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基于定量影像组学的肺肿瘤良恶性预测方法

张利文 刘侠 汪俊 董迪 宋江典 臧亚丽 田捷

张利文, 刘侠, 汪俊, 董迪, 宋江典, 臧亚丽, 田捷. 基于定量影像组学的肺肿瘤良恶性预测方法. 自动化学报, 2017, 43(12): 2109-2114. doi: 10.16383/j.aas.2017.c160264
引用本文: 张利文, 刘侠, 汪俊, 董迪, 宋江典, 臧亚丽, 田捷. 基于定量影像组学的肺肿瘤良恶性预测方法. 自动化学报, 2017, 43(12): 2109-2114. doi: 10.16383/j.aas.2017.c160264
ZHANG Li-Wen, LIU Xia, WANG Jun, DONG Di, SONG Jiang-Dian, ZANG Ya-Li, TIAN Jie. Prediction of Malignant and Benign Lung Tumors Using a Quantitative Radiomic Method. ACTA AUTOMATICA SINICA, 2017, 43(12): 2109-2114. doi: 10.16383/j.aas.2017.c160264
Citation: ZHANG Li-Wen, LIU Xia, WANG Jun, DONG Di, SONG Jiang-Dian, ZANG Ya-Li, TIAN Jie. Prediction of Malignant and Benign Lung Tumors Using a Quantitative Radiomic Method. ACTA AUTOMATICA SINICA, 2017, 43(12): 2109-2114. doi: 10.16383/j.aas.2017.c160264

基于定量影像组学的肺肿瘤良恶性预测方法

doi: 10.16383/j.aas.2017.c160264
基金项目: 

黑龙江省然科学基金 F201311

国家自然科学基金 81227901

国家自然科学基金 81301346

黑龙江省然科学基金 12541105

国家自然科学基金 81501616

中国科学院科研设备项目 YZ201502

国家自然科学基金 61231004

中国科学院科技服务网络计划 KFJ-SW-STS-160

国家自然科学基金 61672197

国家自然科学基金 81527805

详细信息
    作者简介:

    张利文    中国科学院自动化研究所和哈尔滨理工大学自动化学院联合培养硕士研究生.主要研究方向为医学图像处理.E-mail:zhangliwen2015@ia.ac.cn

    刘侠    哈尔滨理工大学自动化学院教授.2006年获得哈尔滨工程大学自动化学院博士学位.主要研究方向为模式识别与智能系统.E-mail:liuxia@hrbust.edu.cn

    汪俊     哈尔滨理工大学测控技术与通信工程学院硕士研究生.主要研究方向为图像处理和模式识别.E-mail:wangjun.542@163.com

    董迪     中国科学院自动化研究所副研究员.2013年获得中国科学院自动化研究所博士学位.主要研究方向为影像组学.E-mail:di.dong@ia.ac.cn

    宋江典    东北大学中荷生物医学与信息工程学院博士研究生, IEEE会员.主要研究方向为医学影像处理与分析.E-mail:dr.j.song@ieee.org

    臧亚丽    中国科学院自动化研究所副研究员.2013年获得中国科学院自动化研究所博士学位.主要研究方向为影像组学.E-mail:yali.zang@ia.ac.cn

    通讯作者:

    田捷     中国科学院自动化研究所研究员.1992年获得中国科学院自动化研究所博士学位.主要研究方向为模式识别, 医学图像处理和分子影像.本文通信作者.E-mail:tian@ieee.org

Prediction of Malignant and Benign Lung Tumors Using a Quantitative Radiomic Method

Funds: 

Natural Science Foundation of Heilongjiang Province F201311

National Natural Science Foundation of China 81227901

National Natural Science Foundation of China 81301346

Natural Science Foundation of Heilongjiang Province 12541105

National Natural Science Foundation of China 81501616

Chinese Academy of Science Program of Equipment YZ201502

National Natural Science Foundation of China 61231004

Chinese Academy of Science Program of Scientific Service Network KFJ-SW-STS-160

National Natural Science Foundation of China 61672197

National Natural Science Foundation of China 81527805

More Information
    Author Bio:

       Master student at the Institute of Automation, Chinese Academy of Sciences, and School of Automation, Harbin University of Science and Technology. His main research interest is medical imaging processing

       Professor at the School of Automation, Harbin University of Science and Technology. He received his Ph.D. degree from Harbin Engineering University in 2006. His research interest covers pattern recognition and intelligent systems

       Master student at the School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology. His research interest covers imaging processing and pattern recognition

       Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Chinese Academy of Sciences in 2013. His research interest covers radiomics

       Ph.D. candidate at Biomedical Engineering, School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, IEEE member. His research interest covers medical image processing and analysis

       Associate professor at the Institute of Automation, Chinese Academy of Sciences. She received her Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2013. Her research interest covers radiomics

    Corresponding author: TIAN Jie    Professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 1992. His research interest covers pattern recognition, medical image processing, and molecular imaging. Corresponding author of this paper
  • 摘要: 肺癌是世界范围内致死率最高的癌症之一,肺肿瘤的良恶性诊断对于治疗方式选择意义重大.本文借助影像组学(Radiomics)方法利用LIDC(Lung imaging database consortium)肺癌公开数据库中619例病人的肺癌计算机断层(Computed tomography,CT)影像数据,分割出病变区域,并结合肿瘤医学特性和临床认知,提取反映肿瘤形状大小、强度和纹理特性的60个定量影像特征,然后利用支持向量机(Support vector machine,SVM)构建诊断肺肿瘤良恶性的预测模型,筛选出对诊断肺肿瘤良恶性有价值的20个影像组学特征.为肺肿瘤良恶性预测提供了一种非入侵的检测手段.随着CT影像在肺癌临床诊断中的广泛使用,应用样本量的不断增加,本文方法有望成为一种辅助诊断工具,有效提高临床肺肿瘤良恶性诊断准确率.
    1)  本文责任编委 朱朝喆
  • 图  1  肺部病变区域分割

    Fig.  1  Segmentation result of lung lesion

    图  2  肺肿瘤良恶性预测模型的生成和验证示意图

    Fig.  2  Diagram of generation and validation of benign and malignant discrimination model of lung tumor

    图  3  遗传算法流程图

    Fig.  3  Flow chart of genetic algorithm

    图  4  遗传算法适应度曲线

    Fig.  4  Fitness curve of genetic algorithm

    表  1  肺肿瘤良恶性预测模型的诊断准确率

    Table  1  Diagnostic accuracy of benign and malignant discrimination model of lung tumor

    数据集灵敏度(%)特异性(%)阳性预测值(%)阴性预测值(%)准确度(%)
    训练集82.282.682.582.282.4
    (175/213)(176/213)(175/212)(176/214)(351/426)
    测试集75.481.687.665.977.7
    (92/122)(58/71)(92/105)(58/88)(150/193)
    总计79.982.484.277.580.9
    (257/335)(234/284)(267/317)(234/302)(501/619)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-03-11
  • 录用日期:  2017-01-04
  • 刊出日期:  2017-12-20

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