Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data

TitleIndependent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data
Publication TypeJournal Article
Year of Publication2018
AuthorsGray, E, Marti, J, Brewster, DH, Wyatt, JC, Hall, PS
Corporate Authorsthe SATURNE Advisory Group
JournalBritish Journal of Cancer
Volume119
Issue7
Pagination808 - 814
Date Published09/2018
URLhttp://www.nature.com/articles/s41416-018-0256-x
DOI10.1038/s41416-018-0256-x
ISSN0007-0920
Accession NumberPMID:30220705
KeywordsBreast cancer
Abstract

Background

PREDICT is a widely used online prognostication and treatment benefit tool for patients with early stage breast cancer. The aim of this study was to conduct an independent validation exercise of the most up-to-date version of the PREDICT algorithm (version 2) using real-world outcomes from the Scottish population of women with breast cancer.

Methods

Patient data were obtained for all Scottish Cancer Registry (SCR) records with a diagnosis of primary invasive breast cancer diagnosed in the period between January 2001 and December 2015. Prognostic scores were calculated using the PREDICT version 2 algorithm. External validity was assessed by statistical analysis of discrimination and calibration. Discrimination was assessed by area under the receiver-operator curve (AUC). Calibration was assessed by comparing the predicted number of deaths to the observed number of deaths across relevant sub-groups.

Results

A total of 45,789 eligible cases were selected from 61,437 individual records. AUC statistics ranged from 0.74 to 0.77. Calibration results showed relatively close agreement between predicted and observed deaths. The 5-year complete follow-up sample reported some overestimation (11.5%), while the 10-year complete follow-up sample displayed more limited overestimation (1.7%).

Conclusions

Validation results suggest that the PREDICT tool remains essentially relevant for contemporary patients with early stage breast cancer

WOS ID (UT)

000447291900005

Short TitleBr J Cancer
Citation Key / SERVAL ID9259
Peer reviewRefereed
Groupe spécialisé: 

                         

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