Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

Journal: Annals of surgical oncology
PMID:

Abstract

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC).

Authors

  • Hao Zhong
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Tongyu Wang
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Mingyu Hou
    Department of Pathology, Qingdao University Affiliated Qingdao Women and Children's Hospital, Qingdao, Shandong, People's Republic of China.
  • Xiaodong Liu
    Academy of Fine Arts, Linyi University, Linyi, Shandong 276000, China.
  • Yulong Tian
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Shougen Cao
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Zequn Li
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Zhenlong Han
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Gan Liu
    Department of information and computing science, University of Science and Technology Beijing, Beijing, 100083, China.
  • Yuqi Sun
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Cheng Meng
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Yujun Li
    Research Center of Intelligent Medical Information Processing, School of Information Science and Engineering, Shandong University, Qingdao 266237, China. Electronic address: liyujun@sdu.edu.cn.
  • Yanxia Jiang
    Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Qinglian Ji
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Dapeng Hao
    Department of Radiology, The Affiliated Hospital of Qingdao University, Shinan Jiangsu 16 Rd, Qingdao, Shandong 266003, China.
  • Zimin Liu
    Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
  • Yanbing Zhou
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China. zhouyanbing@qduhospital.cn.