An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation
An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation
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.
Candido dos Reis, Francisco J.
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Wishart, Gordon C.
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Dicks, Ed M.
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Greenberg, David
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Rashbass, Jem
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Schmidt, Marjanka K.
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van den Broek, Alexandra J.
ba4b6be8-1e23-4cf6-8f4b-09463b46e7b2
Ellis, Ian O.
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Green, Andrew
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Rakha, Emad
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Maishman, Tom
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Eccles, Diana M.
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Pharoah, Paul D.P.
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December 2017
Candido dos Reis, Francisco J.
047ffdc4-a750-4161-94f4-60e48a4ed551
Wishart, Gordon C.
f66cd4e0-e16f-4a97-b629-d11f4166d509
Dicks, Ed M.
55ddcce8-ca47-4ea1-8de4-74148f77de6d
Greenberg, David
6e7be180-8552-458c-b84d-d191a353993a
Rashbass, Jem
5fe51eb2-4850-4b4c-9be9-9143daf32005
Schmidt, Marjanka K.
68f4851a-9b14-48a9-b08f-854618465310
van den Broek, Alexandra J.
ba4b6be8-1e23-4cf6-8f4b-09463b46e7b2
Ellis, Ian O.
1410252f-5e8a-4e20-96f0-138c7049a22a
Green, Andrew
303649c7-75e1-4bc7-8b5e-6b672bb3a1cf
Rakha, Emad
a417febe-729b-470c-be98-550a1a35ecfb
Maishman, Tom
cf4259a4-0eef-4975-9c9d-a2c3d594f989
Eccles, Diana M.
5b59bc73-11c9-4cf0-a9d5-7a8e523eee23
Pharoah, Paul D.P.
c8fe0784-f4ab-4bf0-bd99-787a205a58cb
Candido dos Reis, Francisco J., Wishart, Gordon C., Dicks, Ed M., Greenberg, David, Rashbass, Jem, Schmidt, Marjanka K., van den Broek, Alexandra J., Ellis, Ian O., Green, Andrew, Rakha, Emad, Maishman, Tom, Eccles, Diana M. and Pharoah, Paul D.P.
(2017)
An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.
Breast Cancer Research, 19 (1), [58].
(doi:10.1186/s13058-017-0852-3).
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.
Text
(Candido Dos Reis et al, 2017) An updated PREDICT breast cancer...
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Accepted/In Press date: 4 May 2017
e-pub ahead of print date: 22 May 2017
Published date: December 2017
Organisations:
Cancer Sciences, Clinical Trials Unit
Identifiers
Local EPrints ID: 410778
URI: http://eprints.soton.ac.uk/id/eprint/410778
PURE UUID: d6ab8034-03c2-4a38-b869-1408fcc359ad
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Date deposited: 09 Jun 2017 09:38
Last modified: 16 Mar 2024 02:39
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Contributors
Author:
Francisco J. Candido dos Reis
Author:
Gordon C. Wishart
Author:
Ed M. Dicks
Author:
David Greenberg
Author:
Jem Rashbass
Author:
Marjanka K. Schmidt
Author:
Alexandra J. van den Broek
Author:
Ian O. Ellis
Author:
Andrew Green
Author:
Emad Rakha
Author:
Tom Maishman
Author:
Paul D.P. Pharoah
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