A Joint Estro and Aapm Guideline for Development, Clinical Validation and Reporting of Artificial Intelligence Models in Radiation Therapy

Authors

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

Language

English

Publication Date

8-1-2024

Journal

Radiotherapy & Oncology

DOI

10.1016/j.radonc.2024.110345

PMID

38838989

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, Delphi Technique, Radiotherapy Planning, Computer-Assisted, Radiation Oncology, Radiotherapy, Algorithms, Artificial Intelligence, Deep learning, Ethics, Guideline, Machine Learning, Quality Assurance, Radiation Therapy, Segmentation, Treatment planning

Published Open-Access

yes

Recommended Citation

Hurkmans, Coen; Bibault, Jean-Emmanuel; Brock, Kristy K; et al., "A Joint Estro and Aapm Guideline for Development, Clinical Validation and Reporting of Artificial Intelligence Models in Radiation Therapy" (2024). Faculty, Staff and Student Publications. 5377.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5377