Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Sep 10, 2020

Abstract

Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.

Authors

  • Liesbeth Vandewinckele
    Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium. Electronic address: liesbeth.vandewinckele@uzleuven.be.
  • 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.
  • Anna Dinkla
    Department of Radiation Oncology, Amsterdam University Medical Center, University of Amsterdam, The Netherlands. Electronic address: a.dinkla@amsterdamumc.nl.
  • Charlotte Brouwer
    University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands. Electronic address: c.l.brouwer@umcg.nl.
  • Wouter Crijns
    Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium. Electronic address: wouter.crijns@uzleuven.be.
  • Dirk Verellen
    Department of Radiotherapy, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Belgium.
  • Wouter van Elmpt
    Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands. Electronic address: wouter.vanelmpt@maastro.nl.

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