The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Mammographic density, breast cancer risk and risk prediction
Open accessAbstract
In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gail, M. H., Brinton, L. A., Byar, D. P., Corle, D. K., Green, S. B., Schairer, C., & Mulvihill, J. J. (1989) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/81.24.1879 · Cited by 3,334
Mammographic Density and the Risk and Detection of Breast Cancer
Boyd, N. F., Guo, H., Martin, L. J., Sun, L., Stone, J., Fishell, E., Jong, R. A., Hislop, G., Chiarelli, A., Minkin, S., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa062790 · Cited by 2,478
Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis
McCormack, V. A., & dos Santos Silva, I. (2006) · Cancer Epidemiology, Biomarkers & Prevention · doi:10.1158/1055-9965.epi-06-0034 · Cited by 2,143
Pepe, M. S. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh101 · Cited by 1,276
Berg, W. A., Campassi, C., Langenberg, P., & Sexton, M. J. (2000) · American Journal of Roentgenology · doi:10.2214/ajr.174.6.1741769 · Cited by 1,273
Boyd, N. F., Byng, J. W., Jong, R. A., Fishell, E. K., Little, L. E., Miller, A. B., Lockwood, G. A., Tritchler, D. L., & Yaffe, M. J. (1995) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/87.9.670 · Cited by 1,105
Breast patterns as an index of risk for developing breast cancer
Wolfe, J. (1976) · American Journal of Roentgenology · doi:10.2214/ajr.126.6.1130 · Cited by 916
Mammographic Features and Breast Cancer Risk: Effects With Time, Age, and Menopause Status
Byrne, C., Schairer, C., Wolfe, J., Parekh, N., Salane, M., Brinton, L. A., Hoover, R., & Haile, R. (1995) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/87.21.1622 · Cited by 864
Validation Studies for Models Projecting the Risk of Invasive and Total Breast Cancer Incidence
Costantino, J. P., Gail, M. H., Pee, D., Anderson, S., Redmond, C. K., Benichou, J., & Wieand, H. S. (1999) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/91.18.1541 · Cited by 765
Rockhill, B., Spiegelman, D., Byrne, C., Hunter, D. J., & Colditz, G. A. (2001) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/93.5.358 · Cited by 618
Cited by
Matrix Crosslinking Forces Tumor Progression by Enhancing Integrin Signaling
Levental, K. R., Yu, H., Kass, L., Lakins, J. N., Egeblad, M., Erler, J. T., Fong, S. F. T., Csiszar, K., Giaccia, A., Weninger, W., et al. (2009) · Cell · doi:10.1016/j.cell.2009.10.027 · Cited by 4,101
The Relationship of Mammographic Density and Age: Implications for Breast Cancer Screening
Checka, C. M., Chun, J. E., Schnabel, F. R., Lee, J., & Toth, H. (2012) · American Journal of Roentgenology · doi:10.2214/ajr.10.6049 · Cited by 422
Increased peri‐ductal collagen micro‐organization may contribute to raised mammographic density
McConnell, J. C., O’Connell, O. V., Brennan, K., Weiping, L., Howe, M., Joseph, L., Knight, D., O’Cualain, R., Lim, Y., Leek, A., et al. (2016) · Breast Cancer Research · doi:10.1186/s13058-015-0664-2 · Cited by 110
Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women
Banegas, M. P., John, E. M., Slattery, M. L., Gomez, S. L., Yu, M., LaCroix, A. Z., Pee, D., Chlebowski, R. T., Hines, L. M., Thompson, C. A., et al. (2017) · Journal of the National Cancer Institute · doi:10.1093/jnci/djw215 · Cited by 74
Metadata
- Authors
- Vachon, C. M., van Gils, C. H., Sellers, T. A., Ghosh, K., Pruthi, S., Brandt, K. R., & Pankratz, V. S.
- Venue
- Breast Cancer Research
- Published
- 2007-12
- Volume
- 9
- Issue
- 6
- Pages
- 217-217
- ISSN
- 1465-5411 (ISSN-L)
- 1465-542X
- OpenAlex
- W2151266081
- Cited by
- 346
- References
- 44
- FWCI
- 8.07
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Mammographic density, breast cancer risk and risk prediction. https://app.cochat.ai/papers/10.1186/bcr1829
- The paper
- Vachon, C. M., van Gils, C. H., Sellers, T. A., Ghosh, K., Pruthi, S., Brandt, K. R., & Pankratz, V. S. (2007). Mammographic density, breast cancer risk and risk prediction. Breast Cancer Research. https://doi.org/10.1186/bcr1829
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
