Literature DB >> 15894652

Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies.

Gabriela Torres-Mejía1, Bianca De Stavola, Diane S Allen, Juan J Pérez-Gavilán, Jorge M Ferreira, Ian S Fentiman, Isabel Dos Santos Silva.   

Abstract

Mammographic features are known to be associated with breast cancer but the magnitude of the effect differs markedly from study to study. Methods to assess mammographic features range from subjective qualitative classifications to computer-automated quantitative measures. We used data from the UK Guernsey prospective studies to examine the relative value of these methods in predicting breast cancer risk. In all, 3,211 women ages > or =35 years who had a mammogram taken in 1986 to 1989 were followed-up to the end of October 2003, with 111 developing breast cancer during this period. Mammograms were classified using the subjective qualitative Wolfe classification and several quantitative mammographic features measured using computer-based techniques. Breast cancer risk was positively associated with high-grade Wolfe classification, percent breast density and area of dense tissue, and negatively associated with area of lucent tissue, fractal dimension, and lacunarity. Inclusion of the quantitative measures in the same model identified area of dense tissue and lacunarity as the best predictors of breast cancer, with risk increasing by 59% [95% confidence interval (95% CI), 29-94%] per SD increase in total area of dense tissue but declining by 39% (95% CI, 53-22%) per SD increase in lacunarity, after adjusting for each other and for other confounders. Comparison of models that included both the qualitative Wolfe classification and these two quantitative measures to models that included either the qualitative or the two quantitative variables showed that they all made significant contributions to prediction of breast cancer risk. These findings indicate that breast cancer risk is affected not only by the amount of mammographic density but also by the degree of heterogeneity of the parenchymal pattern and, presumably, by other features captured by the Wolfe classification.

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Year:  2005        PMID: 15894652     DOI: 10.1158/1055-9965.EPI-04-0717

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


  60 in total

Review 1.  Breast tissue composition and susceptibility to breast cancer.

Authors:  Norman F Boyd; Lisa J Martin; Michael Bronskill; Martin J Yaffe; Neb Duric; Salomon Minkin
Journal:  J Natl Cancer Inst       Date:  2010-07-08       Impact factor: 13.506

2.  Mammographic density change with 1 year of aerobic exercise among postmenopausal women: a randomized controlled trial.

Authors:  Christy G Woolcott; Kerry S Courneya; Norman F Boyd; Martin J Yaffe; Tim Terry; Anne McTiernan; Rollin Brant; Rachel Ballard-Barbash; Melinda L Irwin; Charlotte A Jones; Sony Brar; Kristin L Campbell; Margaret L McNeely; Kristina H Karvinen; Christine M Friedenreich
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-03-23       Impact factor: 4.254

3.  Methods for assessing and representing mammographic density: an analysis of 4 case-control studies.

Authors:  Christy G Woolcott; Shannon M Conroy; Chisato Nagata; Giske Ursin; Celine M Vachon; Martin J Yaffe; Ian S Pagano; Celia Byrne; Gertraud Maskarinec
Journal:  Am J Epidemiol       Date:  2013-10-11       Impact factor: 4.897

4.  Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.

Authors:  Qi Guo; Jiaqing Shao; Virginie F Ruiz
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

5.  Cumulative sum quality control for calibrated breast density measurements.

Authors:  John J Heine; Ke Cao; Craig Beam
Journal:  Med Phys       Date:  2009-12       Impact factor: 4.071

6.  Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment.

Authors:  Despina Kontos; Lynda C Ikejimba; Predrag R Bakic; Andrea B Troxel; Emily F Conant; Andrew D A Maidment
Journal:  Radiology       Date:  2011-07-19       Impact factor: 11.105

7.  Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.

Authors:  Aimilia Gastounioti; Andrew Oustimov; Meng-Kang Hsieh; Lauren Pantalone; Emily F Conant; Despina Kontos
Journal:  Acad Radiol       Date:  2018-02-01       Impact factor: 3.173

8.  An automated approach for estimation of breast density.

Authors:  John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-11       Impact factor: 4.254

9.  Mammographic density and markers of socioeconomic status: a cross-sectional study.

Authors:  Zoe Aitken; Kate Walker; Bernardine H Stegeman; Petra A Wark; Sue M Moss; Valerie A McCormack; Isabel dos Santos Silva
Journal:  BMC Cancer       Date:  2010-02-09       Impact factor: 4.430

Review 10.  Mammographic density.

Authors:  Norman F Boyd; Lisa J Martin; Martin Yaffe; Salomon Minkin
Journal:  Breast Cancer Res       Date:  2009-12-18       Impact factor: 6.466

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