Feasibility of automated planning for whole-brain radiation therapy using deep learning.

Journal: Journal of applied clinical medical physics
Published Date: Dec 19, 2020

Abstract

PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes.

Authors

  • Jesang Yu
    Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.
  • Youngmoon Goh
    Department of Radiation Oncology, Asan Medical Center, Seoul, South Korea.
  • Kye Jin Song
    Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.
  • Jungwon Kwak
    Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
  • Byungchul Cho
    Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
  • Su San Kim
    Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.
  • Sang-Wook Lee
    Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Eun Kyung Choi
    Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.

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