Quality Assurance for AI-Based Applications in Radiation Therapy.

Journal: Seminars in radiation oncology
Published Date: Oct 1, 2022

Abstract

Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment. However, the QA for AI-based systems is an emerging area, which has not been intensively explored and requires interactive collaborations between medical doctors, medical physics experts, and commercial/research AI institutions. This article summarizes the current QA methodologies for AI modules of every subdomain in RT with further focus on persistent shortcomings and upcoming key challenges and perspectives.

Authors

  • Michaël Claessens
    Faculty of Medicine and Health Sciences, University of Antwerp, Belgium; Department of Radiation Oncology, Iridium Cancer Network, Wilrijk (Antwerp), Belgium. Electronic address: michael.claessens@uantwerpen.be.
  • Carmen Seller Oria
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Charlotte L Brouwer
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Benjamin P Ziemer
    Department of Radiation Oncology, University of California, San Francisco, CA.
  • Jessica E Scholey
    Department of Radiation Oncology, University of California, San Francisco, CA.
  • Hui Lin
    Department of Mechanical Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America.
  • Alon Witztum
    Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States.
  • Olivier Morin
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Issam El Naqa
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Wouter van Elmpt
    Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands. Electronic address: wouter.vanelmpt@maastro.nl.
  • Dirk Verellen
    Department of Radiotherapy, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Belgium.

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