Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model: a multicenter study.

Journal: International journal of surgery (London, England)
PMID:

Abstract

BACKGROUND: Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggressive biological characteristics and a dismal prognosis. Predicting early recurrence may help determine treatment strategies for LAGC. The goal is to develop a deep learning model for early recurrence prediction (DLER) based on preoperative multiphase computed tomography (CT) images and to further explore the underlying biological basis of the proposed model.

Authors

  • Xinyu Guo
    Keya Medical Co., Ltd, Longong, Shenzhen, China. Electronic address: xinyug@keyamedical.com.
  • Mingzhen Chen
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
  • Lingling Zhou
    Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. zllgwy@126.com.
  • Lingyi Zhu
    Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Lishui Hospital, School of Medicine, Zhejiang University, Lishui, China.
  • Shuang Liu
    Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China.
  • Liyun Zheng
    School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
  • Yongjun Chen
    Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Qiang Li
    Department of Dermatology, Air Force Medical Center, PLA, Beijing, People's Republic of China.
  • Shuiwei Xia
    Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
  • Chenying Lu
    Departments of Medicine and Radiology, State University of New York, Upstate Medical University Hospital, Syracuse, USA.
  • Minjiang Chen
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China.
  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Jiansong Ji
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China.