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基于3D增强CT影像组学的肾癌亚型三分类预测模型

张海捷 殷夫 陈梦林 漆安琪 杨丽洋 崔维维 杨姗姗 文戈

张海捷, 殷夫, 陈梦林, 漆安琪, 杨丽洋, 崔维维, 杨姗姗, 文戈. 基于3D增强CT影像组学的肾癌亚型三分类预测模型[J]. 分子影像学杂志, 2021, 44(3): 427-434. doi: 10.12122/j.issn.1674-4500.2021.03.03
引用本文: 张海捷, 殷夫, 陈梦林, 漆安琪, 杨丽洋, 崔维维, 杨姗姗, 文戈. 基于3D增强CT影像组学的肾癌亚型三分类预测模型[J]. 分子影像学杂志, 2021, 44(3): 427-434. doi: 10.12122/j.issn.1674-4500.2021.03.03
Haijie ZHANG, Fu YIN, Menglin CHEN, Anqi QI, Liyang YANG, Weiwei CUI, Shanshan YANG, Ge WEN. A three categories prediction model for renal cell carcinoma subtype based on 3D enhanced CT radiomics[J]. Journal of Molecular Imaging, 2021, 44(3): 427-434. doi: 10.12122/j.issn.1674-4500.2021.03.03
Citation: Haijie ZHANG, Fu YIN, Menglin CHEN, Anqi QI, Liyang YANG, Weiwei CUI, Shanshan YANG, Ge WEN. A three categories prediction model for renal cell carcinoma subtype based on 3D enhanced CT radiomics[J]. Journal of Molecular Imaging, 2021, 44(3): 427-434. doi: 10.12122/j.issn.1674-4500.2021.03.03

基于3D增强CT影像组学的肾癌亚型三分类预测模型

doi: 10.12122/j.issn.1674-4500.2021.03.03
基金项目: 

广东省自然科学基金 2020A151501046

详细信息
    作者简介:

    张海捷,硕士,主治医师,E-mail: zhjszey@163.com

    通讯作者:

    文戈,博士,主任医师,E-mail: m13360022166@163.com

A three categories prediction model for renal cell carcinoma subtype based on 3D enhanced CT radiomics

  • 摘要: 目的探讨可靠的基于3D多期增强CT影像组学特征的肾癌亚型三分类预测模型。方法210例肾细胞癌患者(透明细胞癌143例,乳头状癌25例,嫌色细胞癌29例,其他亚型的肾细胞癌13例)被纳入研究。使用ITK-SNAP软件,获取患者的3D增强CT病灶分割图像,使用PyRadiomics计算平台进行特征提取,使用集成学习分层bagging法来筛选特征和构建肾细胞癌亚型三分类预测模型:首先用100次5折交叉验证将模型分为训练集和测试集,然后将Lasso回归作为基学习器对影像组学特征的进行筛选,最后使用logistic回归作为基学习器进行建模和校正。根据不同期像的CT图像,构建平扫期模型、皮髓质期模型、实质期模型、排泄期模型和全期模型。使用准确率、精确率、敏感度和Kappa值评估测试集上不同期像预测模型的性能。结果每期CT图像中提取到了105个影像组学特征。在5个模型中,全期模型的预测效能最好,准确率为0.81,AUC为0.85;精确度为0.717;敏感度为0.799,kappa值为0.679。全期模型的影像组学特征中,有4个皮髓质期特征、3个实质期特征、1个排泄期特征和1个平扫期特征,且与其他4个单期模型中的特征没有重叠。在4个单期模型中,实质期模型的性能最好,准确性0.786,精确度0.689,敏感度0.734,AUC 0.811,Kappa值0.532;皮髓质期模型和排泄期模型的性能相似,但是排泄期模型的Kappa值0.285,明显低于皮髓质期的Kappa值0.446。平扫期模型的性能最差,AUC为0.693。结论基于3D多期增强CT影像组学特征的全期模型是区分肾细胞癌亚型的可靠和有效的方法。

     

  • 图  1  患者入组流程图

    Figure  1.  Flow chart of patient enrollment.

    图  2  皮髓质期分割图像示例

    Figure  2.  CT image segmentation in a cortico-medullary phase enhanced CT scan.

    图  3  集成学习特征筛选流程图

    Figure  3.  Flow chart of the ensemble learning bagging method of feature selection.

    图  4  集成学习模型构建流程图

    Figure  4.  Flow chart of the ensemble learning bagging method of model building.

