Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm.

Journal: Medical physics
Published Date: Sep 6, 2019

Abstract

PURPOSE: To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART).

Authors

  • Chuang Wang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Andreas Rimner
    Memorial Sloan Kettering Cancer Center, New York, New York.
  • 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.
  • Neelam Tyagi
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Jue Jiang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.
  • Ellen Yorke
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.
  • Sadegh Riyahi
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.
  • Gig Mageras
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Joseph O Deasy
    Department of Medical Physics, 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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