^(18)F-FDG PET/CT影像组学预测非小细胞肺癌患者表皮生长因子受体基因突变亚型  

Prediction of epidermal growth factor receptor mutation subtypes in patients with non-small cell lung cancer by ^(18)F-FDG PET/CT radiomics

作　　者：张建媛 赵新明[1] 赵妍[3] 张敬勉 张召奇[1] Zhang Jianyuan;Zhao Xinming;Zhao Yan;Zhang Jingmian;Zhang Zhaoqi(Department of Nuclear Medicine,the Fourth Hospital of Hebei Medical University,Shijiazhuang 050011,China;Department of Nuclear Medicine,Baoding No.1 Central Hospital,Baoding 071000,China;Department of Medical Oncology,the Fourth Hospital of Hebei Medical University,Shijiazhuang 050011,China)

机构地区：[1]河北医科大学第四医院核医学科,石家庄050011 [2]保定市第一中心医院核医学科,保定071000 [3]河北医科大学第四医院肿瘤内科,石家庄050011

出　　处：《中华核医学与分子影像杂志》2023年第8期480-485,共6页Chinese Journal of Nuclear Medicine and Molecular Imaging

基　　金：河北省医学适用技术跟踪项目(GL2011-52)。

摘　　要：目的探讨基于治疗前^(18)F-FDG PET/CT的影像组学模型在识别非小细胞肺癌(NSCLC)患者表皮生长因子受体(EGFR)突变亚型中的价值。方法回顾性纳入2015年1月至2019年11月间于河北医科大学第四医院诊治的172例EGFR突变型NSCLC患者[男54例、女118例;年龄(56.2±12.5)岁],其中外显子19缺失突变75例,外显子21 L858R错义突变97例。采用随机数字表法,按7∶3比例将患者分为训练集(121例)与验证集(51例)。使用LIFEx 4.00软件提取病灶的PET/CT影像组学特征,采用最小绝对收缩和选择算子(LASSO)算法进行特征筛选。构建3种机器学习模型:逻辑回归模型、随机森林模型、支持向量机模型,采用ROC曲线分析评估3种模型的预测效能,并采用决策曲线分析评估模型的临床价值。结果经LASSO算法最终筛选出9个组学特征,包括6个PET特征[直方图(HISTO)_峰度(Kurtosis)、形状特征(SHAPE)_球形度(Sphericity)、灰度游程长度矩阵(GLRLM)_低灰度运行重点(LGRE)、GLRLM_运行长度不均匀性(RLNU)、邻域灰度差异矩阵(NGLDM)_对比度(Contrast)、灰度区域长度矩阵(GLZLM)_低灰度短区重点(SZLGE)],3个CT特征[灰度共生矩阵(GLCM)_相关性(Correlation)、GLRLM_运行百分比(RP)、NGLDM_Contrast]。构建的3种机器学习模型在训练集与验证集中表现出相似的预测性能:随机森林模型AUC分别为0.79、0.77;支持向量机模型AUC分别为0.76、0.75;逻辑回归模型AUC分别为0.77、0.75。决策曲线分析显示,3种模型均具有较好的净获益与临床价值。结论基于^(18)F-FDG PET/CT的影像组学模型为识别NSCLC患者EGFR外显子19缺失与外显子21 L858R错义突变提供了非侵入性的方法,可辅助临床决策及制定个体化治疗方案。Objective To investigate the value of pre-therapy ^(18)F-FDG PET/CT radiomic models in differentiating epidermal growth factor receptor(EGFR)exon 19 deletion from exon 21 L858R missense in patients with non-small cell lung cancer(NSCLC).Methods A total of 172 patients with EGFR mutant NSCLC(54 males,118 females,age:(56.2±12.5)years)in the Fourth Hospital of Hebei Medical University between January 2015 and November 2019 were retrospectively included.Exon 19 mutation was found in 75 patients and exon 21 mutation was identified in 97 patients.The patients were divided into training set(n=121)and validation set(n=51)in a 7∶3 ratio by using random number table.The LIFEx 4.00 package was used to extract texture features of PET/CT images of lesions.The least absolute shrinkage and selection operator(LASSO)algorithm was used for feature screening.Three machine learning models,namely logistic regression(LR),random forest(RF),and support vector machine(SVM)models,were constructed based on the selected optimal feature subsets.The ROC curve analysis was performed to assess the predictive performance of those models.Finally,decision curve analysis(DCA)was used to evaluate the clinical value of the models.Results Nine radiomics features,including 6 PET features(histogram(HISTO)_Kurtosis,SHAPE_Sphericity,gray level run length matrix(GLRLM)_low gray-level run emphasis(LGRE),GLRLM_run length non-uniformity(RLNU),neighborhood grey level different matrix(NGLDM)_Contrast,gray level zone length matrix(GLZLM)_short-zone low gray-level emphasis(SZLGE)),and 3 CT features(gray level co-occurrence matrix(GLCM)_Correlation,GLRLM_run percentage(RP),NGLDM_Contrast),were screened by LASSO algorithm.Three machine learning models had similar predictive performance in the training and validation sets:AUCs for the RF model were 0.79,0.77,and those for the SVM model were 0.76,0.75,for the LR model were 0.77,0.75.The DCA showed that the 3 machine learning models had good net benefits and clinical values in predicting EGFR mutation subtypes.Conc

关 键 词：癌 非小细胞肺 突变 基因 erbB-1 正电子发射断层显像术 体层摄影术 X线计算机 氟脱氧葡萄糖F18 预测 影像组学 

分 类 号：R734.2[医药卫生—肿瘤]