ArticleBreast cancer research and treatment2018
A comprehensive tool for measuring mammographic density changes over time.
Article in Breast cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature.Cancer causes & control : CCC · 2023Pooled it
- Endoxifen for mammographic density reduction-results from the KARISMA endoxifen trial.Journal of the National Cancer Institute · 2026Trial
- Influence of endoxifen on mammographic density: results from the KARISMA-Tam trial.Journal of the National Cancer Institute · 2025Trial
- Low-dose tamoxifen treatment reduces collagen organisation indicative of tissue stiffness in the normal breast: results from the KARISMA randomised controlled trial.Breast cancer research : BCR · 2024Trial
- A Randomized Controlled Trial of Soy Isoflavone Intake on Mammographic Density among Malaysian Women.Nutrients · 2023Trial
- Time to Mammographic Density Decrease After Exposure to Tamoxifen.The oncologist · 2022Trial
- Topical Endoxifen for Mammographic Density Reduction-A Randomized Controlled Trial.The oncologist · 2022Trial
- Low-Dose Tamoxifen for Mammographic Density Reduction: A Randomized Controlled Trial.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2021Trial
- The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen-Detected Breast Cancer Risk.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Genome-wide association study of Asian women identifies putative mammographic density-associated loci.Breast cancer research : BCR · 2025Article
- Assessing mammographic density change within individuals across screening rounds using deep learning-based software.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
- Incorporating Continuous Mammographic Density Into the BOADICEA Breast Cancer Risk Prediction Model.JCO precision oncology · 2025Article
- Breast Density Changes after Risk-Reducing Salpingo-oophorectomy in Women with a Pathogenic Germline Variant in BRCA1 or BRCA2.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Incidence and Risk Factors of Interval and Screen-Detected Breast Cancer.JAMA oncology · 2025Article
- Studying the association between longitudinal nondense breast tissue measurements and the risk of breast cancer: a joint modeling approach.American journal of epidemiology · 2025Article
- A roadmap to reduce the incidence and mortality of breast cancer by rethinking our approach to women's health.Breast cancer research and treatment · 2025Review
- Investigating the added value of incorporatingmammographic density to an integrated breastcancer risk model with questionnaire-based riskfactors and polygenic risk score.Research square · 2024Article
- Association of area- and volumetric-mammographic density and breast cancer risk in women of Asian descent: a case control study.Breast cancer research : BCR · 2024Article
- European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening-a nested case-control study.The Lancet regional health. Europe · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundMammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time.
methodRaw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment.
resultsThe average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3-1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60-0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001.
conclusionsThe quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up.
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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.