Deep reinforcement learning for automated radiation adaptation in lung cancer.

Journal: Medical physics
Published Date: Nov 14, 2017

Abstract

PURPOSE: To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2).

Authors

  • Huan-Hsin Tseng
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Sunan Cui
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Jen-Tzung Chien
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Randall K Ten Haken
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Issam El Naqa
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.

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