Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial.

Journal: The Lancet. Digital health
PMID: 37087370

Abstract

BACKGROUND: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making.

Authors

  • Benjamin H Kann
    Artificial Intelligence in Medicine (AIM) Program, Harvard Medical School, Boston, Massachusetts, USA.
  • 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.
  • Dennis Bontempi
    Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Zezhong Ye
    Artificial Intelligence in Medicine (AIM) Program, Harvard Medical School, Boston, Massachusetts, USA.
  • Sanjay Aneja
    Yale University, New Haven, Connecticut.
  • Richard Bakst
    Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Hillary R Kelly
    Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Amy F Juliano
    Mass Eye and Ear, Mass General Hospital, Boston, MA, USA.
  • Sam Payabvash
    Department of Radiology, Yale School of Medicine, New Haven, CT.
  • Jeffrey P Guenette
    Division of Neuroradiology, Brigham & Women's Hospital and Dana Farber Cancer Institute, Boston, Massachusetts, USA.
  • Ravindra Uppaluri
    Department of Surgery/Otolaryngology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Boston, MA, USA.
  • Danielle N Margalit
    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.
  • Roy B Tishler
    Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Robert Haddad
    Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • 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.
  • Joaquin J Garcia
    Department of Pathology, Mayo Clinic, Rochester, MN, USA.
  • Yael Flamand
    Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, ECOG-ACRIN Biostatistics Center, Boston, MA, USA.
  • Rathan M Subramaniam
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Otago Medical School, University of Otago, New Zealand.
  • Barbara A Burtness
    Department of Medicine, Yale School of Medicine, New Haven, CT.
  • Robert L Ferris
    Department of Otolaryngology-Head and Neck Surgery, University of Pittsburgh Medical Center Pittsburgh, Pennsylvania.

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