Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer.

Journal: Medical physics
Published Date: Oct 1, 2023

Abstract

BACKGROUND: In cancer care, determining the most beneficial treatment technique is a key decision affecting the patient's survival and quality of life. Patient selection for proton therapy (PT) over conventional radiotherapy (XT) currently entails comparing manually generated treatment plans, which requires time and expertise.

Authors

  • Margerie Huet-Dastarac
    Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium.
  • Steven Michiels
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
  • Sara Teruel Rivas
    Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium.
  • Hamdiye Ozan
    Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium.
  • Edmond Sterpin
    Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), UCLouvain, Brussels, Belgium.
  • John A Lee
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
  • Ana Barragán-Montero
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium. Electronic address: ana.barragan@uclouvain.be.

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