Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk.

Journal: Clinical and translational radiation oncology
Published Date: Jul 1, 2025

Abstract

INTRODUCTION: Target volume delineation is crucial in breast cancer radiotherapy planning but involves significant interobserver variability. Deep learning (DL) models may reduce this variability, saving time and costs. However, current DL-models do not consider clinical data, such as tumor location and patient comorbidity, to adjust the target and reduce dose to organs at risk (OAR). This study compares clinically defined target volumes to those generated by a DL-model in terms of size, geometric overlap, and dose to OAR.

Authors

  • Vivi Tang
    Skåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, Sweden.
  • Elinore Wieslander
    Skåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, Sweden.
  • Mahnaz Haghanegi
    Skåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, Sweden.
  • Elisabeth Kjellén
    Skåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, Sweden.
  • Sara Alkner
    Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden.

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