基于增强CT深度学习影像组学术前预测胸腺瘤风险分类

吴树剑 俞咏梅 范莉芳 过永 张虎 朱浩雨 任超 徐争元

吴树剑, 俞咏梅, 范莉芳, 过永, 张虎, 朱浩雨, 任超, 徐争元. 基于增强CT深度学习影像组学术前预测胸腺瘤风险分类[J]. 中国肿瘤临床, 2023, 50(19): 999-1005. doi: 10.12354/j.issn.1000-8179.2023.20230828
引用本文: 吴树剑, 俞咏梅, 范莉芳, 过永, 张虎, 朱浩雨, 任超, 徐争元. 基于增强CT深度学习影像组学术前预测胸腺瘤风险分类[J]. 中国肿瘤临床, 2023, 50(19): 999-1005. doi: 10.12354/j.issn.1000-8179.2023.20230828
Shujian Wu, Yongmei Yu, Lifang Fan, Yong Guo, Hu Zhang, Haoyu Zhu, Chao Ren, Zhengyuan Xu. Preoperative prediction of thymoma risk classification based on enhanced CT deep learning radiomics[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(19): 999-1005. doi: 10.12354/j.issn.1000-8179.2023.20230828
Citation: Shujian Wu, Yongmei Yu, Lifang Fan, Yong Guo, Hu Zhang, Haoyu Zhu, Chao Ren, Zhengyuan Xu. Preoperative prediction of thymoma risk classification based on enhanced CT deep learning radiomics[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(19): 999-1005. doi: 10.12354/j.issn.1000-8179.2023.20230828

基于增强CT深度学习影像组学术前预测胸腺瘤风险分类

doi: 10.12354/j.issn.1000-8179.2023.20230828
基金项目: 本文课题受皖南医学院校级重点研究项目(编号:WK2021Z15)资助
详细信息
    作者简介:

    吴树剑:专业方向为影像医学与核医学研究

    通讯作者:

    徐争元 xuzy@wnmc.edu.cn

Preoperative prediction of thymoma risk classification based on enhanced CT deep learning radiomics

Funds: This work was supported by the Key Research Project of Wannan Medical College (No. WK2021Z15)
More Information
  • 摘要:   目的  探讨基于增强CT深度学习影像组学术前预测胸腺瘤风险分类的价值。  方法  收集2015年1月至2023年1月皖南医学院弋矶山医院139例胸腺瘤患者为训练集,池州市人民医院与芜湖市第二人民医院81例患者为外部验证集。其中低风险组(A、AB、B1型)137例、高风险组(B2、B3型)83例。基于CT静脉期图像分别提取手工影像组学(hand-crafted radiomics,HCR)特征与深度学习(deep learning,DL)特征,构建影像组学标签评分(Radscore)。单因素与多因素Logistic回归分析筛选预测胸腺瘤高风险的影像学独立影响因素,利用逻辑回归(Logistic regression,LR)、随机森林(random forest,RF)、决策树(decision tree,DT)及支持向量机(support vector machine,SVM)机器学习建模。选择最佳模型为输出模型,构建模型列线图、校准曲线及临床决策曲线(decision curve analysis,DCA)。受试者工作特征(receiver operating characteristics,ROC)曲线下面积(area under the curve,AUC)及净重新分类指数(net reclassification index,NRI)用于评估模型效能。  结果  LR、RF、DT及SVM构建预测模型的AUC分别为训练集0.899、0.835 、0.897、0.860,外部验证集0.889、0.830、0.905、0.858。由于训练集LR模型效能最佳,作为本研究输出模型,校准曲线及DCA表明模型具有较高的校准度及临床适用性。列线图与Radscore比较效能有改善,NRI训练集为7.5%(P=0.007),外部验证集为5.3%(P=0.020)。  结论  基于增强CT深度学习影像组学术前实现了胸腺瘤准确风险分类,列线图能够提供个性化预测结果。

     

