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The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Application of Breast Cancer Risk Prediction Models in Clinical Practice

Domchek, Susan M., Eisen, Andrea, Calzone, Kathleen, Stopfer, Jill, Blackwood, Anne, Weber, Barbara L.·2003·Journal of Clinical Oncology

Abstract

Breast cancer risk assessment provides an estimation of disease risk that can be used to guide management for women at all levels of risk. In addition, the likelihood that breast cancer risk is due to specific genetic susceptibility (such as BRCA1 or BRCA2 mutations) can be determined. Recent developments have reinforced the clinical importance of breast cancer risk assessment. Tamoxifen chemoprevention as well as prevention studies such as the Study of Tamoxifen and Raloxifene are available to women at increased risk of developing breast cancer. In addition, specific management strategies are now defined for BRCA1 and BRCA2 mutation carriers. Risk may be assessed as the likelihood of developing breast cancer (using risk assessment models) or as the likelihood of detecting a BRCA1 or BRCA2 mutation (using prior probability models). Each of the models has advantages and disadvantages, and all need to be interpreted in context. We review available risk assessment tools and discuss their application. As illustrated by clinical examples, optimal counseling may require the use of several models, as well as clinical judgment, to provide the most accurate and useful information to women and their families.

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Metadata

Authors
Domchek, S. M., Eisen, A., Calzone, K., Stopfer, J., Blackwood, A., & Weber, B. L.
Venue
Journal of Clinical Oncology
Published
2003-02-15
Volume
21
Issue
4
Pages
593-601
ISSN
  • 0732-183X (ISSN-L)
  • 1527-7755
OpenAlex
W2116841457
Cited by
201
References
3
FWCI
11.05
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Application of Breast Cancer Risk Prediction Models in Clinical Practice. https://app.cochat.ai/papers/10.1200/jco.2003.07.007
The paper
Domchek, S. M., Eisen, A., Calzone, K., Stopfer, J., Blackwood, A., & Weber, B. L. (2003). Application of Breast Cancer Risk Prediction Models in Clinical Practice. Journal of Clinical Oncology. https://doi.org/10.1200/jco.2003.07.007

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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.