单位:[1]Affiliated Hospital of Qingdao University, Qingdao, China.[2]Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.[3]Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, & National Clinical Research Center for Digestive Diseases, Beijing, China.临床科室国家中心普外分中心普外五科(综合普外科)首都医科大学附属北京友谊医院[4]Beijing Hospital & National Center of Gerontology, Beijing. China.[5]Fourth Hospital of Hebei Medical University, Hebei, China.河北医科大学第四医院[6]First Affiliated Hospital of Zhengzhou University, Zhenzhou, China.[7]Qingdao Municipal Hospital, Qingdao, China.[8]The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
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.
基金:
Twelfth Five-Year Plan for Science and Technology Support of China [2013BAI01B03]; Major Science and Technology Project of Independent Innovation of Qingdao, China [14-6-1-6-zdzx]; Key Research and Development Plan (tackle hard-nut problems in science and technology) of Shandong Province [2018GSF118206]
第一作者单位:[1]Affiliated Hospital of Qingdao University, Qingdao, China.[2]Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.[*1]Affiliated Hospital of Qingdao University, Shinan Jiangsu-road No. 16, Qingdao, Shandong 266071, China.
共同第一作者:
通讯作者:
通讯机构:[1]Affiliated Hospital of Qingdao University, Qingdao, China.[2]Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China.[*1]Affiliated Hospital of Qingdao University, Shinan Jiangsu-road No. 16, Qingdao, Shandong 266071, China.
推荐引用方式(GB/T 7714):
Yun Lu,Qiyue Yu,Yuanxiang Gao,et al.Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks[J].CANCER RESEARCH.2018,78(17):5135-5143.doi:10.1158/0008-5472.CAN-18-0494.
APA:
Yun Lu,Qiyue Yu,Yuanxiang Gao,Yunpeng Zhou,Guangwei Liu...&Shujian Yang.(2018).Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks.CANCER RESEARCH,78,(17)
MLA:
Yun Lu,et al."Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks".CANCER RESEARCH 78..17(2018):5135-5143