Predicting response to patients with gastric cancer via a dynamic-aware model with longitudinal liquid biopsy data.

Journal: Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Published Date: Jun 17, 2025

Abstract

BACKGROUND: Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, offering essential cellular and molecular insights while facilitating the capture of time-sensitive information. This study aimed to leverage artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data.

Authors

  • Zifan Chen
    Center for Data Science, Peking University, Haidian District, Beijing, 100080, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.
  • Yanyan Li
    Department of Center of Integrated Traditional Chinese and Western Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Xujiao Feng
    Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Yilin Li
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Xinyu Nan
    Center for Data Science, Peking University, Haidian District, Beijing, 100080, China.
  • Huimin Liu
    Department of Cardiology, The Key Laboratory of Myocardial Ischemia Chinese Ministry of Education Harbin, the Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Nangang District, Harbin, 150086, People's Republic of China.
  • Bin Dong
    Ricoh Software Research Center (Beijing), Beijing, China.
  • Lin Shen
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.

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