An image-based deep learning framework for individualizing radiotherapy dose.

Journal: The Lancet. Digital health
Published Date: Jun 27, 2019

Abstract

BACKGROUND: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose.

Authors

  • Bin Lou
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.
  • Semihcan Doken
    2111 East 96th St/NE-6, Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH, 44195.
  • Tingliang Zhuang
    40 Liberty Blvd, Diagnostic Imaging Computed Tomography, Siemens Healthineers, Malvern, PA 19355.
  • Danielle Wingerter
    2111 East 96th St/NE-6, Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH, 44195.
  • Mishka Gidwani
    2111 East 96th St/NE-6, Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH, 44195.
  • Nilesh Mistry
    40 Liberty Blvd, Diagnostic Imaging Computed Tomography, Siemens Healthineers, Malvern, PA 19355.
  • Lance Ladic
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.
  • Ali Kamen
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.
  • Mohamed E Abazeed
    2111 East 96th St/NE-6, Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH, 44195.

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