Literature DB >> 15159301

A prospective study of breast cancer risk using routine mammographic breast density measurements.

Pamela M Vacek1, Berta M Geller.   

Abstract

Mammographic breast density is a major risk factor for breast cancer but estimates of the relative risk associated with differing density patterns have varied widely. It is also unclear how menopausal status influences this association and to what extent the effects of density are due to its correlation with other risk factors. Most recent investigations of breast density have been case-control studies, which provide indirect estimates of relative risk. We have prospectively followed 61,844 women for an average of 3.1 years to directly estimate risk among women in the four mammographic breast density categories defined by the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). The study was population-based and used density assessments routinely made by community radiologists. Cox regression was used to obtain age-adjusted relative risk estimates and to control for other risk factors. Risk increased with density and the risk associated with extremely dense breasts, relative to entirely fatty breasts, was 4.6 (95% confidence interval, 1.7-12.6) for premenopausal women and 3.9 (95% confidence interval, 2.6-5.8) for postmenopausal women. Estimates for pre- and postmenopausal women did not differ significantly. Although breast density was significantly related to body mass index, age at first childbirth, and postmenopausal hormone use (P < 0.001), adjustment for these variables only slightly altered the relative risk estimates. Our results correspond well to those from case-control studies using more quantitative measures of mammographic breast density and suggest that routine Breast Imaging Reporting and Data System density measurements may be useful in models for assessing breast cancer risk in individual women.

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Mesh:

Year:  2004        PMID: 15159301

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  48 in total

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2.  Effects of exposure equalization on image signal-to-noise ratios in digital mammography: a simulation study with an anthropomorphic breast phantom.

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Review 7.  Body mass index and breast cancer risk according to postmenopausal estrogen-progestin use and hormone receptor status.

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8.  Breast density influences tumor subtypes and tumor aggressiveness.

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10.  Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Authors:  Richard Ha; Peter Chang; Jenika Karcich; Simukayi Mutasa; Eduardo Pascual Van Sant; Michael Z Liu; Sachin Jambawalikar
Journal:  Acad Radiol       Date:  2018-07-31       Impact factor: 3.173

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