    表  1  不同肾癌亚型患者的一般情况

    Table  1.   General situation of patients with different subtypes of renal cell carcinoma [n(%)]

    因素 肾细胞癌 P
    透明细胞癌(n=143) 乳头状肾癌(n=25) 嫌色细胞癌(n=29) 其他类型肾癌(n=13)
    性别 0.881
      男 92(64.3) 18(72.0) 13(44.8) 7(53.8)
      女 51(35.7) 7(28.0) 16(55.2) 6(46.2)
    年龄(岁,Mean±SD 53.2±11.58 51.3±12.59 54.14±13.76 52.1±19.88 0.327
    分布 0.772
      右侧 62(43.4) 15(60.0) 16(55.2) 5(38.5)
      左侧 81(56.6) 10(40.0) 13(44.8) 8(61.5)
    下载: 导出CSV

    表  2  各期模型的影像组学特征和LASSO系数

    Table  2.   Radiomic features and lasso coefficient of each phase model

    模型 影像组学特征名称 特征类別 LASSO系数
    NCP Volume Shape 0.098130
    Major Axis Shape 0.104464
    Least Axis Shape 0.12697
    Maximum 2D Diameter Column Shape 0.054351
    Large Dependence Emphasis Gldm 0.123407
    Large Dependence Low Gray Level Emphasis Gldm 0.098824
    Large Dependence High Gray Level Emphasis Gldm 0.123407
    Kurtosis First order 0.085602
    Run Length Non Uniformity glrlm 0.080397
    Long Run High Gray Level Emphasis glrlm 0.123407
    CMP Large Dependence Emphasis Gldm 0.172186
    Cluster Prominence Glcm 0.172011
    Total Energy First order 0.101716
    Gray Level Non Uniformity Glrlm 0.149354
    Size Zone Non Uniformity Glszm 0.153395
    Large Area Low Gray Level Emphasis Glszm 0.133549
    Large Area High Gray Level Emphasis Glszm 0.132384
    PP Dependence Non Uniformity Gldm_ 0.088395
    Gray Level Non Uniformity Gldm_ 0.168465
    Large Dependence Emphasis Gldm_ 0.082350
    Total Energy First order 0.149260
    Long Run High Gray Level Emphasis Glrlm 0.088069
    Zone Variance Glszm 0.147044
    Gray Level Non Uniformity Glszm 0.144033
    Busyness Ngtdm 0.132384
    EP Volume Shape 0.092024
    Gray Level Non Uniformity Gldm 0.099262
    Large Dependence Emphasis Gldm 0.128963
    Total Energy First order 0.082465
    Gray Level Non Uniformity Glrlm 0.105180
    Run Length Non Uniformity Glrlm 0.093679
    Long Run High Gray Level Emphasis Glrlm 0.114855
    Zone Variance Glszm 0.088184
    Large Area Emphasis Glszm 0.084421
    Busyness Ngtdm 0.110967
    ALL-P Maximum Probability CMP-Glcm 0.141184
    Idn CMP-Glcm 0.110634
    Low Gray Level Zone Emphasis CMP-Glszm 0.105727
    Small Area Low Gray Level Emphasis CMP-Glszm 0.113860
    Elongation PP-Shape 0.101759
    Kurtosis PP-First order 0.109194
    Small Area High Gray Level Emphasis PP-Glszm 0.096117
    Maximum 3D Diameter EP-Shape 0.083936
    Minimum NCP-First order 0.137583
    NCP:平扫期;CMP:皮质-髓质期;PP:实质期;EP:排泄期
    下载: 导出CSV

    表  3  测试集上5种模型鉴别肾癌亚型的性能

    Table  3.   Performance of five models in the test set in differentiating renal cell carcinoma subtypes

    模型 准确率 精确率 敏感度 AUC(95%CI) Kappa
    平扫期模型 0.529 0.533 0.555 0.693(0.509~0.876) 0.163
    皮髓质期模型 0.764 0.671 0.727 0.752(0.662~0.869) 0.446
    实质期模型 0.783 0.689 0.734 0.811(0.659~0.912) 0.532
    排泄期模型 0.711 0.62 0.665 0.725(0.575~0.831) 0.285
    全期模型 0.810 0.717 0.799 0.853(0.768~0.889) 0.679
    Kappa:0~1表示不同级别的一致性:0.00~0.20极低的一致性,0.21~0.40一般的一致性,0.41~0.60中等的一致性,0.61~0.80高度的一致性,0.81~1.00几乎完全一致.
    下载: 导出CSV
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  • 收稿日期:  2021-04-03
  • 刊出日期:  2021-05-20

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