| Literature DB >> 30026330 |
Yun Lu1,2, Qiyue Yu1,2, Yuanxiang Gao3, Yunpeng Zhou3,2, Guangwei Liu3,2, Qian Dong3,2, Jinlong Ma3, Lei Ding3, Hongwei Yao4, Zhongtao Zhang4, Gang Xiao5, Qi An5, Guiying Wang6, Jinchuan Xi6, Weitang Yuan7, Yugui Lian7, Dianliang Zhang8, Chunbo Zhao8, Qin Yao3, Wei Liu3, Xiaoming Zhou3, Shuhao Liu3, Qingyao Wu3, Wenjian Xu3, Jianli Zhang3, Dongshen Wang3, Zhenqing Sun3, Yuan Gao3, Xianxiang Zhang3, Jilin Hu3, Maoshen Zhang3, Guanrong Wang3, Xuefeng Zheng3, Lei Wang9, Jie Zhao3, Shujian Yang3.
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. ©2018 American Association for Cancer Research.Entities:
Mesh:
Year: 2018 PMID: 30026330 DOI: 10.1158/0008-5472.CAN-18-0494
Source DB: PubMed Journal: Cancer Res ISSN: 0008-5472 Impact factor: 12.701