Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the SEER database and a multicentre external validation cohort.

Journal: Journal of cancer research and clinical oncology
Published Date: Oct 1, 2023

Abstract

PURPOSE: Due to the rarity of primary gastrointestinal lymphoma (PGIL), the prognostic factors and optimal management of PGIL have not been clearly defined. We aimed to establish prognostic models using a deep learning algorithm for survival prediction.

Authors

  • Feifan Wang
    Gastrointestinal Disease Diagnosis and Treatment Center, The First Hospital of Hebei Medical University, 89 Donggang Road, Shijiazhuang, 050000, China.
  • Lu Chen
    Ultrasonic Department, Zhongda Hospital Affiliated to Southeast University, Nanjing, 210009, China.
  • Lihong Liu
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Yitao Jia
    Department of Oncology, Hebei General Hospital, Shijiazhuang, 050051, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Lianjing Wang
    Department of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
  • Jie Zhi
    Department of Oncology, Hebei General Hospital, Shijiazhuang, 050051, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Weijing Li
    Department of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
  • Zhongxin Li
    Gastrointestinal Disease Diagnosis and Treatment Center, The First Hospital of Hebei Medical University, 89 Donggang Road, Shijiazhuang, 050000, China. lizhongxin99@163.com.

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