Literature DB >> 18483328

Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: case-control study.

Jane Ding1, Ruth Warren, Iqbal Warsi, Nick Day, Deborah Thompson, Michael Brady, Christopher Tromans, Ralph Highnam, Douglas Easton.   

Abstract

Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfe's parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.82], 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.00-1.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method.

Entities:  

Mesh:

Year:  2008        PMID: 18483328     DOI: 10.1158/1055-9965.EPI-07-2634

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


  38 in total

1.  Mammographic density and risk of breast cancer by adiposity: an analysis of four case-control studies.

Authors:  Shannon M Conroy; Christy G Woolcott; Karin R Koga; Celia Byrne; Chisato Nagata; Giske Ursin; Celine M Vachon; Martin J Yaffe; Ian Pagano; Gertraud Maskarinec
Journal:  Int J Cancer       Date:  2011-09-17       Impact factor: 7.396

2.  A novel automated mammographic density measure and breast cancer risk.

Authors:  John J Heine; Christopher G Scott; Thomas A Sellers; Kathleen R Brandt; Daniel J Serie; Fang-Fang Wu; Marilyn J Morton; Beth A Schueler; Fergus J Couch; Janet E Olson; V Shane Pankratz; Celine M Vachon
Journal:  J Natl Cancer Inst       Date:  2012-07-03       Impact factor: 13.506

3.  Detection and identification of potential biomarkers of breast cancer.

Authors:  Yuxia Fan; Jiachen Wang; Yang Yang; Qiuliang Liu; Yingzhong Fan; Jiekai Yu; Shu Zheng; Mengquan Li; Jiaxiang Wang
Journal:  J Cancer Res Clin Oncol       Date:  2010-03-17       Impact factor: 4.553

Review 4.  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

5.  Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study.

Authors:  Huanjun Ding; Sabee Molloi
Journal:  Phys Med Biol       Date:  2012-07-06       Impact factor: 3.609

6.  Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

Authors:  Said Pertuz; Elizabeth S McDonald; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2015-10-21       Impact factor: 11.105

7.  The effect of change in body mass index on volumetric measures of mammographic density.

Authors:  Vicki Hart; Katherine W Reeves; Susan R Sturgeon; Nicholas G Reich; Lynnette Leidy Sievert; Karla Kerlikowske; Lin Ma; John Shepherd; Jeffrey A Tice; Amir Pasha Mahmoudzadeh; Serghei Malkov; Brian L Sprague
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2015-08-27       Impact factor: 4.254

8.  Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: automated measurement development for full field digital mammography.

Authors:  E E Fowler; T A Sellers; B Lu; J J Heine
Journal:  Med Phys       Date:  2013-11       Impact factor: 4.071

9.  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

10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.