基于^(18)F-FDG PET-CT的瘤内及瘤周影像组学预测肺腺癌表皮生长因子受体突变状态  

Intratumoral and peritumoral radiomics based on^(18)F-FDG PET-CT for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma

作　　者：高建雄 葛欣宇 牛荣[1] 史云梅 蒋振兴[2] 孙艳 冯金宝 王跃涛[1] 邵小南[1] Gao Jianxiong;Ge Xinyu;Niu Rong;Shi Yunmei;Jiang Zhenxing;Sun Yan;Feng Jinbao;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;Department of Radiology,the Third Affiliated Hospital of Soochow University,the First People′s Hospital of Changzhou,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 [2]苏州大学附属第三医院,常州市第一人民医院放射科,常州213003

出　　处：《中华放射学杂志》2024年第10期1042-1049,共8页Chinese Journal of Radiology

基　　金：常州高技术研究重点实验室(CM20193010);常州市科技计划项目(CJ20220228、CJ20210063);常州市“十四五”卫生健康高层次人才培养工程-拔尖人才(2022-260);常州市临床医学中心(核医学)(CZZX202204)。

摘　　要：目的探讨基于^(18)F-FDG PET-CT的瘤内、瘤周影像组学模型预测肺腺癌表皮生长因子受体(EGFR)突变状态的价值及瘤周影像组学特征的可解释性。方法该研究为横断面研究。回顾性收集2018年1月至2022年4月在苏州大学附属第三医院接受^(18)F-FDG PET-CT检查的肺腺癌患者,按6∶4的比例随以分层随机抽样分为训练集(309例)和测试集(206例)。分别基于PET及CT图像的瘤内及瘤周感兴趣区域提取影像组学特征,并筛选出最佳特征集。使用XGBoost算法建立影像组学模型,并计算影像组学标签(CT瘤内标签、CT瘤周标签、PET瘤内标签、PET瘤周标签)。采用logistic回归分析构建临床模型及联合模型(结合PET-CT瘤内、瘤周影像组学与临床特征及CT语义特征构建)。采用受试者操作特征曲线及曲线下面积(AUC)评估模型预测效能。使用无监督聚类、Spearman相关性分析及可视化方法增加瘤周影像组学特征的可解释性。结果在训练集和测试集中,CT瘤周标签预测肺腺癌EGFR突变型的AUC值大于CT瘤内标签(训练集:Z=3.84,P<0.001;测试集:Z=1.99,P=0.046);测试集中,PET瘤内标签预测肺腺癌EGFR突变型的AUC值(0.684)略大于PET瘤周标签的AUC值(0.672),但差异无统计学意义(P>0.05)。联合模型在训练集及测试集中预测肺腺癌EGFR突变型的AUC值均最大,且显著优于临床模型(训练集:Z=6.52,P<0.001;测试集:Z=2.31,P=0.021)。通过可解释性分析发现,CT瘤周影像组学特征与CT形状特征最具相关性,不同EGFR突变状态的CT瘤周特征差异有统计学意义。结论CT瘤周标签预测肺腺癌EGFR突变型的价值优于CT瘤内标签,作为一种可解释的方法,PET-CT瘤内、瘤周影像组学与临床特征及CT语义特征联合可提升预测效能。Objective To investigate the value of intratumoral and peritumoral radiomics models based on^(18)F-FDG PET-CT in predicting epidermal growth factor receptor(EGFR)mutation status in lung adenocarcinoma and interpret peritumoral radiomics features.Methods This study was a cross-sectional study.Patients with lung adenocarcinoma who underwent^(18)F-FDG PET-CT at the Third Affiliated Hospital of Soochow University between January 2018 and April 2022 were retrospectively collected and samplied into a training set(309 cases)and a test set(206 cases)in a 6∶4 ratio randomly.Radiomics features were extracted from the intratumoral and peritumoral regions of interest based on PET and CT images,respectively,and the optimal feature sets were selected.Radiomics models were established using the XGBoost algorithm,and radiomics scores(intratumoral CT label,peritumoral CT label,intratumoral PET label,peritumoral PET label)were calculated.Logistic regression analysis was used to construct a clinical model and a combined model(incorporating PET-CT intratumoral and peritumoral radiomics,clinical features,and CT semantic features).The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve(AUC).Unsupervised clustering,Spearman correlation analysis,and visualization methods were used for the interpretability of peritumoral radiomics features.Results In both the training and test sets,the AUC value of CT peritumoral labels was greater than that of CT intratumoral labels for predicting EGFR mutation status in lung adenocarcinoma(training set:Z=3.84,P<0.001;test set:Z=1.99,P=0.046).In the test set,the AUC value of PET intratumoral labels(0.684)was slightly higher than that of PET peritumoral labels(0.672)for predicting EGFR mutation status,but the difference was not statistically significant(P>0.05).The combined model had the highest AUC value for predicting EGFR mutation status of lung adenocarcinoma in both the training and test sets and was significantly better th

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 影像组学 表皮生长因子受体 

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