ArticleBreast cancer research : BCR2017
Combining quantitative and qualitative breast density measures to assess breast cancer risk.
Article in Breast cancer research : BCR, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis.Breast (Edinburgh, Scotland) · 2022Pooled it
- Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Racial differences in quantitative background parenchymal enhancement on breast magnetic resonance imaging.Cancer · 2025Article
- Article
- Interplay of BMI and volumetric breast density measures and breast cancer risk for black and white women.Breast cancer research : BCR · 2025Article
- Incorporating Continuous Mammographic Density Into the BOADICEA Breast Cancer Risk Prediction Model.JCO precision oncology · 2025Article
- Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.Scientific reports · 2025Article
- Cross-field strength and multi-vendor reliability of MagDensity for MRI-based quantitative breast density analysis.PloS one · 2025Article
- Artificial intelligence and consistency in patient care: a large-scale longitudinal study of mammographic density assessment.BJR artificial intelligence · 2025Article
- MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.Aesthetic plastic surgery · 2024Article
- Updates in Breast Cancer Screening and Diagnosis.Current treatment options in oncology · 2024Review
- Prediction of breast cancer risk by automated volumetric breast density measurement.Japanese journal of radiology · 2023Article
- Toward a digital analysis of environmental impacts on rodent mammary gland density during critical developmental windows.Reproductive toxicology (Elmsford, N.Y.) · 2022Article
- Fatty liver disease and changes in dense breasts in pre- and postmenopausal women: the Kangbuk Samsung Health Study.Breast cancer research and treatment · 2021Article
- Association of Breast Density With Breast Cancer Risk Among Women Aged 65 Years or Older by Age Group and Body Mass Index.JAMA network open · 2021Article
- Mammographic Variation Measures, Breast Density, and Breast Cancer Risk.AJR. American journal of roentgenology · 2021Article
- Gut microbiome, body weight, and mammographic breast density in healthy postmenopausal women.Cancer causes & control : CCC · 2021Article
- Abnormal anti-Müllerian hormone level may be a trigger for breast cancer in young women: A case-control study.International journal of reproductive biomedicine · 2021Article
- Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.Academic radiology · 2019Article
- Combined effect of volumetric breast density and body mass index on breast cancer risk.Breast cancer research and treatment · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundAccurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.
methodsWe conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.
resultsCompared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.
conclusionsRisk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.