Baseline Mammographic Breast Density and the Risk of Invasive Breast Cancer in Postmenopausal Women Participating in the NSABP Study of Tamoxifen and Raloxifene (STAR)
ABSTRACT Mammographic breast density is an established risk factor for breast cancer. However, results are inconclusive regarding its use in risk prediction models. The current study evaluated 13,409 postmenopausal participants in the NSABP Study of Tamoxifen and Raloxifene. A measure of breast density as reported on the entry mammogram report was extracted and categorized according to The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) classifications. An increased risk of invasive breast cancer was associated with higher mammographic breast density (P=0.001). The association remained significant after adjusting for age, treatment, and smoking history (HR 1.32, 95%CI 1.16-1.58), as well as when added to a model including the Gail score (HR 1.33, 95%CI 1.14-1.55). At five years after random assignment, time-dependent AUC improved from 0.63 for a model with Gail score alone to 0.64 when considering breast density and Gail score. Breast density was also significant when added to an abbreviated model tailored for estrogen receptor-positive breast cancers (P=0.02). In this study, high BI-RADS breast density was significantly associated with increased breast cancer risk when considered in conjunction with Gail score but provided only slight improvement to the Gail score for predicting the incidence of invasive breast cancer. The BI-RADS breast composition classification system is a quick and readily available method for assessing breast density for risk prediction evaluations; however, its addition to the Gail model does not appear to provide substantial predictability improvements in this population of postmenopausal healthy women at increased risk for breast cancer.
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ABSTRACT: Breast cancer is one of the most common cancers in the world and is the first cause of death due to cancer among women. Mammography is the best screening method and mammographic density, which determines the percentage of fibro glandular tissue of breast, is one of the strongest risk factors of breast cancer. Because benign and malignant lesions may present as dense lesions in mammography so it is necessary to take a core biopsy of any suspicious lesions to evaluate pathologic findings. The aim of this study was to assess the association between mammographic density and histopathological findings in Iranian population. Moreover, we assessed the correlation between mammographic density and protein expression profile. We indeed, determined the accuracy and positive predictive value and negative predictive value of mammographic reports in our center. This study is a cross-sectional study carried out among 131 eligible women who had referred to imaging center for mammographic examination and had been advised to take biopsy of breast tissue. All participants of the study had filled out the informed consent. Pathologic review was performed blinded to the density status. Patients were divided into low density breast tissue group (ACR density group 1-2) and high density breast tissue group (ACR 3, 4) and data was compared between these two groups. Statistical analysis performed using SPSS for windows, version 11.5. We used chi-square, t-test, and logistic regression test for analysis and Odds Ratio calculated where indicated. In patients with high breast densities, malignant cases (61.2%) were significantly more in comparison to patients with low breast densities (37.3%) (P= 0.007, OR=2.66 95% CI=1.29-5.49). After adjusting for age, density was associated with malignancy in age groups <46 years (P=0.007), and 46-60 years (P=0.024) but not in age group >60yrs (P=0.559). Adjusting for menopausal status, density showed association with malignancy in both pre-menopause (P=0.041) and menopause (P=0.010) patients. Using logistic regression test, only age and density showed independent association with risk of breast cancer. No association was found between density and protein profile expression. Mammographic method has a false negative percent of 10.3% for negative BI-RADS group and a Positive Predictive Value (PPV) of 69.6% for positive BI-RADS group. PPVs for BI-RADS 4a, 4b, 4c and 5 were 16%, 87.5%, 84.6%, and 91.5% respectively. NPVs for BI-RADS 1, 2 and 3 were 66.7%, 95.8% and 90.0% respectively. In this study we found that increasing in mammographic density is associated with an increase in malignant pathology reports. Expression of ER, PR and HER-2 receptors didn't show association with density. Our mammographic reports had a sensitivity of 94.1% and a specificity of 55.6%, which shows that our mammography is an acceptable method for screening breast cancer in this center.12/2013; 15(12):e16698. DOI:10.5812/ircmj.16698
Nature medicine 04/2014; 20(4):332-3. DOI:10.1038/nm.3523 · 28.05 Impact Factor
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ABSTRACT: Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our efforts to predict risk and prevent breast cancer over the next 10 years.Breast cancer research: BCR 09/2014; 16(5):446. DOI:10.1186/s13058-014-0446-2 · 5.88 Impact Factor