Literature DB >> 30026330

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

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


  21 in total

1.  Radiomics signature for the preoperative assessment of stage in advanced colon cancer.

Authors:  Yu Li; Aydin Eresen; Yun Lu; Jia Yang; Junjie Shangguan; Yury Velichko; Vahid Yaghmai; Zhuoli Zhang
Journal:  Am J Cancer Res       Date:  2019-07-01       Impact factor: 6.166

2.  Future of Radiotherapy in Nasopharyngeal Carcinoma.

Authors:  Xue-Song Sun; Xiao-Yun Li; Qiu-Yan Chen; Lin-Quan Tang; Hai-Qiang Mai
Journal:  Br J Radiol       Date:  2019-07-09       Impact factor: 3.039

Review 3.  Artificial Intelligence for Precision Oncology.

Authors:  Sherry Bhalla; Alessandro Laganà
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 2.622

4.  Differentiate cavernous hemangioma from schwannoma with artificial intelligence (AI).

Authors:  Shaowei Bi; Rongxin Chen; Kai Zhang; Yifan Xiang; Ruixin Wang; Haotian Lin; Huasheng Yang
Journal:  Ann Transl Med       Date:  2020-06

5.  Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy.

Authors:  Haitao Zhu; Xiaoyan Zhang; Xiaoting Li; Yanjie Shi; Huici Zhu; Yingshi Sun
Journal:  Chin J Cancer Res       Date:  2019-12       Impact factor: 5.087

Review 6.  Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects.

Authors:  Ke-Wei Wang; Ming Dong
Journal:  World J Gastroenterol       Date:  2020-09-14       Impact factor: 5.742

Review 7.  Emerging radiotherapy technologies and trends in nasopharyngeal cancer.

Authors:  Michelle Tseng; Francis Ho; Yiat Horng Leong; Lea Choung Wong; Ivan Wk Tham; Timothy Cheo; Anne Wm Lee
Journal:  Cancer Commun (Lond)       Date:  2020-08-03

8.  Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer.

Authors:  Lei Ding; Guang-Wei Liu; Bao-Chun Zhao; Yun-Peng Zhou; Shuai Li; Zheng-Dong Zhang; Yu-Ting Guo; Ai-Qin Li; Yun Lu; Hong-Wei Yao; Wei-Tang Yuan; Gui-Ying Wang; Dian-Liang Zhang; Lei Wang
Journal:  Chin Med J (Engl)       Date:  2019-02       Impact factor: 2.628

9.  Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study.

Authors:  Xingyu Zhao; Peiyi Xie; Mengmeng Wang; Wenru Li; Perry J Pickhardt; Wei Xia; Fei Xiong; Rui Zhang; Yao Xie; Junming Jian; Honglin Bai; Caifang Ni; Jinhui Gu; Tao Yu; Yuguo Tang; Xin Gao; Xiaochun Meng
Journal:  EBioMedicine       Date:  2020-06-05       Impact factor: 8.143

10.  Real-time colorectal cancer diagnosis using PR-OCT with deep learning.

Authors:  Yifeng Zeng; Shiqi Xu; William C Chapman; Shuying Li; Zahra Alipour; Heba Abdelal; Deyali Chatterjee; Matthew Mutch; Quing Zhu
Journal:  Theranostics       Date:  2020-02-03       Impact factor: 11.556

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