18F-氟脱氧葡萄糖PET-CT双模态生境成像预测肺腺癌表皮生长因子受体突变状态的研究  

The study of 18F-fluorodeoxyglucose PET-CT dual-modality habitat imaging in predicting epidermal growth factor receptor mutation status of lung adenocarcinoma

作　　者：牛荣[1] 冯金宝 高建雄 葛欣宇 孙艳 史云梅 王跃涛[1] 邵小南[1] Niu Rong;Feng Jinbao;Gao Jianxiong;Ge Xinyu;Sun Yan;Shi Yunmei;Wang Yuetao;Shao Xiaonan(Department of Nuclear Medicine,the Third Affiliated Hospital of Soochow University,the First People′s Hospital of Changzhou,Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging,Soochow University,Changzhou Key Laboratory of Molecular Imaging,Changzhou 213003,China)

机构地区：[1]苏州大学附属第三医院、常州市第一人民医院核医学科、苏州大学核医学与分子影像临床转化研究所、常州市分子影像重点实验室,常州213003

出　　处：《中华放射学杂志》2025年第4期409-417,共9页Chinese Journal of Radiology

基　　金：常州市“十四五”卫生健康高层次人才培养工程-拔尖人才(2022CZBJ037,2024CZBJ008);常州市科技计划项目(CJ20220228,CJ20210063);常州高技术研究重点实验室(CM20193010);常州市卫健委重大科技项目(ZD202109);常州市临床医学中心(核医学)(CZZX202204)。

摘　　要：目的探讨18F-氟脱氧葡萄糖(18F-FDG)PET-CT双模态生境成像技术预测肺腺癌表皮生长因子受体(EGFR)突变状态的价值。方法该研究为横断面研究。回顾性分析2018年1月至2022年4月在苏州大学附属第三医院接受18F-FDG PET-CT显像且EGFR检测结果明确的403例肺腺癌患者的临床及影像资料。采用分层随机抽样方法按7∶3分为训练集(282例)和验证集(121例)。采用自适应聚类算法对感兴趣区进行分区,形成不同的生境(Habitat)并获取衍生参数。以独立样本t检验或Mann-Whitney U检验比较EGFR突变型与野生型临床、影像指标及生境衍生参数。将单因素分析中差异有统计学意义的临床、影像指标及生境衍生参数纳入多因素logistic回归分别构建临床模型及临床-生境联合模型。采用受试者操作特征曲线和曲线下面积(AUC)评估各模型预测肺腺癌EGFR突变的效能;采用净重新分类指数(NRI)评估模型的分类改进能力。结果EGFR突变型249例,野生型154例。最优的生境数量为2个,分别为Habitat 1与Habitat 2。纳入临床模型的参数为吸烟史、支气管征、胸膜凹陷征和肿瘤长径;纳入临床-生境联合模型的参数为吸烟史、支气管征、胸膜凹陷征、Habitat 2、Habitat 1体素数。训练集中临床模型和临床-生境联合模型预测肺腺癌EGFR突变的AUC分别为0.723和0.733,差异无统计学意义(Z=0.60,P=0.549);在验证集中分别为0.684和0.715,差异无统计学意义(Z=1.32,P=0.186)。验证集中临床-生境联合模型预测肺腺癌EGFR突变的准确度(0.694)和特异度(0.609)较临床模型(分别为0.686、0.565)略高;NRI分析证实,在正确分类EGFR野生型肺腺癌方面临床-生境联合模型较临床模型提升了10.9%(P=0.018)。结论18F-FDG PET-CT双模态生境成像技术可用于分析肺腺癌瘤内微环境,在无创预测EGFR突变状态方面具有一定潜力,为实现肺腺癌患者的个性化精准治疗提供了重要依据。ObjectiveTo explore the value of 18F-fluorodeoxyglucose(18F-FDG)PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor(EGFR)mutation status in lung adenocarcinoma.MethodsThis study was designed as a cross-sectional study.Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set(282 cases)and a validation set(121 cases)using a stratified random sampling method at a 7∶3 ratio.An adaptive clustering algorithm was used to segment the regions of interest,forming different habitats and obtaining derived parameters.Independent samples t-test or Mann-Whitney U test were used to compare clinical,imaging indicators,and habitat-derived parameters between EGFR mutant and wild-type patient.The clinical,imaging indicators,and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models,respectively.The receiver operating characteristic curve and area under the curve(AUC)were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma.Additionally,the net reclassification index(NRI)was employed to assess the model′s classification improvement capability.ResultsThere were 249 cases of EGFR mutation and 154 cases of wild type.The optimal number of habitats was two,namely Habitat 1 and Habitat 2.The parameters included in the clinical model were smoking history,bronchial sign,pleural indentation sign,and tumor diameter.The parameters incorporated into the clinical-habitat combined model were smoking history,bronchial sign,pleural indentation sign,Habitat 2,and Habitat 1 voxel count.In the development set,the AUCs for predicting EGFR mutations in lung adenocarcinoma using the

关 键 词：肺肿瘤 正电子发射断层显像术 体层摄影术 X线计算机 生境成像 表皮生长因子受体 

分 类 号：R734.2[医药卫生—肿瘤]