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Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks.
Lu, Yun; Yu, Qiyue; Gao, Yuanxiang; Zhou, Yunpeng; Liu, Guangwei; Dong, Qian; Ma, Jinlong; Ding, Lei; Yao, Hongwei; Zhang, Zhongtao; Xiao, Gang; An, Qi; Wang, Guiying; Xi, Jinchuan; Yuan, Weitang; Lian, Yugui; Zhang, Dianliang; Zhao, Chunbo; Yao, Qin; Liu, Wei; Zhou, Xiaoming; Liu, Shuhao; Wu, Qingyao; Xu, Wenjian; Zhang, Jianli; Wang, Dongshen; Sun, Zhenqing; Gao, Yuan; Zhang, Xianxiang; Hu, Jilin; Zhang, Maoshen; Wang, Guanrong; Zheng, Xuefeng; Wang, Lei; Zhao, Jie; Yang, Shujian.
Afiliación
  • Lu Y; Affiliated Hospital of Qingdao University, Qingdao, China. luyun@qdyy.cn yuqiyue@qdu.edu.cn.
  • Yu Q; Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.
  • Gao Y; Affiliated Hospital of Qingdao University, Qingdao, China. luyun@qdyy.cn yuqiyue@qdu.edu.cn.
  • Zhou Y; Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.
  • Liu G; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Dong Q; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Ma J; Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.
  • Ding L; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yao H; Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.
  • Zhang Z; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xiao G; Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.
  • An Q; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Wang G; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xi J; Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, & National Clinical Research Center for Digestive Diseases, Beijing, China.
  • Yuan W; Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, & National Clinical Research Center for Digestive Diseases, Beijing, China.
  • Lian Y; Beijing Hospital & National Center of Gerontology, Beijing. China.
  • Zhang D; Beijing Hospital & National Center of Gerontology, Beijing. China.
  • Zhao C; Fourth Hospital of Hebei Medical University, Hebei, China.
  • Yao Q; Fourth Hospital of Hebei Medical University, Hebei, China.
  • Liu W; First Affiliated Hospital of Zhengzhou University, Zhenzhou, China.
  • Zhou X; First Affiliated Hospital of Zhengzhou University, Zhenzhou, China.
  • Liu S; Qingdao Municipal Hospital, Qingdao, China.
  • Wu Q; Qingdao Municipal Hospital, Qingdao, China.
  • Xu W; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zhang J; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Wang D; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Sun Z; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Gao Y; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zhang X; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Hu J; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zhang M; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Wang G; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zheng X; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Wang L; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zhao J; Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yang S; Affiliated Hospital of Qingdao University, Qingdao, China.
Cancer Res ; 78(17): 5135-5143, 2018 09 01.
Article en En | MEDLINE | ID: mdl-30026330
ABSTRACT
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 R-CNN) 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. Cancer Res; 78(17); 5135-43. ©2018 AACR.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Ganglios Linfáticos / Metástasis Linfática Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Female / Humans / Male Idioma: En Revista: Cancer Res Año: 2018 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Ganglios Linfáticos / Metástasis Linfática Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Female / Humans / Male Idioma: En Revista: Cancer Res Año: 2018 Tipo del documento: Article