A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.

Journal: BMC medical imaging
Published Date: Jul 30, 2025

Abstract

OBJECTIVES: Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume primary (GTVp) channel and parotid glands (PGs) channel.

Authors

  • Lin Lin
    Central Laboratory, The First Affiliated Hospital of Xiamen University, Xiamen, China, zhibinli33@163.com, liusuhuan@xmu.edu.cn.
  • Yuchen Ren
    The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, China.
  • Wanwei Jian
    School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Geng Yang
  • Bailin Zhang
    The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, China.
  • Lin Zhu
    Institute of Environmental Technology, College of Environmental and Resource Sciences; Zhejiang University, Hangzhou 310058, China.
  • Wenhao Zhao
    Department of Spine Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Haoyu Meng
    The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, China.
  • Xuetao Wang
    The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120 People's Republic of China.
  • Qiang He
    College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China.

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