- Poster presentation
- Open Access
A review of the online prognositc model predict using the POSH cohort (women aged ≤40 years at breast cancer diagnosis)
© Maishman et al. 2015
- Published: 16 November 2015
- Breast Cancer
- Young Woman
- Large Cohort
- Common Cancer
- Early Breast Cancer
Breast cancer is the most common cancer women in the UK, with approximately 50,000 new cases each year. PREDICT (http://www.predict.nhs.uk) is an online prognostic tool developed to help determine the best available treatment and long-term outcome for early breast cancer. This study was conducted to establish how well PREDICT performs in estimating survival in a large cohort of younger women (aged ≤40 years) recruited to the POSH study.
The UK POSH cohort includes data from 3000 women aged ≤40 years at breast cancer diagnosis. Study endpoints were overall- and breast cancer specific-survival at 5-,8-, and 10-years. Evaluation of PREDICT included model discrimination and comparison of the number of predicted versus observed events.
PREDICT provided accurate long term (8- and 10-year) survival estimates for younger women. However, short term (5-year) estimates were less accurate, with the tool overestimating survival by 25%, and by 56% for patients with ER positive tumours. PREDICT also underestimated survival at 5-years for patients with ER negative tumours.
PREDICT is a user-friendly and reliable tool for providing accurate long-term survival estimates for younger women with breast cancer. However, the model requires further calibration for more accurate short-term estimates. Prediction in the short-term may be most relevant for the increasing number of women considering risk-reducing bilateral mastectomy.
Funding for data collection/analysis of the POSH study by CRUK (grants A7572, A11699, C1275/A15956). Study sponsored by UHS NHS Foundation Trust. We also thank the NIHR NCRN for supporting patient recruitment and all participating patients.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.