Breast Density Across a Regional Screening Population: Effects of Age, Ethnicity and Deprivation

Overview

Journal Br J Radiol

Publisher Oxford University Press

Specialty Radiology

Date 2015 Sep 3

PMID 26329467

Citations 18

Authors

Samantha L Heller

Sue Hudson

Louise S Wilkinson

Affiliations

Soon will be listed here.

Abstract

Objective: Breast density (BD) is a recognized risk factor for breast cancer. This study maps density variation across a screening population and identifies demographic distinctions, which may affect density and so impact on cancer development/detection. We focus on the relationship between age, ethnicity and socioeconomic status on density.

Methods: This retrospective study on a screening population adheres to local patient confidentiality requirements. BD data from screening mammograms (March 2013 to September 2014) were measured using Volpara((®))Density(™) software (Volpara((®))Solutions(™), Wellington, New Zealand). Demographics, including patient age, ethnicity and deprivation index, were obtained from our breast screening database and analysed with respect to breast volume (BV), fibroglandular tissue volume (FGV), Volpara %BD and Volpara Grade (1-4 scale, lowest to highest).

Results: Study population demonstrates little difference for BV with respect to age, but a slight negative trend was noted when FGV was evaluated vs age. Density was linked to ethnicity: females of Chinese ethnicity had higher BD largely reflecting their lower BV. Females in the most deprived quintiles tended to have larger and therefore less dense breasts.

Conclusion: Our mapping of BD in a regional screening programme demonstrates impact of age, ethnicity and socioeconomic status on BD with attendant implications for breast cancer risk.

Advances In Knowledge: BD is a known risk factor for development of breast cancer. Density trends in a large regional screening population with respect to age, ethnicity and socioeconomics may eventually help identify the risk of breast cancer in certain subsets of the population.

Citing Articles

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Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race.

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Glechner A, Wagner G, Mitus J, Teufer B, Klerings I, Bock N Cochrane Database Syst Rev. 2023; 3:CD009632.

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