Deep learning driven predictive treatment planning for adaptive radiotherapy of lung cancer.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Feb 18, 2022

Abstract

BACKGROUND AND PURPOSE: To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a predictive treatment planning paradigm, and improve the therapeutic ratio.

Authors

  • Donghoon Lee
    Department of Radiation Convergence Engineering, Research Institute of Health Science, Yonsei Univeristy, 1 Yonseidae-gil, Wonju, Gangwon, 26493, Korea.
  • Yu-Chi Hu
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Licheng Kuo
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Sadegh Alam
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Ellen Yorke
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.
  • Anyi Li
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Andreas Rimner
    Memorial Sloan Kettering Cancer Center, New York, New York.
  • Pengpeng Zhang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA. Electronic address: zhangp@mskcc.org.

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