  • 图  1  图像分割

    A:勾画的ROI;B:融合生成的VOI

    图  2  ResNet网络模型的残差块

    图  3  CT胸腺瘤CT影像典型表现

    A~B:低风险;C~D:高风险

    图  4  模型可视化及临床评价

    A:5折交叉验证;B:预测模型的列线图;C:训练集校准曲线;D:外部验证集校准曲线;E:训练集临床决策曲线,F:外部验证集临床决策曲线

    表  1  训练集组内及训练集与外部验证集组间临床影像特征比较

    变量 训练集(n=139)  x2 P 外部验证集(n=81) x2 P
    低风险组(n=86) 高风险组(n=53) 低风险组(n=51) 高风险组(n=30)
    性别     0.950 0.330     0.042 0.838
     男 43 31 26 16
     女 43 22 25 14
    位置 0.513 0.774 1.611 0.447
     右 26 18 21 10
     居中 24 12 12 11
     左 36 23 18 9
    形态 10.404 0.001 5.985 0.014
     规则 55 19 33 11
     不规则 31 34 18 19
    边界 6.342 0.012 5.544 0.019
     清晰 75 37 45 20
     不清晰 11 16 6 10
    钙化 0.503 0.778 2.395 0.302
     弧形 9 4 2 3
     簇状 8 4 8 2
     无 69 45 41 25
    坏死囊变 0.040 0.841 1.711 0.191
     有 23 15 18 13
     无 63 38 43 17
    增强程度 5.890 0.015 5.931 0.015
     中低强化 51 42 27 24
     明显强化 35 11 24 6
    增强均匀性 0.209 0.647 0.106 0.745
     均匀 44 25 24 13
     不均匀 42 28 27 17
    周围侵犯 13.779 <0.001 6.953 0.008
     有 4 14 3 8
     无 82 39     48 22    
    下载: 导出CSV

    表  2  影像特征预测胸腺瘤高风险的影响因素分析变量

    项目 单因素分析 多因素分析
    B OR(95%CI) P B OR(95%CI) P
    形态 1.155 3.175(1.556~6.480) 0.002 0.903 2.466(1.124~5.410) 0.024
    边界 1.081 2.948(1.244~6.987) 0.014 0.873 2.395(0.904~6.343) 0.079
    增强程度 −0.963 0.382(0.173~0.842) 0.017 −0.977 0.377(0.160~0.888) 0.026
    周围侵犯 1.996 7.359(2.273~23.824) 0.001 1.888 6.605(1.850~23.579) 0.004
    下载: 导出CSV

    表  3  LR、RF、DT及SVM机器学习算法构建模型的效能评价

    模型AUC(95%CI)准确度(%)敏感度(%)特异度(%)阳性预测值(%)阴性预测值(%)
    训练集
     LR0.899(0.848~0.950)82.084.979.172.689.6
     RF0.835(0.761~0.908)80.666.090.779.581.1
     DT0.897(0.844~0.950)83.577.487.278.886.2
     SVM0.860(0.796~0.923)78.483.076.768.386.8
    外部验证集
     LR0.889(0.817~0.961)82.786.780.472.291.1
     RF0.830(0.736~0.925)74.193.362.759.694.1
     DT0.905(0.839~0.970)82.780.084.375.087.8
     SVM0.858(0.772~0.944)85.270.094.187.584.2
    下载: 导出CSV

    表  4  机器学习算法构建模型的效能比较

    模型 Z P
    训练集
     LR vs. RF 3.459 <0.001
     LR vs. DT 0.112 0.911
     LR vs. SVM 1.864 0.062
     RF vs. DT −2.318 0.020
     RF vs. SVM −0.787 0.431
     DT vs. SVM 1.549 0.121
    外部验证集
     LR vs. RF 2.365 0.018
     LR vs. DT −0.702 0.483
     LR vs. SVM 1.484 0.138
     RF vs. DT −2.045 0.041
     RF vs. SVM −0.914 0.361
     DT vs. SVM 1.318 0.188
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-08-24
  • 录用日期:  2023-10-23
  • 修回日期:  2023-10-20
  • 网络出版日期:  2023-11-15

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