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    唐聪聪, 陈艾琪, 曹胜男, 李伟, 李想, 杜小萌, 马宜传. CT影像组学在非小细胞肺癌病理分级中的应用[J]. 蚌埠医科大学学报, 2023, 48(6): 783-786. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.017
    引用本文: 唐聪聪, 陈艾琪, 曹胜男, 李伟, 李想, 杜小萌, 马宜传. CT影像组学在非小细胞肺癌病理分级中的应用[J]. 蚌埠医科大学学报, 2023, 48(6): 783-786. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.017
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    TANG Cong-cong, CHEN Ai-qi, CAO Sheng-nan, LI Wei, LI Xiang, DU Xiao-meng, MA Yi-chuan. Application of CT radiomics in the pathological grading of non-small cell lung cancer[J]. Journal of Bengbu Medical University, 2023, 48(6): 783-786. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.017
    Citation: TANG Cong-cong, CHEN Ai-qi, CAO Sheng-nan, LI Wei, LI Xiang, DU Xiao-meng, MA Yi-chuan. Application of CT radiomics in the pathological grading of non-small cell lung cancer[J]. Journal of Bengbu Medical University, 2023, 48(6): 783-786. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.017
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    CT影像组学在非小细胞肺癌病理分级中的应用

    Application of CT radiomics in the pathological grading of non-small cell lung cancer

      摘要
    • 摘要:
      目的探讨CT影像组学在非小细胞肺癌(NSCLC)病理分级中的应用价值。
      方法回顾性分析67例经病理证实为NSCLC病人资料，其中Ⅰ级7例，Ⅱ级39例，Ⅲ级21例，依据非小细胞肺癌的分化程度、异型性、核分裂象的多少，将Ⅰ、Ⅱ级归为低级别组(46例)，Ⅲ级归为高级别组(21例)。将病人按照5∶1的比例随机分为训练组56例，验证组11例，选取病灶最大径层面进行感兴趣区(ROI)的勾画，建立预测模型，通过绘制受试者操作特征曲线(ROC), 计算ROC曲线下面积(AUC), 评价影像组学特征在NSCLC病理分级中的应用价值。
      结果CT影像组学模型共提取了1 878个影像组学特征，采用SVM对特征的重要性进行评估，最终降维得到20个特征维度；训练组中AUC值为0.851，准确率为80.35%；测试组中AUC为0.833，准确率为90.90%。
      结论CT影像组学可通过分析各种影像特征对术前NSCLC病理分级进行预判。

       

      Abstract:
      ObjectiveTo investigate the application value of CT radiomics in the pathological grading of non-small cell lung cancer (NSCLC).
      MethodsThe data of 67 patients with pathologically confirmed NSCLC were analyzed retrospectively.Among them, there were 7 cases of grade Ⅰ, 39 cases of grade Ⅱ and 21 cases of grade Ⅲ.According to the degree of differentiation, atypia and number of mitotic images of NSCLC, the patients of grade Ⅰ and Ⅱ were classified into the low-grade group (46 cases) and the patients of grade Ⅲ were classified into the high-grade group (21 cases).Patients were randomly divided into the training group (56 cases) and the verification group (11 cases) according to the ratio of 5∶1.The maximum diameter of the lesions was selected to delineate the area of interest (ROI), and the prediction model was established.By drawing the receiver operating characteristic curve (ROC) and calculating the area under ROC curve (AUC), the application value of radiomics features in the pathological grading of NSCLC was evaluated.
      ResultsA total of 1878 radiomics features were extracted from the CT radiomics model.SVM was used to evaluate the importance of the features, and finally 20 feature dimensions were obtained.In the training group, the AUC value was 0.851, and the accuracy was 80.35%.In the test group, the AUC was 0.833 and the accuracy was 90.90%.
      ConclusionsCT radiomics can predict the pathological grade of preoperative NSCLC by analyzing various image features.

       

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