ArticlePloS one2015
Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Breast Density and Risk of Breast Cancer in Asian Women: A Meta-analysis of Observational Studies.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2016Pooled it
- Review
- Breast Cancer in Asia: Incidence, Mortality, Early Detection, Mammography Programs, and Risk-Based Screening Initiatives.Cancers · 2022Review
- Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore.Cancer medicine · 2021Article
- Breast Cancer Risk Prediction in Korean Women: Review and Perspectives on Personalized Breast Cancer Screening.Journal of breast cancer · 2020Review
- Incidence of breast cancer attributable to breast density, modifiable and non-modifiable breast cancer risk factors in Singapore.Scientific reports · 2020Article
- Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management.International journal of molecular sciences · 2019Review
- Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model.Breast cancer research : BCR · 2019Article
- Factors associated with false-positive mammography at first screen in an Asian population.PloS one · 2019Article
- Saliva samples as a source of DNA for high throughput genotyping: an acceptable and sufficient means in improvement of risk estimation throughout mammographic diagnostics.European journal of medical research · 2018Article
- Determinants of breast size in Asian women.Scientific reports · 2018Article
- Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionKnown prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.
methodsWe estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman's genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.
resultsDuring 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22-2.10), 2.20 (1.65-2.92), 2.33 (1.71-3.20), 2.12 (1.43-3.14), and 3.27 (2.24-4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03-1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.
conclusionMammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.
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