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Breast Cancer Res. 2015 Dec 1;17(1):147. doi: 10.1186/s13058-015-0653-5.

Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort.

Author information

1
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, EC1M 6BQ, UK. a.brentnall@qmul.ac.uk.
2
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. elaine.f.harkness@manchester.ac.uk.
3
Centre for Imaging Sciences, Institute for Population Health, University of Manchester, Manchester, UK. elaine.f.harkness@manchester.ac.uk.
4
Manchester Academic Health Science Centre, University of Manchester, Manchester, UK. elaine.f.harkness@manchester.ac.uk.
5
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. sue.astley@manchester.ac.uk.
6
Centre for Imaging Sciences, Institute for Population Health, University of Manchester, Manchester, UK. sue.astley@manchester.ac.uk.
7
Manchester Academic Health Science Centre, University of Manchester, Manchester, UK. sue.astley@manchester.ac.uk.
8
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. louise.gorman@uhsm.nhs.uk.
9
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. paula.stavrinos@uhsm.nhs.uk.
10
Manchester Academic Health Science Centre, University of Manchester, Manchester, UK. paula.stavrinos@uhsm.nhs.uk.
11
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. sarah.sampson@uhsm.nhs.uk.
12
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. lynne.fox@uhsm.nhs.uk.
13
Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK. jamie.sergeant@manchester.ac.uk.
14
National Institute for Health Research (NIHR) Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK. jamie.sergeant@manchester.ac.uk.
15
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. michelle.harvie@manchester.ac.uk.
16
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. mary.wilson@uhsm.nhs.uk.
17
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. ursula.beetles@uhsm.nhs.uk.
18
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. soujanya.gadde@uhsm.nhs.uk.
19
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. yit.lim@uhsm.nhs.uk.
20
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. anil.jain@uhsm.nhs.uk.
21
Manchester Academic Health Science Centre, University of Manchester, Manchester, UK. anil.jain@uhsm.nhs.uk.
22
Institute of Cancer Sciences, University of Manchester, Manchester, UK. anil.jain@uhsm.nhs.uk.
23
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. sara.bundred@uhsm.nhs.uk.
24
Education and Research Centre, University Hospital of South Manchester, Manchester, UK. nicky.barr@uhsm.nhs.uk.
25
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. valerie.reece@uhsm.nhs.uk.
26
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. tony.howell@manchester.ac.uk.
27
The Christie NHS Foundation Trust, Manchester, UK. tony.howell@manchester.ac.uk.
28
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, EC1M 6BQ, UK. j.cuzick@qmul.ac.uk.
29
Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK. gareth.evans@cmft.nhs.uk.
30
The Christie NHS Foundation Trust, Manchester, UK. gareth.evans@cmft.nhs.uk.
31
Institute of Human development, Genomic Medicine, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK. gareth.evans@cmft.nhs.uk.

Abstract

INTRODUCTION:

The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model).

METHODS:

Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression.

RESULTS:

The analysis included 50,628 women aged 47-73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34-1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25-1.48), O/E 60 % (95 % CI 44-74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CI 1.12-1.33), O/E 46 % (95 % CI 26-65 %), AUC 0.55]. It continued to add information after allowing for Tyrer-Cuzick [IQR-OR 1.47 (95 % CI 1.33-1.62), combined AUC 0.61] or Gail [IQR-OR 1.45 (95 % CI 1.32-1.60), combined AUC 0.59].

CONCLUSIONS:

Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model.

PMID:
26627479
PMCID:
PMC4665886
DOI:
10.1186/s13058-015-0653-5
[Indexed for MEDLINE]
Free PMC Article

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