Optimizing fractionation schedules for de-escalation radiotherapy in head and neck cancers using deep reinforcement learning.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Mar 14, 2025

Abstract

PURPOSE: Patients with locally-advanced head and neck squamous cell carcinomas (HNSCCs), particularly those related to human papillomavirus (HPV), often achieve good locoregional control (LRC), yet they suffer significant toxicities from standard chemoradiotherapy. This study aims to optimize the daily dose fractionation based on individual responses to radiotherapy (RT), minimizing toxicity while maintaining a low risk of LRC failure.

Authors

  • 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.
  • Xin Sun
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA.
  • Yuanhua Chen
    Radiation Oncology Department, 1st Affiliated Hospital of Medical School of Zhejiang University, Zhejiang, China.
  • Zihan Sun
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, 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.
  • Zhongjie Lu
    Radiation Oncology Department, 1st Affiliated Hospital of Medical School of Zhejiang University, Zhejiang, China.
  • Feng Zhao
    Department of Blood Transfusion, The First Affiliated Hospital of Ningbo University, Ningbo, China.

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