Breast density and risk factors for interval and screen-detected breast cancers: a case–case analysis of the Breast Cancer Now Generations Study, UK

Abstract Background Interval breast cancers (IBC), diagnosed between routine screening rounds, tend to have a worse prognosis than screen-detected breast cancers (SDBC). Identifying risk factors for IBCs is critical for improving early detection and developing risk-stratified screening strategies to reduce their incidence. We evaluated associations of breast density, reproductive, hormonal, lifestyle and medical factors with IBC compared to SDBC in a large UK cohort. Methods Analyses included 1,940 women diagnosed with breast cancer between 2004 and 2018 after enrolment in the Breast Cancer Now Generations Study, a prospective UK cohort linked to the National Health Service Breast Screening Programme. Pre-diagnostic risk factors were collected at enrolment, and breast density was estimated from pre-diagnostic mammograms using Cumulus in a subset of 1,191 cases. Risk factor associations with density were estimated using linear regression models. We identified 1,185 SDBCs and 755 IBCs included in logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between risk factors and IBC, adjusting for age at diagnosis, time since recruitment and year of diagnosis, breast density and tumour characteristics. Results Higher breast density was associated with later age at menarche, nulliparity, breastfeeding, history of benign breast disease (BBD), alcohol consumption, lower body mass index (BMI) at recruitment and at age 20, and current use of menopausal hormone therapy (MHT). In a fully-adjusted model, IBC risk was lower for overweight women (OR (95% CI ) = 0.74 (0.58–0.93) vs. normal weight), and higher for high breast density (2.13 (1.44–3.17) for Q4 vs. Q1), later age at menopause (1.60 (1.02–2.50) 55 + vs. < 50), current MHT use (1.41 (1.04–1.91) vs. never users), history of BBD (1.36 (1.11–1.68)), being underweight at age 20 (1.65 (1.14–2.38) vs. normal weight), family history of breast cancer (1.26 (1.00–1.58)) and ever using oral contraceptives (1.25 (0.93–1.69) vs. never). These risk factor associations were independent of tumour characteristics. Conclusions Breast density and several risk factors independently increase the likelihood of IBC, highlighting opportunities for tailored screening strategies to enhance early detection and reduce IBCs incidence.

Authors

Institutions

Publication Details

Journal
Breast Cancer Research
Published
2026-08-13
DOI
https://doi.org/10.1186/s13058-026-02355-z
Primary Topic
Digital Radiography and Breast Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Breast density and risk factors for interval and screen-detected breast cancers: a case–case analysis of the Breast Cancer Now Generations Study, UK

Penny Coulson, Amy Berrington de González, Montserrat García‐Closas, Micheal Jones et al.
Breast Cancer Research
Digital Radiography and Breast Imaging
article

Breast density and risk factors for interval and screen-detected breast cancers: a case–case analysis of the Breast Cancer Now Generations Study, UK

Penny Coulson, Amy Berrington de González, Montserrat García‐Closas, Micheal Jones, Martina Brayley, Reuben Frost, Louise Johns
article en

Abstract

Abstract Background Interval breast cancers (IBC), diagnosed between routine screening rounds, tend to have a worse prognosis than screen-detected breast cancers (SDBC). Identifying risk factors for IBCs is critical for improving early detection and developing risk-stratified screening strategies to reduce their incidence. We evaluated associations of breast density, reproductive, hormonal, lifestyle and medical factors with IBC compared to SDBC in a large UK cohort. Methods Analyses included 1,940 women diagnosed with breast cancer between 2004 and 2018 after enrolment in the Breast Cancer Now Generations Study, a prospective UK cohort linked to the National Health Service Breast Screening Programme. Pre-diagnostic risk factors were collected at enrolment, and breast density was estimated from pre-diagnostic mammograms using Cumulus in a subset of 1,191 cases. Risk factor associations with density were estimated using linear regression models. We identified 1,185 SDBCs and 755 IBCs included in logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between risk factors and IBC, adjusting for age at diagnosis, time since recruitment and year of diagnosis, breast density and tumour characteristics. Results Higher breast density was associated with later age at menarche, nulliparity, breastfeeding, history of benign breast disease (BBD), alcohol consumption, lower body mass index (BMI) at recruitment and at age 20, and current use of menopausal hormone therapy (MHT). In a fully-adjusted model, IBC risk was lower for overweight women (OR (95% CI ) = 0.74 (0.58–0.93) vs. normal weight), and higher for high breast density (2.13 (1.44–3.17) for Q4 vs. Q1), later age at menopause (1.60 (1.02–2.50) 55 + vs. < 50), current MHT use (1.41 (1.04–1.91) vs. never users), history of BBD (1.36 (1.11–1.68)), being underweight at age 20 (1.65 (1.14–2.38) vs. normal weight), family history of breast cancer (1.26 (1.00–1.58)) and ever using oral contraceptives (1.25 (0.93–1.69) vs. never). These risk factor associations were independent of tumour characteristics. Conclusions Breast density and several risk factors independently increase the likelihood of IBC, highlighting opportunities for tailored screening strategies to enhance early detection and reduce IBCs incidence.

Breast Cancer Research
Institute of Cancer Research (GB), Cancer Research UK (GB), Imperial College London (GB)
Institute of Cancer Research, Breast Cancer Now
Good health and well-being
Openalex Percentile: Top 10%
Digital Radiography and Breast Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.