A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

Coen Hurkmans; Jean-Emmanuel Bibault; Kristy K Brock; Wouter van Elmpt; Mary Feng; Clifton David Fuller; Barbara A Jereczek-Fossa; Stine Korreman; Guillaume Landry; Frederic Madesta; Chuck Mayo; Alan McWilliam; Filipe Moura; Ludvig P Muren; Issam El Naqa; Jan Seuntjens; Vincenzo Valentini; Michael Velec

doi:10.1016/j.radonc.2024.110345

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

Standard

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, Vol. 197, 08.2024, p. 110345.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Hurkmans, C, Bibault, J-E, Brock, KK, van Elmpt, W, Feng, M, David Fuller, C, Jereczek-Fossa, BA, Korreman, S, Landry, G, Madesta, F, Mayo, C, McWilliam, A, Moura, F, Muren, LP, El Naqa, I, Seuntjens, J, Valentini, V & Velec, M 2024, 'A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy', RADIOTHER ONCOL, vol. 197, pp. 110345. https://doi.org/10.1016/j.radonc.2024.110345

APA

Hurkmans, C., Bibault, J-E., Brock, K. K., van Elmpt, W., Feng, M., David Fuller, C., Jereczek-Fossa, B. A., Korreman, S., Landry, G., Madesta, F., Mayo, C., McWilliam, A., Moura, F., Muren, L. P., El Naqa, I., Seuntjens, J., Valentini, V., & Velec, M. (2024). A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy. RADIOTHER ONCOL, 197, 110345. https://doi.org/10.1016/j.radonc.2024.110345

Vancouver

Hurkmans C, Bibault J-E, Brock KK, van Elmpt W, Feng M, David Fuller C et al. A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy. RADIOTHER ONCOL. 2024 Aug;197:110345. https://doi.org/10.1016/j.radonc.2024.110345

Bibtex

@article{9eda71766de54c5bb735ced75e2bc5db,

title = "A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy",

abstract = "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.",

keywords = "Humans, Artificial Intelligence, Delphi Technique, Radiotherapy Planning, Computer-Assisted/standards, Radiation Oncology/standards, Radiotherapy/standards, Algorithms",

author = "Coen Hurkmans and Jean-Emmanuel Bibault and Brock, {Kristy K} and {van Elmpt}, Wouter and Mary Feng and {David Fuller}, Clifton and Jereczek-Fossa, {Barbara A} and Stine Korreman and Guillaume Landry and Frederic Madesta and Chuck Mayo and Alan McWilliam and Filipe Moura and Muren, {Ludvig P} and {El Naqa}, Issam and Jan Seuntjens and Vincenzo Valentini and Michael Velec",

year = "2024",

month = aug,

doi = "10.1016/j.radonc.2024.110345",

language = "English",

volume = "197",

pages = "110345",

journal = "RADIOTHER ONCOL",

issn = "0167-8140",

publisher = "Elsevier Ireland Ltd",

}

RIS

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

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 -