Journal of the National Cancer Institute
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J. Natl. Cancer Inst. · Sep 2006
Meta Analysis Comparative StudySurvival with aromatase inhibitors and inactivators versus standard hormonal therapy in advanced breast cancer: meta-analysis.
Aromatase inhibitors and inactivators have been extensively tested in patients with advanced breast cancer, but it is unclear whether they offer any survival benefits compared with standard hormonal treatment with tamoxifen or progestagens. We performed a meta-analysis of randomized controlled trials that compared several generations of aromatase inhibitors and inactivators with standard hormonal treatment in patients with advanced breast cancer. ⋯ Inhibition of the aromatase system, in particular with third-generation aromatase inhibitors and inactivators, appears to be associated with statistically significant improved survival of patients with advanced breast cancer compared with standard hormonal treatments.
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J. Natl. Cancer Inst. · Sep 2006
Prospective breast cancer risk prediction model for women undergoing screening mammography.
Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography. ⋯ Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.
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J. Natl. Cancer Inst. · Sep 2006
Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density.
To improve the discriminatory power of the Gail model for predicting absolute risk of invasive breast cancer, we previously developed a relative risk model that incorporated mammographic density (DENSITY) from data on white women in the Breast Cancer Detection Demonstration Project (BCDDP). That model also included the variables age at birth of first live child (AGEFLB), number of affected mother or sisters (NUMREL), number of previous benign breast biopsy examinations (NBIOPS), and weight (WEIGHT). In this study, we developed the corresponding model for absolute risk. ⋯ This new model for absolute invasive breast cancer risk in white women promises modest improvements in discriminatory power compared with the Gail model but needs to be validated with independent data.