Evidence map›Paper›PMID 28532503›Full record

ArticleBreast cancer research : BCR2017

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 and 3 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research : BCR, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04985253 (Correlation of Predictive Accuracy of PREDICT Version 2.2,), which is not on this map. Cited by 138 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
138citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04985253 unknown statusnot on this mapstarted 2018, after this paper: background citation

Correlation of Predictive Accuracy of PREDICT Version 2.2, (PREDICT V2.2) on a Retrospective Cohort of Indian Women With Operable Breast Cancer (OBC)

TypeobservationalSponsorTata Memorial CentreRan2018 to 2022Enrolled2,780ConditionsOperable Breast NeoplasmsArmsprediction of overall survival using Web based PREDICTV2.0 portal
3 · Its place in the literature

Who cites it

138 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  7. Personalized Decision Making on Genomic Testing in Early Breast Cancer: Expanding the MINDACT Trial with Decision-Analytic Modeling.Medical decision making : an international journal of the Society for Medical Decision Making · 2021
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78 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Francisco J Candido Dos ReisDepartment of Gynaecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Sao Paulo, Brazil.
Gordon C WishartFaculty of Medical Science, Anglia Ruskin University, Cambridge, UK.
Ed M DicksDepartment of Oncology, University of Cambridge, Cambridge, UK.
David GreenbergNational Cancer Registration and Analysis Service, Public Health England, London, UK.
Jem RashbassNational Cancer Registration and Analysis Service, Public Health England, London, UK.
Marjanka K SchmidtDivision of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Alexandra J van den BroekDivision of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Ian O EllisDivision of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, UK.
Andrew GreenDivision of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, UK.
Emad RakhaDivision of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, UK.
Tom MaishmanCancer Sciences Academic Unit and Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton and University Hospital Southampton Foundation Trust, Southampton, UK.
Diana M EcclesCancer Sciences Academic Unit and Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton and University Hospital Southampton Foundation Trust, Southampton, UK.
Paul D P PharoahDepartment of Oncology, University of Cambridge, Cambridge, UK. pp10001@medschl.cam.ac.uk.

Funding

Cancer Research UK 10119Cancer Research UK 15956Cancer Research UK 16561Cancer Research UK 19187Cancer Research UK C1275/A11699Cancer Research UK C1275/A15956Cancer Research UK C1275/A9896
6 · The paper itself

Abstract

backgroundPREDICT 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.

methodsMultivariable 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.

resultsIn 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.

conclusionsThe 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.

Indexed as

PrognosisAdultBreastBreast NeoplasmsEstrogen Receptor alphaFemaleHumansProportional Hazards ModelsEstrogen Receptor alphaBreast cancerPrognosis

Identifiers

PMID28532503
PMCPMC5440946

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.