Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution.

Journal: Radiation oncology (London, England)
Published Date: May 19, 2025

Abstract

PURPOSE: Accurate pre-treatment dose prediction is essential for efficient radiotherapy planning. Although deep learning models have advanced automated dose distribution, comprehensive multi-tumor analyses remain scarce. This study assesses deep learning models for dose prediction across diverse tumor types, combining objective and subjective evaluation methods.

Authors

  • Zhen Hou
    Institute of Medical Information & Library, Chinese Academy of Medical Sciences, Beijing, China.
  • Lang Qin
    Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China.
  • Jiabing Gu
    School of Medicine and Life Sciences, University of Jinan Shandong Academy of Medical Sciences, Jinan, Shandong, China.
  • Zidong Liu
    The Laboratory of Image Science and Technology, The School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210096, China.
  • Juan Liu
    Key State Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, PR China. Electronic address: liujuan@whu.edu.cn.
  • Yuan Zhang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Shanbao Gao
    The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210000, Jiangsu, China.
  • Jian Zhu
  • Shuangshuang Li
    Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China. Electronic address: lishuangshuang2016@email.szu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals