Deep reinforcement learning in radiation therapy planning optimization: A comprehensive review.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Aug 19, 2024

Abstract

PURPOSE: The formulation and optimization of radiation therapy plans are complex and time-consuming processes that heavily rely on the expertise of medical physicists. Consequently, there is an urgent need for automated optimization methods. Recent advancements in reinforcement learning, particularly deep reinforcement learning (DRL), show great promise for automating radiotherapy planning. This review summarizes the current state of DRL applications in this field, evaluates their effectiveness, and identifies challenges and future directions.

Authors

  • Can Li
    Department of Chemical Engineering, Tsinghua University , Beijing 100084, China.
  • Yuqi Guo
    School of Social Work, University of Alabama, Tuscaloosa, AL, USA.
  • Xinyan Lin
    Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing, 100191, China; School of Physics, Beihang University, Beijing, 102206, China.
  • Xuezhen Feng
    Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing, 100191, China; School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China.
  • Dachuan Xu
    Institute of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, PR China. Electronic address: xudc@bjut.edu.cn.
  • Ruijie Yang
    Department of Radiation Oncology, Peking University Third Hospital, Beijing, China. Electronic address: ruijyang@yahoo.com.

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