A deep-learning-based prediction model for the biodistribution of Y microspheres in liver radioembolization.

Journal: Medical physics
PMID: 34628667

Abstract

BACKGROUND: Radioembolization with Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient, and tissue characteristics.

Authors

  • Dimitris Plachouris
    3DMI Research Group, Department of Medical Physics, University of Patras, Rion GR 265 04, Greece.
  • Ioannis Tzolas
    School of Electrical and Computer Engineering, University of Patras, Rion, Greece.
  • Ilias Gatos
    Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece.
  • Panagiotis Papadimitroulas
  • Trifon Spyridonidis
  • Dimitris Apostolopoulos
    Department of Nuclear Medicine, School of Medicine, University of Patras, Rion, Greece.
  • Nikolaos Papathanasiou
    Department of Nuclear Medicine, School of Medicine, University of Patras, Rion, Greece.
  • Dimitris Visvikis
    LaTIM, INSERM, UMR 1101, Brest 29609, France.
  • Kerasia-Maria Plachouri
    Department of Dermatology, School of Medicine, University of Patras, Rion, Greece.
  • John D Hazle
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
  • George C Kagadis
    Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece and Department of Imaging Physics, The University of  Texas MD Anderson Cancer Center, Houston, Texas 77030.

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