Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Apr 18, 2022

Abstract

PURPOSE/OBJECTIVE(S): Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively impacting treatment outcomes. The aim of this study is to evaluate the performance and efficiency of segmentation of CTVs in planning CT images of breast cancer patients using a 3D convolutional neural network (CNN) compared to the manual process.

Authors

  • P Buelens
    KU Leuven - University of Leuven, Department of Oncology, Experimental Radiation Oncology, Belgium; University Hospitals Leuven, Department of Radiation Oncology, Belgium.
  • S Willems
    KU Leuven, Dept. ESAT, Processing Speech and Images (PSI), & UZ Leuven, Medical Imaging Research Center, Belgium.
  • L Vandewinckele
    KU Leuven - University of Leuven, Department of Oncology, Experimental Radiation Oncology, Belgium; University Hospitals Leuven, Department of Radiation Oncology, Belgium.
  • W Crijns
    KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, Belgium.
  • F Maes
    KU Leuven, Dept. ESAT, Processing Speech and Images (PSI), & UZ Leuven, Medical Imaging Research Center, Belgium.
  • C G Weltens
    KU Leuven - University of Leuven, Department of Oncology, Experimental Radiation Oncology, Belgium; University Hospitals Leuven, Department of Radiation Oncology, Belgium. Electronic address: siri.willems@kuleuven.be.

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