Literature DB >> 12433716

Accuracy of mammographic breast density analysis: results of formal operator training.

Sven Prevrhal1, John A Shepherd, Rebecca Smith-Bindman, Steven R Cummings, Karla Kerlikowske.   

Abstract

Mammographic density is a major risk factor for breast cancer. Breast density is not routinely quantified for research studies because present methods are time intensive and manual, and require expert training. We investigated whether individuals with and without backgrounds in radiology or medicine can achieve sufficient accuracy when compared with an expert (gold standard) reader of mammographic breast density. Nine readers (three radiologists, two non-radiology physicians, and four nonphysicians) assessed breast density on 144 digitized films (60 contralateral films of breast cancer cases and 84 controls) on a computer workstation with custom software. Readings were compared with a radiologist with training in mammography and density reading that read the same films. A correlation of r = 0.9 or higher with the gold standard reading was met by three of three radiologists, one of two nonradiology physicians, and one of four nonphysicians. Intrareader reproducibility measured as the residual sum of mean errors averaged 10% mammographic density for all readers and 9% for readers with a correlation of 0.9 or higher with the gold standard. The odds ratios associated with breast cancer when films with mammographic breast density of 50% or greater are considered "dense" ranged from 3.1 to 3.9 or a 1.9-2.4-per-population-SD increase in percentage density. Although it is advantageous to have a radiological background when quantifying mammographic density, it is not a prerequisite. Applying strict validation criteria to qualify readers to quantify mammographic breast density for research studies will enhance the chance of accurately assessing breast density and discriminating women at high and low risk of breast cancer.

Entities:  

Mesh:

Year:  2002        PMID: 12433716

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


  18 in total

1.  Use of clinical history affects accuracy of interpretive performance of screening mammography.

Authors:  Patricia A Carney; Andrea J Cook; Diana L Miglioretti; Stephen A Feig; Erin Aiello Bowles; Berta M Geller; Karla Kerlikowske; Mark Kettler; Tracy Onega; Joann G Elmore
Journal:  J Clin Epidemiol       Date:  2011-10-15       Impact factor: 6.437

2.  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

3.  Mammographic breast density patterns among a group of women in sub Saharan Africa.

Authors:  M Galukande; E Kiguli-Malwadde
Journal:  Afr Health Sci       Date:  2012-12       Impact factor: 0.927

4.  Volume of mammographic density and risk of breast cancer.

Authors:  John A Shepherd; Karla Kerlikowske; Lin Ma; Frederick Duewer; Bo Fan; Jeff Wang; Serghei Malkov; Eric Vittinghoff; Steven R Cummings
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2011-05-24       Impact factor: 4.254

5.  Reproducibility of BI-RADS breast density measures among community radiologists: a prospective cohort study.

Authors:  Mary C Spayne; Charlotte C Gard; Joan Skelly; Diana L Miglioretti; Pamela M Vacek; Berta M Geller
Journal:  Breast J       Date:  2012-05-21       Impact factor: 2.431

6.  Comparison of mammographic density assessed as volumes and areas among women undergoing diagnostic image-guided breast biopsy.

Authors:  Gretchen L Gierach; Berta M Geller; John A Shepherd; Deesha A Patel; Pamela M Vacek; Donald L Weaver; Rachael E Chicoine; Ruth M Pfeiffer; Bo Fan; Amir Pasha Mahmoudzadeh; Jeff Wang; Jason M Johnson; Sally D Herschorn; Louise A Brinton; Mark E Sherman
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-08-19       Impact factor: 4.254

7.  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

8.  Relationship of serum estrogens and metabolites with area and volume mammographic densities.

Authors:  Gretchen L Gierach; Deesha A Patel; Roni T Falk; Ruth M Pfeiffer; Berta M Geller; Pamela M Vacek; Donald L Weaver; Rachael E Chicoine; John A Shepherd; Amir Pasha Mahmoudzadeh; Jeff Wang; Bo Fan; Sally D Herschorn; Xia Xu; Timothy Veenstra; Barbara Fuhrman; Mark E Sherman; Louise A Brinton
Journal:  Horm Cancer       Date:  2015-03-11       Impact factor: 3.869

9.  Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to the time since the mammogram.

Authors:  Lusine Yaghjyan; Graham A Colditz; Bernard Rosner; Rulla M Tamimi
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-04-19       Impact factor: 4.254

10.  Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort.

Authors:  Catherine Klifa; Julio Carballido-Gamio; Lisa Wilmes; Anne Laprie; John Shepherd; Jessica Gibbs; Bo Fan; Susan Noworolski; Nola Hylton
Journal:  Magn Reson Imaging       Date:  2009-07-23       Impact factor: 2.546

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.