Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Oct 21, 2021

Abstract

BACKGROUND AND PURPOSE: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a highly accurate automatic contouring model for the heart, cardiac chambers, and great vessels for RT planning computed tomography (CT) images that can be used for dose-volume parameter estimation.

Authors

  • Miguel Garrett Fernandes
    Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: miguel.fernandes@radboudumc.nl.
  • Johan Bussink
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Barbara Stam
    Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Robin Wijsman
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Dominic A X Schinagl
    Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
  • RenĂ© Monshouwer
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Jonas Teuwen
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.

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