Mackay, K., Banfill, K., Bernstein, D. et al. (14 more authors) (2026) Royal College of Radiologists Guidance Statements on the Use of Auto-contouring in Radiotherapy. Clinical Oncology, 50. 104004. ISSN: 0936-6555
Abstract
Auto-contouring systems are rapidly becoming more widely used for radiotherapy treatment planning. There is an acknowledged need for formal guidance to help healthcare professionals understand how to safely adopt this technology. The Royal College of Radiologists Artificial Intelligence in Clinical Oncology working group established a multi-disciplinary group of national experts in artificial intelligence and radiotherapy quality assurance (QA). This group has produced consensus recommendations for the safe use of the technology. These include model selection, clinical commissioning, day-to-day QA, and post-implementation monitoring. Other factors such as the impact on the multi-disciplinary team, education, and training are also considered.
The healthcare professional approving auto-contours for use will have overall responsibility, and it is therefore of utmost importance that they have a good understanding of the risks of auto-contouring and how contours should be assessed to mitigate these risks. This guidance aims to enable healthcare professionals acting as operators of a medical device to understand what they need to know about auto-contouring, to facilitate safe adoption of this technology.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Keywords: | Artificial intelligence; auto-contouring; auto-segmentation; deep learning; guidelines; machine learning; radiotherapy |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) |
| Date Deposited: | 19 Mar 2026 08:49 |
| Last Modified: | 19 Mar 2026 16:32 |
| Published Version: | https://www.sciencedirect.com/science/article/pii/... |
| Status: | Published |
| Publisher: | Elsevier |
| Identification Number: | 10.1016/j.clon.2025.104004 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:238963 |

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