A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.

Journal: Physics and imaging in radiation oncology
Published Date: Apr 1, 2025

Abstract

BACKGROUND AND PURPOSE: We aimed to develop a physics-informed deep learning model for beam dose prediction in intensity-modulated radiation therapy (IMRT) for patients with nasopharyngeal cancer.

Authors

  • Zihan Sun
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.
  • Yongheng Yan
    Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, Zhejiang, China. Electronic address: 1521011@zju.edu.cn.
  • Yuanhua Chen
    Radiation Oncology Department, 1st Affiliated Hospital of Medical School of Zhejiang University, Zhejiang, China.
  • Guorong Yao
    Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
  • Jiazhou Wang
    Department of radiation oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Weigang Hu
    Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Zhongjie Lu
    Radiation Oncology Department, 1st Affiliated Hospital of Medical School of Zhejiang University, Zhejiang, China.
  • SenXiang Yan
    Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, Zhejiang, China; Cancer Center, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310027, Zhejiang, China. Electronic address: yansenxiang@zju.edu.cn.

Keywords

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