| Title: |
Identification of recurrences in women diagnosed with early invasive breast cancer using routinely collected data in England |
| Authors: |
Probert, J; Dodwell, D; Broggio, J; Coleman, R; Marshall, H; Darby, SC; Mannu, GS |
| Publisher Information: |
Springer Nature [academic journals on nature.com] |
| Publication Year: |
2025 |
| Collection: |
Oxford University Research Archive (ORA) |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1038/s44276-025-00154-1 |
| Availability: |
https://doi.org/10.1038/s44276-025-00154-1; https://ora.ox.ac.uk/objects/uuid:5eefe2da-27ea-405f-92c3-eb603872c809 |
| Rights: |
info:eu-repo/semantics/openAccess ; CC Attribution (CC BY) |
| Accession Number: |
edsbas.295F576F |
| Database: |
BASE |