Radiotherapy dose prediction using off-the-shelf segmentation networks: A feasibility study with GammaPod planning.

Journal: Medical physics
PMID: 40017352

Abstract

BACKGROUND: Radiotherapy requires precise, patient-specific treatment planning to achieve high-quality dose distributions that improve patient outcomes. Traditional manual planning is time-consuming and clinically impractical for performing necessary plan trade-off comparisons, including treatment modality selection, prescription dose settings, and organ at risk (OAR) constraints. A time-efficient dose prediction tool could accelerate the planning process by guiding clinical plan optimization and adjustments. While the deep convolutional neural networks (CNNs) are prominent in radiotherapy dose prediction tasks, most studies have attempted to customize network architectures for different diseases and treatment modalities.

Authors

  • Qingying Wang
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Mingli Chen
    Department of Gastroenterology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.
  • Mahdieh Kazemimoghadam
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
  • Zi Yang
  • Kangning Zhang
    Department of Radiation Oncology, Stanford University, Stanford, California, USA.
  • Xuejun Gu
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
  • Weiguo Lu
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.

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