双能量CT定量参数联合CT征象预测中晚期肺腺癌表皮生长因子受体基因突变  

Dual-energy CT quantitative parameters combined with CT signs in predicting epidermal growth factor receptor gene mutation in advanced lung adenocarcinoma

作　　者：于蕾 陈望 孙乾 焦志云[3] YU Lei;CHEN Wang;SUN Qian;JIAO Zhiyun(Department of Medical Imaging,Yancheng No.1 People's Hospital,Affiliated Hospital of Medical School,Nanjing University,Yancheng 224000,China;Department of Respiratory,Yancheng No.1 People's Hospital,Affiliated Hospital of Medical School,Nanjing University,Yancheng 224000,China;Department of Medical Imaging,Affiliated Hospital of Yangzhou University,Yangzhou 225000,China)

机构地区：[1]南京大学医学院附属盐城第一医院(盐城市第一人民医院)CT室,江苏盐城224000 [2]南京大学医学院附属盐城第一医院(盐城市第一人民医院)呼吸科,江苏盐城224000 [3]扬州大学附属医院影像科,江苏扬州225100

出　　处：《分子影像学杂志》2024年第8期811-819,共9页Journal of Molecular Imaging

基　　金：江苏省扬州市科技计划项目(YZ2022107);红十字基金会医学赋能公益专项基金领航菁英临床科研项目(XM_LHJY2022_05_33)。

摘　　要：目的探究中晚期肺腺癌患者的双能量CT定量参数联合CT征象、临床特征与表皮生长因子受体(EGFR)基因突变的相关性,预测中晚期肺腺癌EGFR基因的突变情况。方法回顾性收集盐城市第一人民医院2022年1月~2023年6月经病理学(纤维支气管镜、淋巴结、经皮肺穿刺)活检确诊的172例中晚期肺腺癌(临床分期Ⅲ~Ⅳ期)。采集患者的一般临床特征、CT征象及双能量CT(DECT)参数。根据EGFR基因检测结果分为阳性组和阴性组,采用独立样本t检验或秩和检验分析比较组间的差异,对有统计学意义的参数,逐步建立基于临床特征、常规CT征象、DECT定量参数及联合的二元Logistic回归模型,评价联合模型预测效能。结果172例肺腺癌患者EGFR基因表达阳性者80例,阴性者92例。动脉期IC、NIC、斜率K_(40-100)keV及静脉期IC在两组之间的差异有统计学意义(P<0.001);空气支气管征及胸膜牵拉征在两组之间的差异有统计学意义(P<0.05);单因素Logistic回归显示动脉期IC、NIC、斜率K_(40-100)keV、静脉期IC、空气支气管征、胸膜牵拉征与EGFR基因突变相关;DECT联合参数模型Model1、DECT模型联合临床特征模型Model2、DECT模型联合临床特征及CT征象模型Model3的ROC曲线下面积分别为0.746(敏感度63.75%,特异度92.39%)、0.787(敏感度65.00%,特异度91.30%)、0.819(敏感度77.50%,特异度82.61%)。经DeLong检验,3个模型曲线下面积的差异无统计学意义(P>0.05)。结论联合临床特征、CT征象的DECT模型能有效预测中晚期肺腺癌患者EGFR突变,且优于单一模型。Objective To explore the correlation between the quantitative parameters of dual-energy CT combined with CT signs,clinical characteristics and epidermal growth factor receptor(EGFR)gene mutations in patients with advanced lung adenocarcinoma,and to predict the mutation in patients with advanced lung adenocarcinoma.Methods A retrospective collection was performed for 172 cases of advanced lung adenocarcinoma(clinical stage III~IV)diagnosed by pathology(fiber bronchoscopy,lymph node,percutaneous lung puncture)biopsy in the First People's Hospital of Yancheng City from January 2022 to June 2023.The patient's general clinical features,CT signs,and dual-energy CT(DECT)parameters were collected.According to the results of EGFR gene testing,they were divided into positive group and negative group.The independent samples t-test or rank-sum test were used to analyze the differences between the groups,and a binary logistic regression model based on clinical characteristics,conventional CT signs,DECT quantitative parameters and combination was gradually established for statistically significant parameters,and the prediction performance of the combined model was evaluated.Results A total of 172 patients with lung adenocarcinoma during the study period were identified,including 80 positive for EGFR gene expression patients and 92 negative patients.There were significant differences in IC,NIC,slope K40-100kev in arterial phase and IC in venous phase between the two groups(P<0.001);There were significant differences in air bronchogram sign and pleural traction sign between the two EGFR groups(P<0.05);Univariate logistic regression analysis showed that arterial phase IC,NIC,slope K40-100 keV,venous phase IC,air bronchogram sign,and pleural traction sign were associated with EGFR gene mutations.The AUC for the DECT model,the DECT model combined with clinical characteristics,and the DECT model combined with clinical characteristics and CT signs were 0.746(sensitivity 63.75%,specificity 92.39%),0.787(sensitivity 65.00%,specificity

关 键 词：表皮因子生长受体 双能量CT 肺腺癌 基因突变 预测模型 

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