放射科,重庆 [2]推想医疗科技股份有限公司,北京 [3]陆军特色医学中心(第三军医大学大坪医院)放射科,重庆 [4]东部战区总医院放射诊断科,江苏南京

出　　处：《陆军军医大学学报》2025年第8期847-857,共11页Journal of Army Medical University

基　　金：重庆市科卫联合重点项目(2023ZDXM008)。

摘　　要：目的 探究基于胸部CT影像组学特征联合临床特征预测非小细胞肺癌(non-small cell lung cancer,NSCLC)表皮生长因子受体(epidermal growth factor receptor,EGFR)基因突变的预测价值。方法 采用病例对照研究的方法,收集2013年1月至2023年10月3家医疗中心放射科1 070名NSCLC患者的临床信息和CT图像。其中陆军军医大学第一附属医院719名NSCLC患者按照7∶3的比例随机分成训练集、内部验证集;东部战区总医院173名患者、陆军特色医学中心178名患者分别作为外部验证集1、外部验证集2。使用最小绝对收缩和选择算子回归筛选最佳影像组学特征,构建影像组学模型;以单因素及多因素Logistic回归筛选EGFR突变相关的临床特征,构建临床模型;联合影像组学特征及临床特征构建综合模型。3种分类模型均采用随机森林(random forest,RF)的机器学习方法进行建模。采用曲线下面积(AUC)、准确率、敏感度和特异度评价模型预测效能。绘制校正曲线以评估综合模型的拟合优度,决策曲线评估模型的临床应用价值。结果 影像组学模型在内部验证集、外部验证集1、外部验证集2中的AUC值分别为0.762 4(95%CI:0.692 4~0.825 1)、0.745 4(95%CI:0.671 1~0.814 3)和0.724 7(95%CI:0.639 7~0.801 6);临床预测模型在内部验证集、外部验证集1、外部验证集2的AUC值分别为0.6917(95%CI:0.6279~0.7576)、0.6525(95%CI:0.5767~0.7291)和0.7792(95%CI:0.712 5~0.847 3);基于临床特征和影像组学特征构建的综合模型预测效能值最佳,其在内部验证集、外部验证集1、外部验证集2的AUC值分别为0.818 0(95%CI:0.757 7~0.874 3)、0.782 4(95%CI:0.7031~0.848 2)和0.796 6(95%CI:0.718 1~0.868 6)。校准曲线提示综合模型拟合度较好,决策曲线提示综合模型具有较好的净收益。结论 结合胸部CT影像组学特征与临床特征构建的综合模型在预测NSCLC EGFR基因突变的多中心数据集中表现出更好的预测性能,有助Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.7624(95%CI:0.6924~0.8251),0.7454(95%CI:0.6711~0.8143),and 0.7247(95%CI:0.6397~0.8016),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.6917(95%CI:0.6279~0.7576),0.6525(95%CI:0.5767~0.7291),and 0.7792(95%CI:0.7125~0.8473),respectively in the above sets in turn;The compr

关 键 词：非小细胞肺癌 影像组学 临床特征 表皮生长因子受体 

分 类 号：R394.3[医药卫生—医学遗传学] R734.2[医药卫生—基础医学] R814.42