李健维, 杨昭, 王小雷, 张书海, 谢宗玉. CT影像组学联合临床特征在预测肺腺癌EGFR突变中的价值[J]. 蚌埠医学院学报, 2021, 46(8): 1103-1108. DOI: 10.13898/j.cnki.issn.1000-2200.2021.08.030
    引用本文: 李健维, 杨昭, 王小雷, 张书海, 谢宗玉. CT影像组学联合临床特征在预测肺腺癌EGFR突变中的价值[J]. 蚌埠医学院学报, 2021, 46(8): 1103-1108. DOI: 10.13898/j.cnki.issn.1000-2200.2021.08.030
    LI Jian-wei, YANG Zhao, WANG Xiao-lei, ZHANG Shu-hai, XIE Zong-yu. Value of the CT radiomics combined with clinical features in the prediction of EGFR mutation in lung adenocarcinoma[J]. Journal of Bengbu Medical College, 2021, 46(8): 1103-1108. DOI: 10.13898/j.cnki.issn.1000-2200.2021.08.030
    Citation: LI Jian-wei, YANG Zhao, WANG Xiao-lei, ZHANG Shu-hai, XIE Zong-yu. Value of the CT radiomics combined with clinical features in the prediction of EGFR mutation in lung adenocarcinoma[J]. Journal of Bengbu Medical College, 2021, 46(8): 1103-1108. DOI: 10.13898/j.cnki.issn.1000-2200.2021.08.030

    CT影像组学联合临床特征在预测肺腺癌EGFR突变中的价值

    Value of the CT radiomics combined with clinical features in the prediction of EGFR mutation in lung adenocarcinoma

    • 摘要:
      目的探究CT影像组学联合临床特征对肺腺癌EGFR突变状态的预测效能。
      方法对125例肺腺癌病人进行回顾性研究,分成训练组(n=74)与验证组(n=51),基于CT成像提取影像组学特征;采用支持向量机(SVM)分类器,分别构建临床模型、影像组学模型以及联合模型;受试者工作特征曲线(ROC)及曲线下面积(AUC)用于评价模型的预测效能。
      结果临床模型、影像组学模型以及联合模型在训练组中的AUC分别为0.749(0.653~0.843)、0.818(0.711~0.898)、0.860(0.760~0.930),在验证组中的AUC分别为0.753(0.612~0.863)、0.797(0.661~0.896)、0.855(0.728~0.938)。
      结论对于肺腺癌EGFR突变状态的预测,CT影像组学特征优于临床因素与CT征象,当影像组学结合临床因素与CT征象,能进一步提高预测效能。

       

      Abstract:
      ObjectiveTo explore the predictive efficacy of CT radiomics combined with clinical features in predicting EGFR mutation in lung adenocarcinoma.
      MethodsThe clinical data of 125 patients with lung adenocarcinoma were retrospectively analyzed, the patients were divided into the training group(n=74) and verification group(n=51).The radiomics features were extracted based on CT radiomics.The support vector machine(SVM) classifier was used to construct the clinical model, radiomics model and joint model, respectively.The receiver operating characteristic curve(ROC) and area under the curve(AUC) were used to evaluate the predictive efficacy of model.
      ResultsThe AUC of clinical model, radiomics model and joint model in training group were 0.749(0.653-0.843), 0.818(0.711-0.898) and 0.860(0.760-0.930), respectively.The AUC of clinical model, radiomics model and joint model in verification group were 0.753(0.612-0.863), 0.797(0.661-0.896) and 0.855(0.728-0.938), respectively.
      ConclusionsFor the prediction of EGFR mutation status in lung adenocarcinoma, the CT radiomics features are superior to clinical factors and CT signs.The radiomics combined with clinical factors and CT signs can further improve the prediction efficiency.

       

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