Criteria-calibration approaches to deep learning-based cervical cancer radiation treatment auto-planning.

Journal: Radiation oncology (London, England)
Published Date: Aug 20, 2025

Abstract

BACKGROUND: Knowledge-Based Planning (KBP) pipelines, which integrate machine learning-based models to predict dose distribution, have gained popularity in clinical radiation therapy. However, for patients with specific requirements, the trained models may struggle to rapidly adjust to guide the automatic planning process. Therefore, the aim of this study was to calibrate the dose prediction model to improve the quality and accuracy of automatic planning for cervical cancer radiation therapy.

Authors

  • Yongguang Liang
    Department of Radiotherapy, Peking Union Medical College Hospital, Beijing, 100730, China.
  • Jingru Yang
    Department of Radiotherapy, Peking Union Medical College Hospital, Beijing, 100730, China.
  • Shuoyang Wei
    Department of Engineering Physics, Tsinghua University, Beijing, China.
  • Yanfei Liu
    Cardiovascular Disease Centre, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China; Graduate School, Beijing University of Chinese Medicine, Beijing, 100029, China.
  • Shumeng He
    Intelligent Radiation Treatment Laboratory, United Imaging Research Institute of Intelligent Imaging, Beijing, China.
  • Kang Zhang
    Xifeng District People's Hospital, Qingyang, China.
  • Jie Qiu
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. Electronic address: qiujie@pumch.cn.
  • Bo Yang
    Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.

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