A prior knowledge-supervised fusion network predicts survival after radiotherapy in patients with advanced gastric cancer.

Journal: Artificial intelligence in medicine
Published Date: Jun 19, 2025

Abstract

BACKGROUND AND OBJECTIVE: Predicting overall survival (OS) for advanced gastric cancer patients after radiotherapy is critical for developing an individualized treatment plan. However, existing studies have focused on gastric cancer CT images with a large amount of redundant information, neglecting the role of physicians' prior knowledge in guiding gastric cancer CT image information. We propose a multimodal fusion method based on prior knowledge to predict OS after radiotherapy in advanced gastric cancer patients to assist physicians in clinical diagnosis and treatment.

Authors

  • Liang Sun
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, 27599, USA.
  • Yongxin Lan
    College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Jian Sun
    Department Of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, Colorado, 80210, United States of America.
  • Pengfei Ji
    Department of Abdominal·Osteomalacia Radiotherapy, Cancer Hospital of Dalian University of Technology, Shenyang, China. Electronic address: peng_fei_ji@126.com.
  • Hongwei Ge
    School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China.
  • Ming Cui
    Department of Radiation Oncology Gastrointestinal and Urinary and Musculoskeletal Cancer, 74665Cancer Hospital of China Medical University, Shenyang, Liaoning, China.
  • Xin Yuan

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