Improving 3D dose prediction for breast radiotherapy using novel glowing masks and gradient-weighted loss functions.

Journal: Medical physics
Published Date: Aug 1, 2024

Abstract

BACKGROUND: The quality of treatment plans for breast cancer can vary greatly. This variation could be reduced by using dose prediction to automate treatment planning. Our work investigates novel methods for training deep-learning models that are capable of producing high-quality dose predictions for breast cancer treatment planning.

Authors

  • Lance C Moore
    Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.
  • Fatemeh Nematollahi
    Radiation Medicine and Applied Sciences, University of California, La Jolla, San Diego, California, USA.
  • Lingyi Li
    Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.
  • Sandra M Meyers
    Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA.
  • Kelly Kisling
    Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.

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