Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer.

Journal: JAMA network open
Published Date: Aug 1, 2023

Abstract

IMPORTANCE: Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use.

Authors

  • Zezhong Ye
    Artificial Intelligence in Medicine (AIM) Program, Harvard Medical School, Boston, Massachusetts, USA.
  • Anurag Saraf
    Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.
  • Yashwanth Ravipati
    UCLA Computational Diagnostics, University of California Los Angeles, Los Angeles, CA, USA.
  • Frank Hoebers
    Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
  • Paul J Catalano
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Yining Zha
    Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
  • Anna Zapaishchykova
    Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
  • Jirapat Likitlersuang
    Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Mass General Brigham Artificial Intelligence in Medicine Program, Boston, MA, USA.
  • Christian Guthier
    Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Roy B Tishler
    Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Jonathan D Schoenfeld
    Department of Radiation Oncology, Brigham & Women's Hospital and Dana Farber Cancer Institute, Boston, Massachusetts, USA.
  • Danielle N Margalit
    Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Robert I Haddad
    Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.
  • Raymond H Mak
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Mohamed Naser
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Kareem A Wahid
    UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Jaakko Sahlsten
    Dept. of Computer Science, Aalto University School of Science, Espoo, 00076, Finland.
  • Joel Jaskari
    Dept. of Computer Science, Aalto University School of Science, Espoo, 00076, Finland.
  • Kimmo Kaski
    Dept. of Computer Science, Aalto University School of Science, Espoo, 00076, Finland. kimmo.kaski@aalto.fi.
  • Antti A Mäkitie
    Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Clifton D Fuller
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Hugo J W L Aerts
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Benjamin H Kann
    Artificial Intelligence in Medicine (AIM) Program, Harvard Medical School, Boston, Massachusetts, USA.

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