A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy
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A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy. / Hurkmans, Coen; Bibault, Jean-Emmanuel; Brock, Kristy K; van Elmpt, Wouter; Feng, Mary; David Fuller, Clifton; Jereczek-Fossa, Barbara A; Korreman, Stine; Landry, Guillaume; Madesta, Frederic; Mayo, Chuck; McWilliam, Alan; Moura, Filipe; Muren, Ludvig P; El Naqa, Issam; Seuntjens, Jan; Valentini, Vincenzo; Velec, Michael.
in: RADIOTHER ONCOL, Jahrgang 197, 08.2024, S. 110345.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy
AU - Hurkmans, Coen
AU - Bibault, Jean-Emmanuel
AU - Brock, Kristy K
AU - van Elmpt, Wouter
AU - Feng, Mary
AU - David Fuller, Clifton
AU - Jereczek-Fossa, Barbara A
AU - Korreman, Stine
AU - Landry, Guillaume
AU - Madesta, Frederic
AU - Mayo, Chuck
AU - McWilliam, Alan
AU - Moura, Filipe
AU - Muren, Ludvig P
AU - El Naqa, Issam
AU - Seuntjens, Jan
AU - Valentini, Vincenzo
AU - Velec, Michael
N1 - Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.
PY - 2024/8
Y1 - 2024/8
N2 - BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.
AB - BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.
KW - Humans
KW - Artificial Intelligence
KW - Delphi Technique
KW - Radiotherapy Planning, Computer-Assisted/standards
KW - Radiation Oncology/standards
KW - Radiotherapy/standards
KW - Algorithms
U2 - 10.1016/j.radonc.2024.110345
DO - 10.1016/j.radonc.2024.110345
M3 - SCORING: Journal article
C2 - 38838989
VL - 197
SP - 110345
JO - RADIOTHER ONCOL
JF - RADIOTHER ONCOL
SN - 0167-8140
ER -