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An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

Published version
Peer-reviewed

Change log

Authors

Candido dos Reis, FJ 
Wishart, GC 
Dicks, EM 
Greenberg, D 
Rashbass, J 

Abstract

Background

PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in ‘step’ changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Methods

Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT.

Results

In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease.

The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. Conclusions

The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

Description

Keywords

Breast cancer, Prognosis

Journal Title

Breast Cancer Research

Conference Name

Journal ISSN

1465-5411
1465-542X

Volume Title

19

Publisher

BioMed Central
Sponsorship
The BCOS was funded by the Netherlands Cancer Institute (NKI2007-3839). Funding for the POSH study was provided by Cancer Research UK (C1275/A9896, C1275/A11699, and C1275/A15956) and Breast Cancer Now (2005Nov63). PDPP is supported by the National Institute for Health Research Biomedical Research Centre at the University of Cambridge.