Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy.

Journal: Acta oncologica (Stockholm, Sweden)
Published Date:

Abstract

BACKGROUND: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically implemented in-house trained DL segmentation model for breast cancer to a previously performed pilot study to assess possible differences in performance or acceptability.

Authors

  • Nienke Bakx
    Department of Radiation Oncology, Catharina Hospital, Eindhoven, The Netherlands.
  • Maurice van der Sangen
    Department of Radiation Oncology, Catharina Hospital, Eindhoven, The Netherlands.
  • Jacqueline Theuws
    Department of Radiation Oncology, Catharina Hospital, Eindhoven, The Netherlands.
  • Johanna Bluemink
    Catharina Hospital, Department of Radiation Oncology, Eindhoven, The Netherlands.
  • Coen Hurkmans
    Department of Radiation Oncology, Catharina Hospital, Eindhoven, The Netherlands. coen.hurkmans@catharinaziekenhuis.nl.