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基于CT征象量化分析的肺结节恶性度分级

陈皓 段红柏 郭紫园 强永乾

陈皓, 段红柏, 郭紫园, 强永乾. 基于CT征象量化分析的肺结节恶性度分级[J]. 电子与信息学报, 2021, 43(5): 1405-1413. doi: 10.11999/JEIT200167
引用本文: 陈皓, 段红柏, 郭紫园, 强永乾. 基于CT征象量化分析的肺结节恶性度分级[J]. 电子与信息学报, 2021, 43(5): 1405-1413. doi: 10.11999/JEIT200167
Hao CHEN, Hongbai DUAN, Ziyuan GUO, Yongqian QIANG. Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis[J]. Journal of Electronics and Information Technology, 2021, 43(5): 1405-1413. doi: 10.11999/JEIT200167
Citation: Hao CHEN, Hongbai DUAN, Ziyuan GUO, Yongqian QIANG. Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis[J]. Journal of Electronics and Information Technology, 2021, 43(5): 1405-1413. doi: 10.11999/JEIT200167

基于CT征象量化分析的肺结节恶性度分级

doi: 10.11999/JEIT200167
基金项目: 国家自然科学基金(61876138, 61203311),陕西省自然科学基金 (2019JM-365),陕西省教育厅自然科学专项(17JK0701),陕西省网络数据分析与智能处理重点实验室开放课题基金(XUPT-KLND(201804)),西安邮电大学创新基金(CXJJLI2018017)
详细信息
    作者简介:

    陈皓:男,1978年生,博士,副教授,硕士生导师,主要研究方向为医疗大数据

    段红柏:男,1993年生,硕士生,研究方向为数据挖掘和模式识别

    郭紫园:女,1996年生,硕士生,研究方向为数据挖掘和进化计算

    强永乾:男,1965年生,博士,副教授,硕士生导师,研究方向为医学影像学

    通讯作者:

    陈皓 chenhao@xupt.edu.cn

  • 中图分类号: TN911.73; TP391.41

Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis

Funds: The National Natural Science Foundation of China (61876138, 61203311), The Natural Science Basic Research Program of Shaanxi Province (2019JM-365), The Scientific Research Program Funded by Shaanxi Provincial Education Department (17JK0701), The Science Foundation of the Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing (XUPT-KLND (201804)), The Innovation Funds of Xi'an University of Posts and Telecommunications (CXJJLI2018017)
  • 摘要: 为了提高肺结节恶性度分级的计算精度及可解释性,该文提出一种基于CT征象量化分析的肺结节恶性度分级方法。首先,融合影像组学特征和通过卷积神经网络提取的高阶特征构造分析CT征象所需的特征集; 接着,在混合特征集的基础上利用进化搜索机制优化集成学习分类器,实现对7种肺结节征象的识别和量化打分; 最后,将7种CT征象的量化打分输入到一个利用差分进化算法优化产生的多分类器,实现肺结节恶性度的分级计算。在实验研究中使用LIDC-IDRI数据集中的2000个肺结节样本进行进化集成学习器和恶性度分级器的训练和测试。实验结果显示对7种CT征象的识别准确率可达0.9642以上,肺结节恶性度分级的准确率为0.8618,精确率为0.8678,召回率为0.8617,F1指标为0.8627。与多个典型算法的比较显示,该文方法不但具有较高的准确率,而且可对相关CT征象进行量化分析,使得对恶性度的分级结果更具可解释性。
  • 图  1  肺结节恶性度分级流程

    图  2  多特征融合流程

    图  3  基于进化搜索构造集成学习器的流程

    图  4  进化集成学习器的优化过程

    图  5  不同特征集合的聚类结果可视化对比

    表  1  不同征象的有效特征

    特征阈值特征总数量影像组学特征数CNN特征数
    精细度0.0050692841
    球形度0.003013623113
    边缘0.00351002872
    分叶征0.000920334169
    毛刺征0.0060552322
    纹理征0.0070391227
    钙化征0.0300682048
    下载: 导出CSV

    表  2  恶性度分级和CT征象量化的精度

    指标精细度球形度边缘分叶征毛刺征纹理征钙化征恶性度
    ACC0.96680.97640.96420.96930.97920.97280.98440.8618
    Pre0.96480.97720.96590.95220.96950.97330.96830.8678
    Rec0.96510.97850.93460.94970.97080.97360.96740.8619
    F10.96490.97780.94990.95090.97010.97350.96780.8627
    下载: 导出CSV

    表  3  恶性度分级模型的权重系数

    恶性度等级精细度球形度边缘分叶征毛刺征纹理征钙化征
    1–0.0703–0.85600.38350.29350.27510.0848–0.7668
    2–0.04520.30540.5043–0.00620.0069–0.6435–0.4618
    3–0.4187–0.35070.41510.33370.17592–0.03670.5758
    40.1984–0.19480.42210.03690.32170.01130.2751
    50.8548–0.47590.2756–0.29900.4641–0.11760.5337
    下载: 导出CSV

    表  4  不同集成学习器的量化计算结果对比

    对比分类器指标精细度球形度边缘分叶征毛刺征纹理征钙化征
    ETACC0.96380.95260.94220.96030.93720.95720.9512
    Pre0.96370.95580.95060.96160.93720.95740.9507
    Rec0.96460.95320.89940.95900.93740.95720.9517
    F10.96380.95410.91970.95980.93650.95730.9511
    树个数112100881081527676
    XGBoostACC0.96210.94280.94520.95600.92750.93120.9498
    Pre0.96190.94420.95040.95710.92760.93110.9489
    Rec0.96250.94370.92660.95660.92890.93110.9506
    F10.96210.94380.93770.95660.92820.93110.9496
    树个数18818019218817611080
    RFACC0.95850.94110.94220.94910.94900.96370.9471
    Pre0.95830.94490.93990.94970.94920.96420.9466
    Rec0.95930.94220.91660.94880.95020.96370.9477
    F10.95860.94320.92700.94910.94880.96390.9470
    树个数12817218813612812872
    本文方法ACC0.96680.97640.96420.96930.97920.97280.9844
    Pre0.96480.97720.96590.95220.96950.97330.9683
    Rec0.96510.97850.93460.96970.97080.97360.9674
    F10.96490.97780.94990.95090.97010.97350.9678
    树个数77677854706023
    下载: 导出CSV

    表  5  相关文献的量化结果对比

    文献精细度球形度边缘分叶征毛刺征纹理征钙化征恶性度
    文献[11]0.71900.52220.7250//0.83400.90800.8420
    文献[22]0.71630.73920.73320.72480.74220.76770.73900.8194
    文献[23]0.74310.76220.70130.80010.78280.9002/0.7556
    文献[24]0.89330.89330.89330.89330.89330.89330.89330.8933
    本文方法0.96680.97640.96420.96930.97920.97280.98440.8618
    下载: 导出CSV

    表  6  不同特征集合的聚类结果对比

    特征集均一性v-measure互信息
    影像组学特征0.326100.32330.3179
    CNN特征0.443420.42820.4118
    融合特征0.608500.59340.5771
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-13
  • 修回日期:  2020-09-25
  • 网络出版日期:  2020-10-16
  • 刊出日期:  2021-05-18

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