Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer.

Journal: Radiation oncology (London, England)
PMID: 35101068

Abstract

BACKGROUND: The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients.

Authors

  • Maria Kawula
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
  • Dinu Purice
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
  • Minglun Li
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Gerome Vivar
  • Seyed-Ahmad Ahmadi
  • Katia Parodi
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Claus Belka
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Christopher Kurz
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.

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