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

Adam R Brentnall,Valerie Reece,Ursula Beetles,Sara Bundred,Susan M Astley,Sarah Sampson,Soujanya Gadde,Mary Wilson,Anthony Howell,Jamie C Sergeant,Jack Cuzick,Louise S Donnelly,Lynne Fox,Paula Stavrinos,Elaine Harkness ,Nicky Barr ,Michelle Harvie ,Anil K Jain ,Yit Yoong Lim ,D Gareth Evans

doi:10.1186/s13058-015-0653-5

Abstract

IntroductionThe 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).MethodsMammographic 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.ResultsThe 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].ConclusionsBreast density may be usefully combined with the Tyrer-Cuzick model or the Gail model.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0653-5) contains supplementary material, which is available to authorized users.

Highlights

The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation
The 10-year risk (Gail) model was originally developed using a case–control study of women attending screening in the United States [4] with invasive and ductal carcinoma in situ (DCIS) cases, but the absolute rates are calibrated to invasive cancer
To assess breast density as a risk factor, the following exclusions were made: 756 who had a previous diagnosis of breast cancer (29 with prospective cancer); 11 bilateral breast cancers and 7 for whom the side was unknown; 122 who had no visual assessment of breast density available (2 prospective cancers); 14 who were older than 73 years of age at enrolment (0 cancers); and 206 who had a breast implant (4 cancers)

Summary

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). Breast cancer risk models estimate the chance that a woman will develop breast cancer in the future, and a more accurate assessment is needed to guide prevention and screening strategies [1]. Risk is often assessed using the Gail (or Breast Cancer Risk Assessment Tool) and Tyrer-Cuzick [or International Breast Intervention Study (IBIS)] models [2,3,4,5]. It is calibrated to invasive and DCIS cancer rates and includes many of the Gail risk factors, but some are handled differently, including a more complex model for family history of the disease. The Tyrer-Cuzick model has not been validated to date in a prospective screening setting, but it has been compared with the Gail model in cohorts with a strong family history [7,8,9]

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