SynthesisJournal of the National Cancer Institute2012
A novel automated mammographic density measure and breast cancer risk.
Synthesis in Journal of the National Cancer Institute, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 53 papers, 2 of them syntheses that pooled it.
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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
53 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Does Laterality in Breast Cancer still have the Importance to be Studied? A Meta-analysis of Patients with Breast Cancer.Current medicinal chemistry · 2024Pooled it
- Mammographic density and risk of breast cancer by age and tumor characteristics.Breast cancer research : BCR · 2013Pooled it
- Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.JNCI cancer spectrum · 2024Trial
- Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.Medical physics · 2015Trial
- Interactions between genetic and epidemiological factors influencing mammographic density.American journal of epidemiology · 2026Article
- Investigating the relationship between breast cancer risk factors and an AI-generated mammographic texture feature in the Nurses' Health Study II.NPJ breast cancer · 2025Article
- Fecal sample biobanking for breast cancer research focused on the gut microbiome.medRxiv : the preprint server for health sciences · 2025Article
- Radiomic Parenchymal Phenotypes of Breast Texture from Mammography and Association with Risk of Breast Cancer.Radiology · 2025Article
- Association between Inflammatory Dietary Pattern and Mammographic Features.The Journal of nutrition · 2024Article
- Plasma C-peptide mammographic features and risk of breast cancer.NPJ breast cancer · 2024Article
- Reproductive Factors Related to Childbearing and a Novel Automated Mammographic Measure, V.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women.Breast cancer research and treatment · 2024Article
- Breast density analysis of digital breast tomosynthesis.Scientific reports · 2023Article
- Breast Density Analysis Using Digital Breast Tomosynthesis.bioRxiv : the preprint server for biology · 2023Article
- Artificial Intelligence in Breast X-Ray Imaging.Seminars in ultrasound, CT, and MR · 2023Review
- A genome-wide association study of mammographic texture variation.Breast cancer research : BCR · 2022Article
- Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms.Journal of digital imaging · 2022Article
- Use of a convolutional neural network-based mammographic evaluation to predict breast cancer recurrence among women with hormone receptor-positive operable breast cancer.Breast cancer research and treatment · 2022Article
- Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci.Breast cancer research : BCR · 2022Article
- Associations of Oral Contraceptives with Mammographic Breast Density in Premenopausal Women.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
backgroundMammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD).
methodsThree clinic-based studies were included: a case-cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case-control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case-control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided.
resultsThe variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 2.0 [95% CI = 1.3 to 3.1]; OR = 2.7 [95% CI = 2.1 to 3.6]; OR = 2.4 [95% CI = 1.4 to 3.9]; [corrected] all P (trend) < .001). [corrected]. The risk estimates and AUCs for the variation measure were similar to [corrected] those for percent density (AUCs for variation = 0.60-0.62 and [corrected] AUCs for percent density = 0.61-0.65). [corrected]. A meta-analysis of the three studies demonstrated similar associations [corrected] between variation and breast cancer (highest vs lowest quartile: RR = 1.8, 95% CI = 1.4 to 2.3) and [corrected] percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9).
conclusionThe association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.
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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.