Observational studyAnnals of internal medicine2016
Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study.
Observational study in Annals of internal medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05021055 (Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization), which is not on this map. Cited by 97 papers, 2 of them syntheses that pooled it.
What it found
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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.
Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization
Who cites it
97 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Test performance metrics for breast, cervical, colon, and lung cancer screening: a systematic review.Journal of the National Cancer Institute · 2023Pooled it
- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.JNCI cancer spectrum · 2024Trial
- Who makes the cut: Should AI-measured breast density alone stratify screening?European radiology · 2026Article
- Mammographic density assessment by an artificial intelligence model for breast cancer detection in BreastScreen Norway.European radiology · 2026Article
- An Unsupervised Deep Learning Framework for Quantitative Breast Density Estimation from Mammograms.Journal of imaging · 2026Article
- Reasoning in machine vision by learning fast and slow thinking.Nature communications · 2026Article
- Compact breast parenchyma: imaging and transcriptomic correlates.Journal of translational medicine · 2026Article
- A Deep Learning Breast Cancer Risk Model for Precise Supplemental Screening.JAMA network open · 2026Article
- Breast Imaging Reporting and Data System v2025: Key Updates in Mammography.Journal of the Korean Society of Radiology · 2026Review
- Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Beyond Cancer Detection: An AI Framework for Multidimensional Risk Profiling on Contrast-Enhanced Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Assessing mammographic density change within individuals across screening rounds using deep learning-based software.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Breast density in MRI: an AI-based quantification and relationship to assessment in mammography.NPJ breast cancer · 2025Article
- Incorporating Continuous Mammographic Density Into the BOADICEA Breast Cancer Risk Prediction Model.JCO precision oncology · 2025Article
- Breast MRI to Screen Women With Extremely Dense Breasts.Journal of magnetic resonance imaging : JMRI · 2025Review
- Mammographic density as a predictor of invasive breast cancer and ductal carcinoma in situ in over six million South Korean women.Breast cancer research and treatment · 2025Article
- Deep learning prediction of mammographic breast density using screening data.Scientific reports · 2025Article
- Impact of breast size and composition on the accuracy of mammography and ultrasonography in breast cancer screening.Breast cancer (Tokyo, Japan) · 2025Article
37 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Background: About half of the United States has legislation requiring radiology facilities to disclose mammographic breast density information to women, often with language recommending discussion of supplemental screening options for women with dense breasts. Objective: To examine variation in breast density assessment across radiologists in clinical practice. Design: Cross-sectional and longitudinal analyses of prospectively collected observational data. Setting: 30 radiology facilities within the 3 breast cancer screening research centers of the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium. Participants: Radiologists who interpreted at least 500 screening mammograms during 2011 to 2013 (n = 83). Data on 216 783 screening mammograms from 145 123 women aged 40 to 89 years were included. Measurements: Mammographic breast density, as clinically recorded using the 4 Breast Imaging Reporting and Data System categories (heterogeneously dense and extremely dense categories were considered "dense" for analyses), and patient age, race, and body mass index (BMI). Results: Overall, 36.9% of mammograms were rated as showing dense breasts. Across radiologists, this percentage ranged from 6.3% to 84.5% (median, 38.7% [interquartile range, 28.9% to 50.9%]), with multivariable adjustment for patient characteristics having little effect (interquartile range, 29.9% to 50.8%). Examination of patient subgroups revealed that variation in density assessment across radiologists was pervasive in all but the most extreme patient age and BMI combinations. Among women with consecutive mammograms interpreted by different radiologists, 17.2% (5909 of 34 271) had discordant assessments of dense versus nondense status. Limitation: Quantitative measures of mammographic breast density were not available for comparison. Conclusion: There is wide variation in density assessment across radiologists that should be carefully considered by providers and policymakers when considering supplemental screening strategies. The likelihood of a woman being told she has dense breasts varies substantially according to which radiologist interprets her mammogram. Primary Funding Source: National Institutes of Health.
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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.