Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.

Journal: JCO clinical cancer informatics
PMID: 36652661

Abstract

PURPOSE: We developed a deep neural network that queries the lung computed tomography-derived feature space to identify radiation sensitivity parameters that can predict treatment failures and hence guide the individualization of radiotherapy dose. In this article, we examine the transportability of this model across health systems.

Authors

  • James Randall
    Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois, USA.
  • P Troy Teo
    Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois, USA.
  • Bin Lou
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.
  • Jainil Shah
    Diagnostic Imaging Computed Tomography, Siemens Healthineers, Malvern, Pennsylvania, USA.
  • Jyoti Patel
    Division of Hematology/Oncology, Northwestern University, Chicago, IL.
  • 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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