Probert, J., Dodwell, D., Broggio, J. et al. (4 more authors) (2025) Identification of recurrences in women diagnosed with early invasive breast cancer using routinely collected data in England. BJC Reports, 3. 39. ISSN 2731-9377
Abstract
Background
Breast cancer is the commonest cancer in the UK, with around 55,000 women diagnosed annually. Information is routinely available on breast cancer mortality but not on recurrence.
Methods
We used a database compiled by the West Midlands Cancer Intelligence Unit during 1997–2011 to develop and train a deterministic algorithm to identify recurrences in routinely collected data (RCD) available within NHS England. We trained the algorithm further using 150 women with stage II-III breast cancer who were recruited into the AZURE trial during 2003–2006 and invited to approximately 24 clinic follow-up visits over ten years. We then evaluated its performance using data for the remaining 1930 women in England in the AZURE trial.
Results
The sensitivity of the RCD to detect distant recurrences recorded in the AZURE trial during the ten years following randomisation was 95.6% and its sensitivity to detect any recurrence was 96.6%. The corresponding specificities were 91.9% for distant recurrence and 77.7% for any recurrence.
Conclusions
These findings demonstrate the potential of routinely collected data to identify breast cancer recurrences in England. The algorithm may have a role in several settings and make long-term follow-up in randomised trials of breast cancer treatments more cost-effective.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2025. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > The Medical School (Sheffield) > Division of Genomic Medicine (Sheffield) > Department of Oncology and Metabolism (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jun 2025 15:04 |
Last Modified: | 04 Jun 2025 15:04 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s44276-025-00154-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:227323 |