Evidence map›Paper›PMID 41639723›Full record

ArticleBreast cancer research : BCR2026

Associations of local breast density with patient characteristics and risk of breast cancer among women undergoing diagnostic breast biopsy.

Aileen Burke, Shaoqi Fan, Ruth M Pfeiffer, Clara Bodelon, Ariane H L Chan, Melissa L Hill, Nabeel Khan, Aristarkh Tikhonov, Alexandra R Harris, Ralph P Highnam and 8 more

Abstract read
In one paragraph

Article in Breast cancer research : BCR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

18 authors.

Aileen BurkeSchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Dublin, Ireland. aileenburke20@rcsi.ie.ORCID http://orcid.org/0000-0001-8341-1670
Shaoqi FanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.ORCID http://orcid.org/0000-0001-5894-7510
Ruth M PfeifferDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.ORCID http://orcid.org/0000-0001-7791-2698
Clara BodelonDepartment of Population Science, American Cancer Society, Atlanta, GA, USA.ORCID http://orcid.org/0000-0002-6578-2678
Ariane H L ChanLunit International Ltd., Wellington, New Zealand.ORCID http://orcid.org/0000-0002-9314-7719
Melissa L HillLunit Canda, Inc., Vancouver, BC, Canada.ORCID http://orcid.org/0000-0002-9925-7601
Nabeel KhanLunit International Ltd., Wellington, New Zealand.
Aristarkh TikhonovLunit International Ltd., Wellington, New Zealand.ORCID http://orcid.org/0000-0002-2713-1508
Alexandra R HarrisDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.ORCID http://orcid.org/0000-0002-1049-3131
Ralph P HighnamLunit International Ltd., Wellington, New Zealand.ORCID http://orcid.org/0009-0007-2292-8175
Pamela M VacekUniversity of Vermont, Burlington, VT, USA.ORCID http://orcid.org/0000-0003-4963-0049
Donald L WeaverUniversity of Vermont, Burlington, VT, USA.ORCID http://orcid.org/0009-0003-9946-9369
Jason M JohnsonDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.ORCID http://orcid.org/0000-0001-7724-5784
Sally D HerschornUniversity of Vermont, Burlington, VT, USA.ORCID http://orcid.org/0000-0002-9193-6490
Brian L SpragueUniversity of Vermont, Burlington, VT, USA.ORCID http://orcid.org/0000-0001-7948-8317
Mark E ShermanQuantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA.ORCID http://orcid.org/0000-0002-9222-8808
Maeve MulloolySchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Dublin, Ireland.ORCID http://orcid.org/0000-0003-1388-6907
Gretchen L GierachDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.ORCID http://orcid.org/0000-0002-0165-5522

Funding

Clinical breast cancer risk prediction models for women with a high-risk benign breast diagnosisR01CA282725 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Brian L Sprague, Donald Lee Weaver · 2023 to 2026
$3.0M
Health Research Board EIA-2019-012NCI NIH HHS R01 CA282725
6 · The paper itself

Abstract

backgroundMuch of the existing literature reporting associations of breast density with breast cancer risk has evaluated the density of the entire breast. However, density is not homogeneously distributed throughout the breast, and tumors are more likely to arise in areas of dense tissue. This study describes novel measures of local breast density and evaluates their association with participant characteristics and risk of breast cancer.

methodsAmong a cohort of women aged 40–65 years who underwent image-guided breast biopsy following an abnormal mammogram from 2007 to 2010 (n = 808), absolute fibroglandular volume (FGV, cm3) and percentage fibroglandular volume (%FGV) of the entire breast (global) and local to the biopsy site (lesional, perilesional) were assessed from pre-biopsy raw digital mammograms using Volpara® software. Associations of participant characteristics with density measures were evaluated using multivariable linear regression models. Associations of density measures with prevalent (baseline) breast cancer and risk of incident (during 10-years of follow-up) breast cancer were evaluated using multivariable logistic and Cox proportional hazard regression models respectively, adjusted for age at mammogram, body mass index, reason for mammogram and self-reported lump prior to mammogram.

resultsGlobal and local density measures shared similar patterns of associations with participant characteristics. %FGV was not associated with prevalent or risk of incident breast cancer. However, absolute FGV was positively associated with prevalent breast cancer (n = 177 cases; odds ratio (OR) per 0.25 standard deviation (SD) increase for global FGV: OR 2.74, 95% confidence interval (CI) 1.07–7.00; lesional FGV: OR 1.43, 95% CI 1.16–1.76; perilesional FGV: OR 1.11, 95% CI 1.01–1.23). Additionally, local absolute FGV was associated with increased risk of incident breast cancer (n = 31 cases) during follow-up, with a hazard ratio (HR) per 0.25 SD increase for lesional FGV of 2.05, 95% CI 1.53–2.76 and for perilesional FGV of 1.42, 95% CI 1.14–1.77.

conclusionAmong women biopsied following an abnormal mammogram, higher global and local absolute FGV were associated with prevalent breast cancer. Additionally, elevated absolute FGV local to the biopsy site was associated with increased risk of breast cancer during follow-up. These findings may assist with risk stratification of women undergoing diagnostic breast biopsy.

Indexed as

BreastBreast DensityBreast NeoplasmsAdultAgedBiopsyFemaleHumansImage-Guided BiopsyMammographyMiddle AgedRisk FactorsBenign breast diseaseBreast cancer riskBreast diseasesBreast pathologyMammographic breast density

Identifiers

PMID41639723
PMCPMC12964696

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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.