Baseline Mammographic Breast Density and the Risk of Invasive Breast Cancer in Postmenopausal Women Participating in the NSABP Study of Tamoxifen and Raloxifene (STAR)

1Dpt of Biostatistics, NSABP
Cancer Prevention Research (Impact Factor: 4.44). 10/2012; 5(11). DOI: 10.1158/1940-6207.CAPR-12-0273
Source: PubMed


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: Developing improved methods for breast cancer risk prediction could facilitate the targeting of interventions to women at highest risk, thereby reducing mortality, while sparing low-risk women the costs and inconvenience of unnecessary testing and procedures. However, currently available risk assessment tools fall short of achieving accurate individual risk prediction, precluding implementation of this approach. Improving these tools will require the identification of new methods of assessing risk and increasing the accuracy of existing risk indicators. We review four emerging topics that may have importance for breast cancer risk assessment: etiological heterogeneity, genetic susceptibility, mammographic breast density, and assessment of breast involution.
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    ABSTRACT: Purpose: To (a) perform a pilot study comparing radiologists' reading of breast density at computed tomography (CT) of the chest with breast density readings from mammography performed in the same patient and (b) compare a subset of these with computer-derived measurements of breast density at CT. Materials and methods: The institutional review board waived informed consent for this HIPAA-compliant retrospective review of mammograms and chest CT scans from 206 women obtained within 1 year of each other. Two radiologists with expertise in interpreting mammographic and CT findings independently reviewed the mammograms and CT scans and classified each case into one of the four breast density types defined by the Breast Imaging Reporting and Data System of the American College of Radiology. Interreader agreements for the mammographic density types and CT density grades were determined by using the Cohen weighted κ statistic. The intrareader correlation coefficient was determined in a subset of CT images. In another subset of 40 cases, the agreement of the semiautomated computer-derived measurements of breast density with the consensus of the two radiologists was assessed. Results: Interreader agreement was higher for the CT density grades than for the mammographic density types, with 0.79 (95% confidence interval [CI]: 0.73, 0.85) versus 0.62 (95% CI: 0.54, 0.70). The intrareader reliability of breast density grades on CT images was 0.88. The computer-derived breast density measurements agreed with those of the radiologists in 36 (90%) cases. When four cases were manually adjusted for the complex anatomy, there was agreement for all cases. Conclusion: Preliminary results suggest that on further validation, breast density readings at CT may provide important additional risk information on CT of the chest and that computer-derived measurements may be helpful in such assessment.
    No preview · Article · Oct 2013 · Radiology
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