Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks

被引:81
|
作者
Lu, Yun [1 ,2 ]
Yu, Qiyue [1 ,2 ]
Gao, Yuanxiang [1 ]
Zhou, Yunpeng [1 ,2 ]
Liu, Guangwei [1 ,2 ]
Dong, Qian [1 ,2 ]
Ma, Jinlong [1 ]
Ding, Lei [1 ]
Yao, Hongwei [3 ,4 ]
Zhang, Zhongtao [3 ,4 ]
Xiao, Gang [5 ,6 ]
An, Qi [5 ,6 ]
Wang, Guiying [7 ]
Xi, Jinchuan [7 ]
Yuan, Weitang [8 ]
Lian, Yugui [8 ]
Zhang, Dianliang [9 ]
Zhao, Chunbo [9 ]
Yao, Qin [1 ]
Liu, Wei [1 ]
Zhou, Xiaoming [1 ]
Liu, Shuhao [1 ]
Wu, Qingyao [1 ]
Xu, Wenjian [1 ]
Zhang, Jianli [1 ]
Wang, Dongshen [1 ]
Sun, Zhenqing [1 ]
Gao, Yuan [1 ]
Zhang, Xianxiang [1 ]
Hu, Jilin [1 ]
Zhang, Maoshen [1 ]
Wang, Guanrong [1 ]
Zheng, Xuefeng [1 ]
Wang, Lei [10 ]
Zhao, Jie [1 ]
Yang, Shujian [1 ]
机构
[1] Qingdao Univ, Affiliated Hosp, Shinan Jiangsu Rd 16, Qingdao 266071, Shandong, Peoples R China
[2] Shandong Key Lab Digital Med & Comp Assisted Surg, Qingdao, Peoples R China
[3] Capital Med Univ, Beijing Friendship Hosp, Dept Gen Surg, Beijing, Peoples R China
[4] Natl Clin Res Ctr Digest Dis, Beijing, Peoples R China
[5] Beijing Hosp, Beijing, Peoples R China
[6] Natl Ctr Gerontol, Beijing, Peoples R China
[7] Hebei Med Univ, Hosp 4, Shijiazhuang, Hebei, Peoples R China
[8] Zhengzhou Univ, Affiliated Hosp 1, Zhenzhou, Peoples R China
[9] Qingdao Municipal Hosp, Qingdao, Peoples R China
[10] Sun Yat Sen Univ, Affiliated Hosp 6, Guangzhou, Guangdong, Peoples R China
关键词
COMPUTER-AIDED DIAGNOSIS; CHARACTERISTIC ROC CURVE; RECTAL-CANCER; NODULES;
D O I
10.1158/0008-5472.CAN-18-0494
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
MRI is the gold standard for confirming a pelvic lymph node metastasis diagnosis. Traditionally, medical radiologists have analyzed MRI image features of regional lymph nodes to make diagnostic decisions based on their subjective experience; this diagnosis lacks objectivity and accuracy. This study trained a faster region-based convolutional neural network (Faster RCNN) with 28,080 MRI images of lymph node metastasis, allowing the Faster R-CNN to read those images and to make diagnoses. For clinical verification, 414 cases of rectal cancer at various medical centers were collected, and Faster R-CNN-based diagnoses were compared with radiologist diagnoses using receiver operating characteristic curves (ROC). The area under the Faster R-CNN ROC was 0.912, indicating a more effective and objective diagnosis. The Faster R-CNN diagnosis time was 20 s/case, which was much shorter than the average time (600 s/case) of the radiologist diagnoses. Significance: Faster R-CNN enables accurate and efficient diagnosis of lymph node metastases. (C) 2018 AACR.
引用
收藏
页码:5135 / 5143
页数:9
相关论文
共 50 条
  • [41] Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes
    Cadillolaurentt, Diego Alberto
    Paiva-Peredo, Ernesto Alonso
    [J]. INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING, 2024, 20 (02) : 31 - 45
  • [42] Categorization of Breast Carcinoma Histopathology Images by Utilizing Region-Based Convolutional Neural Networks
    Tuğçe Sena Altuntaş
    Tuğba Toyran
    Sami Arıca
    [J]. Arabian Journal for Science and Engineering, 2024, 49 : 6695 - 6705
  • [43] Categorization of Breast Carcinoma Histopathology Images by Utilizing Region-Based Convolutional Neural Networks
    Altuntas, Tugce Sena
    Toyran, Tugba
    Arica, Sami
    [J]. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2024, 49 (05) : 6695 - 6705
  • [44] Improved Faster Region-Based Convolutional Neural Networks (R-CNN) Model Based on Split Attention for the Detection of Safflower Filaments in Natural Environments
    Zhang, Zhenguo
    Shi, Ruimeng
    Xing, Zhenyu
    Guo, Quanfeng
    Zeng, Chao
    [J]. AGRONOMY-BASEL, 2023, 13 (10):
  • [45] Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network
    Liu, Shang-Long
    Li, Shuo
    Guo, Yu-Ting
    Zhou, Yun-Peng
    Zhang, Zheng-Dong
    Li, Shuai
    Lu, Yun
    [J]. CHINESE MEDICAL JOURNAL, 2019, 132 (23) : 2795 - 2803
  • [46] Study on Power Line Insulator Defect Detection via Improved Faster Region-Based Convolutional Neural Network
    Liao, Guo-Ping
    Yang, Geng-Jie
    Tong, Wen-Tao
    Gao, Wei
    Lv, Fang-Liang
    Gao, Da
    [J]. PROCEEDINGS OF 2019 IEEE 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2019), 2019, : 262 - 266
  • [47] Effects of Faster Region-based Convolutional Neural Network on the Detection Efficiency of Rail Defects under Machine Vision
    Yu Cheng
    Deng HongGui
    Feng YuXin
    [J]. PROCEEDINGS OF 2020 IEEE 5TH INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC 2020), 2020, : 1377 - 1380
  • [48] Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network
    Liu Shang-Long
    Li Shuo
    Guo Yu-Ting
    Zhou Yun-Peng
    Zhang Zheng-Dong
    Li Shuai
    Lu Yun
    [J]. 中华医学杂志(英文版), 2019, 132 (23) : 2795 - 2803
  • [49] Adaptive Localizing Region-Based Level Set for Segmentation of Maxillary Sinus Based on Convolutional Neural Networks
    Qi, Xianglong
    Zhong, Jie
    Cui, Shengjia
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2021, 2021
  • [50] Indoor Localization using Region-based Convolutional Neural Network
    Xu, Haowei
    Koppanyi, Zoltan
    Toth, Charles K.
    Brzezinska, Dorota
    [J]. PROCEEDINGS OF THE 2017 INTERNATIONAL TECHNICAL MEETING OF THE INSTITUTE OF NAVIGATION, 2017, : 1269 - 1279