摘要
目的:探讨基于多序列MRI影像组学在预测肺腺癌EGFR基因表型中的应用价值。方法:回顾性分析2015年1月-2018年12月行肺部MRI检查及EGFR基因检测的74例肺腺癌患者的临床、病理和影像资料。对肿瘤标本进行基因检测,证实EGFR突变型32例,野生型42例。MRI序列包括T_(2)WI、DWI及ADC图。临床资料包括性别、年龄、吸烟史、CEA、Ki-67、位置、最大直径和病理分级。分别在T_(2)WI、DWI和ADC图上于肿瘤最大截面手动勾画感兴趣区,共提取1404个影像组学特征。然后,利用Student-t检验和基于非线性支持向量机的递归特征消除(SVM-RFE)策略进行特征优选后建立预测模型,并应用受试者工作特征曲线(ROC)评估模型的预测效能。结果:最终选取16个最优纹理特征构建EGFR表型预测模型,其预测EGFR突变型的敏感度为53.1%,特异度为92.9%,符合率为75.7%,曲线下面积(AUC)为0.826。在此基础上进一步联合性别因素构建模型,预测符合率提高到78.9%。结论:基于多序列MRI影像组学方法可在一定程度上预测肺腺癌的EGFR基因表型,为术前肺腺癌患者的个体化风险分层提供参考。
Objective:To explore the feasibility of prediction of epithelial growth factor receptor(EGFR)gene mutation for lung adenocarcinoma based on multi-sequence MRI radiomics.Methods:A retrospective study was conducted on 74 cases with pulmonary adenocarcinoma(EGFR-mutant 32 cases and wild-type 42 cases diagnosed by a genetic test for the tumor)confirmed by postoperative pathology.All patients underwent 1.5T chest MRI examination before surgery.The clinical index including gender,age,tobacco smoking history,lesion location,maximum diameter,CEA and Ki-67 level,and histopathological grade were recorded.On the T_(2)WI,DWI and ADC images of each patient,ROIs was drawn in the largest tumor region respectively,and totally 1404 radiographic features were extracted.Then,the Student t-test and support vector machine recursive feature elimination(SVM-RFE)method were used to select out the optimal characteristics and establish the prediction model.ROC was used to analyze the performance of the predictive models.Results:In the performance evaluation of EGFR mutation in lung adenocarcinoma,16 optimal characteristics were selected out for establishing the prediction model,and its sensitivity,specificity,accuracy and AUC for predicting EGFR-mutant were 53.1%,92.9%,75.7% and 0.826,respectively.When the prediction model was further added with gender factors,and the accuracy reached 78.9%.Conclusion:Radiomics based on multi-sequence MRI can predict EGFR gene phenotypes of lung adenocarcinoma with relative high accuracy,thus can be helpful for preoperative individualized risk stratification of lung adenocarcinoma patients.
作者
唐兴
白国艳
王虹
印弘
张艰
徐肖攀
康晓伟
TANG Xing;BAI Guo-yan;WANG Hong(Department of Radiology,Xijing Hospital,the Air Force Medical University,Xi′an 710032,China)
出处
《放射学实践》
CSCD
北大核心
2021年第8期1010-1015,共6页
Radiologic Practice
基金
国家自然科学基金青年项目(81901698)
陕西省重点研发计划项目(2017ZDXM-SF-044)
西京医院助推计划(XJZT5ZL04)
西安市人民医院(西安市第四医院)科研孵化基金(CX-17)。
关键词
肺肿瘤
腺癌
影像组学
磁共振成像
扩散加权成像
表观扩散系数
血管内皮细胞生长因子
Lung neoplasms
Adenocacimoma
Radiomics
Magnetic resonance imaging
Diffusion weighted imaging
Apparent diffusion coefficient
Epithelial growth factor receptor
