Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

Journal: Practical radiation oncology
PMID: 37935308

Abstract

PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a multidimensional evaluation method to test pretrained AI-based automated contouring solutions across a network of clinics.

Authors

  • Dominic Maes
    Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington, USA.
  • Evan D H Gates
    Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, Texas, USA.
  • Juergen Meyer
    Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Radiation Oncology, University of Washington, Seattle, Washington.
  • John Kang
    Department of Radiation Oncology, University of Washington, Seattle, Washington, USA.
  • Bao-Ngoc Thi Nguyen
    Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington.
  • Myra Lavilla
    Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington.
  • Dustin Melancon
    Department of Radiation Oncology, University of Washington, Seattle, Washington.
  • Emily S Weg
    Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Radiation Oncology, University of Washington, Seattle, Washington.
  • Yolanda D Tseng
    Department of Radiation Oncology, University of Washington, Seattle, Washington; Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington.
  • Andrew Lim
  • Stephen R Bowen
    Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington, USA.

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