Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.

Journal: Medical physics
Published Date: Apr 26, 2024

Abstract

BACKGROUND: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains.

Authors

  • William T Hrinivich
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
  • Mahasweta Bhattacharya
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
  • Lina Mekki
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
  • Todd McNutt
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Xun Jia
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75235.
  • Heng Li
    Department of Anesthesiology, Affiliated Nanhua Hospital, University of South China, Hengyang 421002, Hunan Province, China.
  • Daniel Y Song
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
  • Junghoon Lee
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.

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