Literature DB >> 26632060

Vision 20/20: Mammographic breast density and its clinical applications.

Kwan-Hoong Ng1, Susie Lau1.   

Abstract

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are "dense" and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

Entities:  

Mesh:

Year:  2015        PMID: 26632060     DOI: 10.1118/1.4935141

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  14 in total

1.  Breast tissue density change after oophorectomy in BRCA mutation carrier patients using visual and volumetric analysis.

Authors:  Augustin Lecler; Ariane Dunant; Suzette Delaloge; Delphine Wehrer; Tania Moussa; Olivier Caron; Corinne Balleyguier
Journal:  Br J Radiol       Date:  2018-01-05       Impact factor: 3.039

Review 2.  Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic.

Authors:  Emily F Conant; Brian L Sprague; Despina Kontos
Journal:  Radiology       Date:  2018-02       Impact factor: 11.105

3.  Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures.

Authors:  Emily F Conant; Brad M Keller; Lauren Pantalone; Aimilia Gastounioti; Elizabeth S McDonald; Despina Kontos
Journal:  Radiology       Date:  2017-01-25       Impact factor: 11.105

4.  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

5.  Milk intake and mammographic density in premenopausal women.

Authors:  Yunan Han; Xiaoyu Zong; Yize Li; Graham A Colditz; Adetunji T Toriola
Journal:  Breast Cancer Res Treat       Date:  2018-11-20       Impact factor: 4.872

Review 6.  Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad.

Authors:  Stamatia Destounis; Andrea Arieno; Renee Morgan; Christina Roberts; Ariane Chan
Journal:  Diagnostics (Basel)       Date:  2017-05-31

Review 7.  Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.

Authors:  Aimilia Gastounioti; Emily F Conant; Despina Kontos
Journal:  Breast Cancer Res       Date:  2016-09-20       Impact factor: 6.466

8.  Mammographic compression in Asian women.

Authors:  Susie Lau; Yang Faridah Abdul Aziz; Kwan Hoong Ng
Journal:  PLoS One       Date:  2017-04-18       Impact factor: 3.240

9.  Automated volumetric breast density estimation out of digital breast tomosynthesis data: feasibility study of a new software version.

Authors:  Youichi Machida; Ai Saita; Hirofumi Namba; Eisuke Fukuma
Journal:  Springerplus       Date:  2016-06-18

10.  Breast density and impacts on health.

Authors:  Cheryl Cruwys; JoAnn Pushkin
Journal:  Ecancermedicalscience       Date:  2017-08-08
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