Evaluation of AI-based auto-contouring tools in radiotherapy: A single-institution study.

Journal: Journal of applied clinical medical physics
Published Date: Jan 21, 2025

Abstract

BACKGROUND: Accurate delineation of organs at risk (OARs) is crucial yet time-consuming in the radiotherapy treatment planning workflow. Modern artificial intelligence (AI) technologies had made automation of OAR contouring feasible. This report details a single institution's experience in evaluating two commercial auto-contouring software tools and making well-informed decisions about their clinical adoption.

Authors

  • Tingyu Wang
  • James Tam
    Department of Radiation Oncology, The Mount Sinai Hospital, New York, New York, USA.
  • Thomas Chum
    Department of Radiation Oncology, The Mount Sinai Hospital, New York, New York, USA.
  • Cyril Tai
    Department of Radiation Oncology, Mount Sinai Union Square, New York, New York, USA.
  • Deborah C Marshall
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Michael Buckstein
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Jerry Liu
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Sheryl Green
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Robert D Stewart
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Tian Liu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Ming Chao
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

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