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

Francisco J. Candido dos Reis, Gordon C. Wishart, Ed M. Dicks, David Greenberg, Jem Rashbass, Marjanka K. Schmidt, Alexandra J. van den Broek, Ian O. Ellis, Andrew Green, Emad Rakha, Tom Maishman, Diana M. Eccles, Paul D.P. Pharoah*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

96 Citations (Scopus)

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.

Original languageEnglish
Article number58
JournalBreast Cancer Research
Volume19
Issue number1
DOIs
Publication statusPublished - 22 May 2017

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2017 The Author(s).

Keywords

  • Breast cancer
  • Prognosis

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