Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.

Journal: Journal of applied clinical medical physics
PMID: 39545816

Abstract

BACKGROUND: Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, but development has primarily been on external beam radiation therapy. Thus, there is a need for translation to brachytherapy.

Authors

  • Suman Gautam
    Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA.
  • Alexander F I Osman
    Department of Radiation Oncology, American University of Beirut Medical Center, Riad El-Solh, 1107 2020, Beirut, Lebanon.
  • Dylan Richeson
    Department of Radiation Oncology, Inova Schar Cancer Institute, Fairfax, Virginia, USA.
  • Somayeh Gholami
    Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA.
  • Binod Manandhar
    Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA.
  • Sharmin Alam
    Department of Radiation Oncology, Baylor Scott and White Health, Temple, Texas, USA.
  • William Y Song
    Department of Physics, Ryerson University, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Canada.

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