Literature DB >> 17341730

Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk.

Karla Kerlikowske1, Laura Ichikawa, Diana L Miglioretti, Diana S M Buist, Pamela M Vacek, Rebecca Smith-Bindman, Bonnie Yankaskas, Patricia A Carney, Rachel Ballard-Barbash.   

Abstract

BACKGROUND: Whether a change over time in clinically measured mammographic breast density influences breast cancer risk is unknown.
METHODS: From January 1993 to December 2003, data that included American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density categories (1-4 in order of increasing density) were collected prospectively on 301,955 women aged 30 and older who were not using postmenopausal hormone replacement therapy and underwent at least two screening mammography examinations; 2639 of the women were diagnosed with breast cancer within 1 year of the last examination. Women's first and last BI-RADS breast density (average 3.2 years apart) and logistic regression were used to model the odds of having invasive breast cancer or ductal carcinoma in situ diagnosed within 12 months of the last examination by change in BI-RADS category. Rates of breast cancer adjusted for age, mammography registry, and time between screening examinations were estimated from this model. All statistical tests were two-sided.
RESULTS: The rate (breast cancers per 1000 women) of breast cancer was higher if BI-RADS breast density category increased from 1 to 2 (5.6, 95% confidence interval [CI] = 4.7 to 6.9) or 1 to 3 (9.9, 95% CI = 6.4 to 15.5) compared to when it remained at BI-RADS density of 1 (3.0, 95% CI = 2.3 to 3.9; P<.001 for trend). Similar and statistically significant trends between increased or decreased density and increased or decreased risk of breast cancer, respectively, were observed for women whose breast density category was initially 2 or 3 and changed categories. BI-RADS density of 4 on the first examination was associated with a high rate of breast cancer (range 9.1-13.4) that remained high even if breast density decreased.
CONCLUSION: An increase in BI-RADS breast density category within 3 years may be associated with an increase in breast cancer risk and a decrease in density category with a decrease in risk compared to breast cancer risk in women in whom breast density category remains unchanged. Two longitudinal measures of BI-RADS breast density may better predict a woman's risk of breast cancer than a single measure.

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Year:  2007        PMID: 17341730     DOI: 10.1093/jnci/djk066

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN: 0027-8874            Impact factor:   13.506


  92 in total

1.  Automatic classification of mammography reports by BI-RADS breast tissue composition class.

Authors:  Bethany Percha; Houssam Nassif; Jafi Lipson; Elizabeth Burnside; Daniel Rubin
Journal:  J Am Med Inform Assoc       Date:  2012-01-29       Impact factor: 4.497

2.  Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique.

Authors:  Jacob D Shea; Panagiotis Kosmas; Susan C Hagness; Barry D Van Veen
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

3.  Breast cancer risk by breast density, menopause, and postmenopausal hormone therapy use.

Authors:  Karla Kerlikowske; Andrea J Cook; Diana S M Buist; Steve R Cummings; Celine Vachon; Pamela Vacek; Diana L Miglioretti
Journal:  J Clin Oncol       Date:  2010-07-19       Impact factor: 44.544

4.  Consistency of visual assessments of mammographic breast density from vendor-specific "for presentation" images.

Authors:  Mohamed Abdolell; Kaitlyn Tsuruda; Christopher B Lightfoot; Eva Barkova; Melanie McQuaid; Judy Caines; Sian E Iles
Journal:  J Med Imaging (Bellingham)       Date:  2015-10-30

5.  Comparative effectiveness of digital versus film-screen mammography in community practice in the United States: a cohort study.

Authors:  Karla Kerlikowske; Rebecca A Hubbard; Diana L Miglioretti; Berta M Geller; Bonnie C Yankaskas; Constance D Lehman; Stephen H Taplin; Edward A Sickles
Journal:  Ann Intern Med       Date:  2011-10-18       Impact factor: 25.391

6.  Double-Blind Randomized 12-Month Soy Intervention Had No Effects on Breast MRI Fibroglandular Tissue Density or Mammographic Density.

Authors:  Anna H Wu; Darcy Spicer; Agustin Garcia; Chiu-Chen Tseng; Linda Hovanessian-Larsen; Pulin Sheth; Sue Ellen Martin; Debra Hawes; Christy Russell; Heather MacDonald; Debu Tripathy; Min-Ying Su; Giske Ursin; Malcolm C Pike
Journal:  Cancer Prev Res (Phila)       Date:  2015-08-14

7.  Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI.

Authors:  Ke Nie; Jeon-Hor Chen; Siwa Chan; Man-Kwun I Chau; Hon J Yu; Shadfar Bahri; Tiffany Tseng; Orhan Nalcioglu; Min-Ying Su
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

8.  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

9.  Reported mammographic density: film-screen versus digital acquisition.

Authors:  Jennifer A Harvey; Charlotte C Gard; Diana L Miglioretti; Bonnie C Yankaskas; Karla Kerlikowske; Diana S M Buist; Berta A Geller; Tracy L Onega
Journal:  Radiology       Date:  2012-12-18       Impact factor: 11.105

10.  Baseline mammographic breast density and the risk of invasive breast cancer in postmenopausal women participating in the NSABP study of tamoxifen and raloxifene (STAR).

Authors:  Reena S Cecchini; Joseph P Costantino; Jane A Cauley; Walter M Cronin; D Lawrence Wickerham; Hanna Bandos; Joel L Weissfeld; Norman Wolmark
Journal:  Cancer Prev Res (Phila)       Date:  2012-10-11
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