Literature DB >> 20544272

Predicting breast cancer risk using mammographic density measurements from both mammogram sides and views.

Jennifer Stone1, Jane Ding, Ruth M L Warren, Stephen W Duffy.   

Abstract

Mammographic density is a strong risk factor for breast cancer. Which and how many x-rays are used for research, and how mammographic density is measured varies across studies. In this article, we compared three different measurements (absolute dense area, percent dense area and percent dense volume) from each of four mammograms [left, right, medio-lateral oblique (MLO) and cranio-caudal (CC) views] using three different methods of measurement [computer-assisted thresholding, visual assessment and standard mammogram form (SMF)] to investigate whether additional measurements and/or different methods of measurement provide more information in the prediction of breast cancer risk. Mammographic density was measured in all four mammograms from 318 cases and 899 age-matched controls combined from the Cambridge and Norwich Breast Screening Programmes. Measurements were averaged across various combinations of mammogram type and/or view. Conditional logistic regression was used to estimate odds ratios associated with increasing quintiles of each mammographic measure. Overall, there appeared to be no difference in the fit of the models using two or four mammograms compared to the models using just the contralateral MLO or CC mammogram (all P > 0.07) for all methods of measurement. Common practice of measuring just the contralateral MLO or CC mammogram for analysis in case-control studies investigating the association between mammographic density and breast cancer risk appears to be sufficient.

Entities:  

Mesh:

Year:  2010        PMID: 20544272     DOI: 10.1007/s10549-010-0976-y

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  11 in total

1.  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

2.  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

3.  Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures.

Authors:  Brad M Keller; Diane L Nathan; Sara C Gavenonis; Jinbo Chen; Emily F Conant; Despina Kontos
Journal:  Acad Radiol       Date:  2013-03-05       Impact factor: 3.173

4.  Breast fat and breast cancer.

Authors:  Andreas Pettersson; Rulla M Tamimi
Journal:  Breast Cancer Res Treat       Date:  2012-08-02       Impact factor: 4.872

5.  Do fatty breasts increase or decrease breast cancer risk?

Authors:  John A Shepherd; Karla Kerlikowske
Journal:  Breast Cancer Res       Date:  2012-01-25       Impact factor: 6.466

6.  Using mammographic density to predict breast cancer risk: dense area or percentage dense area.

Authors:  Jennifer Stone; Jane Ding; Ruth Ml Warren; Stephen W Duffy; John L Hopper
Journal:  Breast Cancer Res       Date:  2010-11-18       Impact factor: 6.466

7.  Nondense mammographic area and risk of breast cancer.

Authors:  Andreas Pettersson; Susan E Hankinson; Walter C Willett; Pagona Lagiou; Dimitrios Trichopoulos; Rulla M Tamimi
Journal:  Breast Cancer Res       Date:  2011-10-21       Impact factor: 6.466

8.  Density is in the eye of the beholder: visual versus semi-automated assessment of breast density on standard mammograms.

Authors:  M B I Lobbes; J P M Cleutjens; V Lima Passos; C Frotscher; M J Lahaye; K B M I Keymeulen; R G Beets-Tan; J Wildberger; C Boetes
Journal:  Insights Imaging       Date:  2011-11-20

9.  Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer.

Authors:  Rikke Rass Winkel; My von Euler-Chelpin; Mads Nielsen; Pengfei Diao; Michael Bachmann Nielsen; Wei Yao Uldall; Ilse Vejborg
Journal:  BMC Cancer       Date:  2015-04-12       Impact factor: 4.430

10.  Statistical evaluation of a fully automated mammographic breast density algorithm.

Authors:  Mohamed Abdolell; Kaitlyn Tsuruda; Gerry Schaller; Judy Caines
Journal:  Comput Math Methods Med       Date:  2013-05-08       Impact factor: 2.238

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