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, 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

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.

Keywords: Breast cancer; Prognosis.

Figures

Fig. 1
Fig. 1
Web access to the online version of PREDICT at www.predict.nhs.uk, January 2011–October 2016. a Access per month. b Access by city. Source: Google Analytics (Mountain View, CA, USA)
Fig. 2
Fig. 2
Breast cancer-specific mortality HR functions for age, tumour size and number of positive nodes derived from the model development data. ER-negative is indicated by red lines; ER-positive is indicated by blue lines. ER Oestrogen receptor
Fig. 3
Fig. 3
Age-specific HR for non-breast cancer mortality derived from the model development data. ER-negative is indicated by red lines; ER-positive is indicated by blue lines. ER Oestrogen receptor
Fig. 4
Fig. 4
Observed and predicted breast cancer deaths at 10 years by quintile of predicted risk. a Model development data. b Validation data. ER-negative is indicated by red lines; ER-positive is indicated by blue lines. ER Oestrogen receptor

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Source: PubMed

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