Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 39299574

Abstract

BACKGROUND/PURPOSE: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions.

Authors

  • Kareem A Wahid
    UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Zaphanlene Y Kaffey
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • David P Farris
    Research Medical Library, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Laia Humbert-Vidan
    Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK.
  • Amy C Moreno
    Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Mathis Rasmussen
    Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark.
  • Jintao Ren
    Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
  • Mohamed A Naser
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. Electronic address: manaser@mdanderson.org.
  • Tucker J Netherton
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
  • Stine Korreman
    Danish Center for Particle Therapy & Department of Oncology, Aarhus University Hospital, Denmark.
  • Guha Balakrishnan
    Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.
  • Clifton D Fuller
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • David Fuentes
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States.
  • Michael J Dohopolski
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address: michael.dohopolski@utsouthwestern.edu.

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