Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Jul 9, 2022

Abstract

PURPOSE: To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm.

Authors

  • Jihye Koo
    Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA; Department of Physics, University of South Florida, FL, USA. Electronic address: jihye.koo@moffitt.org.
  • Jimmy J Caudell
    Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA. Electronic address: jimmy.caudell@moffitt.org.
  • Kujtim Latifi
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Petr Jordan
    Varian Medical Systems, Inc., 3100 Hansen Way, Palo Alto, CA 94304, USA.
  • Sangyu Shen
    Varian Medical Systems, Palo Alto, CA, USA. Electronic address: sangyu.shen@varian.com.
  • Philip M Adamson
    Varian Medical Systems, Palo Alto, CA, USA. Electronic address: padamson@stanford.edu.
  • Eduardo G Moros
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Vladimir Feygelman
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.

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