ArticleBreast cancer research : BCR2018
A comparison of five methods of measuring mammographic density: a case-control study.
Article in Breast cancer research : BCR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it, 132 citations in OpenAlex.
- Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis.Breast (Edinburgh, Scotland) · 2022Pooled it
- Comparison of three algorithms to measure breast density on mammograms in a population-based screening cohort.European radiology · 2026Article
- Early-adulthood body mass index, mammographic density and post-menopausal breast cancer risk: a mediation analysis.British journal of cancer · 2026Article
- A comparison of tumour characteristics of screen-detected and interval cancers and their relationship with breast density and AJCC prognostic stage in a UK screening cohort.European radiology · 2026Article
- DensiThAI: A Multi-View Deep Learning Framework for Breast Density Estimation Using Infrared Images.Journal of imaging informatics in medicine · 2026Article
- Deep learning models to predict mammographic density jointly on standard dose and low dose images.The British journal of radiology · 2026Article
- An Unsupervised Deep Learning Framework for Quantitative Breast Density Estimation from Mammograms.Journal of imaging · 2026Article
- A novel quantification method for automatic computation of breast density from mammography images using deep learning.Breast cancer (Tokyo, Japan) · 2026Article
- Change in mammographic density as a potential predictor of cancer recurrence after breast conservation surgery and adjuvant endocrine therapy: results of the MEDICI study.Breast cancer research : BCR · 2026Article
- Development and evaluation of artificial intelligence tools to estimate volumetric breast density from processed 2D mammograms.BJR artificial intelligence · 2026Article
- Agreement Among Breast Cancer Screening Modalities in Breast Density Assessment and Cancer Risk Prediction.International journal of breast cancer · 2026Article
- Article
- Comparing percent breast density assessments of an AI-based method with expert reader estimates: inter-observer variability.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Evaluating mammographic density's contribution to improve a breast cancer risk model with questionnaire-based and polygenic factors.NPJ breast cancer · 2025Article
- Differentiating Breast Tissue Stiffness With Magnetic Resonance Elastography (MRE): A Focus on Fibroglandular and Fatty Tissues.NMR in biomedicine · 2025Article
- Circulating sex hormones and volumetric breast density: A prospective study in women from the EPIC Florence cohort.International journal of cancer · 2025Article
- Identifying breast cancer risk factors and evaluating biennial mammography screening efficacy using big data analysis in Taiwan.Scientific reports · 2025Article
- Concordant and discordant breast density patterns by different approaches for assessing breast density and breast cancer risk.Breast cancer research and treatment · 2025Article
- Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review.Journal of imaging · 2025Review
- Deep learning in breast cancer risk prediction: a review of recent applications in full-field digital mammography.Frontiers in oncology · 2025Review
7 more citing papers are in PubMed but not listed here.
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Authors and funding
18 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundHigh mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening.
methodsWomen participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors.
resultsThe strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05).
conclusionsVisual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.
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