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A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy
Open accessAbstract
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.
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“Acceptance testing of AI auto contouring algorithms typically involves both area‐based metrics (e.g., Dice Similarity Coefficient, Intersection over Union (IoU)) and distance‐based metrics (e.g., Hausdorff Distance) evaluated against reference standard contours. 11 , 12 , 13 , 14 These metrics are well established for assessing contour agreement, but they say nothing about where the model is confi”
From Uncertainty quantification of U‐Net based segmentation tool using conformal prediction (2026) · cites this paper as [11]
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Metadata
- Authors
- 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.
- Affiliations
- Catharina Ziekenhuis (NL)
- Hôpital Européen Georges-Pompidou (FR)
- The University of Texas MD Anderson Cancer Center (US)
- Maastricht University Medical Centre (NL)
- University of California, San Francisco (US)
- Aarhus University (DK)
- LMU Klinikum (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- University of Michigan (US)
- University of Manchester (GB)
- University Health Network (CA)
- Università Cattolica del Sacro Cuore (IT)
- University of Toronto (CA)
- Corresponding author
- Hurkmans, Coen
- ORCID
- Venue
- Radiotherapy and Oncology
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 197
- Pages
- 110345
- ISSN
- 0167-8140 (ISSN-L)
- OpenAlex
- W4399327635
- Cited by
- 110
- References
- 85
- Citations by year
- 2026: 56 · 2025: 46 · 2024: 7
- FWCI
- 10.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Radiotherapy and Oncology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, cc-by)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, cc-by)
- Funders
- European SocieTy for Radiotherapy and Oncology
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence, radiotherapy, treatment planning, adaptive radiotherapy, image segmentation, patient-specific quality assurance, outcome prediction, AI ethics, clinical adoption of AI, Delphi consensus
- MeSH terms
- Algorithms, Artificial Intelligence, Delphi Technique, Humans, Radiotherapy, Radiotherapy Planning, Computer-Assisted, Radiation Oncology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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