Generalizability of deep learning in organ-at-risk segmentation: A transfer learning study in cervical brachytherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 38763356

Abstract

PURPOSE: Deep learning can automate delineation in radiation therapy, reducing time and variability. Yet, its efficacy varies across different institutions, scanners, or settings, emphasizing the need for adaptable and robust models in clinical environments. Our study demonstrates the effectiveness of the transfer learning (TL) approach in enhancing the generalizability of deep learning models for auto-segmentation of organs-at-risk (OARs) in cervical brachytherapy.

Authors

  • Ruiyan Ni
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
  • Kathy Han
    Princess Margaret Cancer Center, University Health Network, Toronto, CA, Canada; Department of Radiation Oncology, University of Toronto, Toronto, CA, Canada.
  • Benjamin Haibe-Kains
    Princess Margaret Cancer Centre, University Health Network, Canada, Toronto, ON, Canada.
  • Alexandra Rink
    Department of Radiation Physics, Princess Margaret Cancer Centre, ON, M5G 2M9, Canada.

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