Literature DB >> 25763719

Breast density: clinical implications and assessment methods.

Nicole S Winkler1, Sughra Raza, Meaghan Mackesy, Robyn L Birdwell.   

Abstract

Breast density assessment is an important component of the screening mammography report and conveys information to referring clinicians about mammographic sensitivity and the relative risk for developing breast cancer. These topics have gained substantial attention because of recent legislation in several states that requires patients to be informed of dense breast tissue and the potential for associated breast cancer risk and decreased mammographic sensitivity. Because of the considerable implications of diagnosing a woman with dense breast tissue, radiologists should strive to be as consistent as possible when assessing breast density. Commonly used methods of breast density assessment range from subjective visual estimation to quantitative calculations of area and volume density percentages made with complex computer algorithms. The basic principles of currently available commercial methods of calculating fibroglandular density are described and illustrated. There is no criterion standard for determining breast density, but understanding the pros and cons of the various assessment methods will allow radiologists to make informed decisions. Radiologists should understand the basic factors involved in breast density assessment, the changes related to density assessment described in the fifth edition of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon, and the capabilities of currently available software. Online supplemental material is available for this article. (©)RSNA, 2015.

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Year:  2015        PMID: 25763719     DOI: 10.1148/rg.352140134

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  19 in total

1.  Differential diagnosis of breast masses in South Korean premenopausal women using diffuse optical spectroscopic imaging.

Authors:  Anaïs Leproux; You Me Kim; Jun Won Min; Christine E McLaren; Wen-Pin Chen; Thomas D O'Sullivan; Seung-Ha Lee; Phil-Sang Chung; Bruce J Tromberg
Journal:  J Biomed Opt       Date:  2016-07-01       Impact factor: 3.170

Review 2.  A review of mammographic positioning image quality criteria for the craniocaudal projection.

Authors:  Rhonda-Joy I Sweeney; Sarah J Lewis; Peter Hogg; Mark F McEntee
Journal:  Br J Radiol       Date:  2017-12-05       Impact factor: 3.039

Review 3.  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

4.  Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement.

Authors:  Philip Teare; Michael Fishman; Oshra Benzaquen; Eyal Toledano; Eldad Elnekave
Journal:  J Digit Imaging       Date:  2017-08       Impact factor: 4.056

Review 5.  Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients.

Authors:  Victoria Chernyak; Kathryn J Fowler; Aya Kamaya; Ania Z Kielar; Khaled M Elsayes; Mustafa R Bashir; Yuko Kono; Richard K Do; Donald G Mitchell; Amit G Singal; An Tang; Claude B Sirlin
Journal:  Radiology       Date:  2018-09-25       Impact factor: 11.105

Review 6.  The Role of Imaging in Health Screening: Screening for Specific Conditions.

Authors:  David H Ballard; Kirsteen R Burton; Nikita Lakomkin; Shannon Kim; Prabhakar Rajiah; Midhir J Patel; Parisa Mazaheri; Gary J Whitman
Journal:  Acad Radiol       Date:  2020-05-11       Impact factor: 3.173

7.  Persistent inter-observer variability of breast density assessment using BI-RADS® 5th edition guidelines.

Authors:  Leah H Portnow; Dianne Georgian-Smith; Irfanullah Haider; Mirelys Barrios; Camden P Bay; Kerrie P Nelson; Sughra Raza
Journal:  Clin Imaging       Date:  2021-12-10       Impact factor: 1.605

8.  Circulating carotenoids and breast cancer among high-risk individuals.

Authors:  Cheng Peng; Chi Gao; Donghao Lu; Bernard A Rosner; Oana Zeleznik; Susan E Hankinson; Peter Kraft; A Heather Eliassen; Rulla M Tamimi
Journal:  Am J Clin Nutr       Date:  2021-03-11       Impact factor: 8.472

9.  Relationships between mammographic density, tissue microvessel density, and breast biopsy diagnosis.

Authors:  Ashley S Felix; Petra Lenz; Ruth M Pfeiffer; Stephen M Hewitt; Jennifer Morris; Deesha A Patel; Berta Geller; Pamela M Vacek; Donald L Weaver; Rachael E Chicoine; John Shepherd; Amir Pasha Mahmoudzadeh; Jeff Wang; Bo Fan; Serghei Malkov; Sally D Herschorn; Jason M Johnson; Renata L Cora; Louise A Brinton; Mark E Sherman; Gretchen L Gierach
Journal:  Breast Cancer Res       Date:  2016-08-23       Impact factor: 6.466

10.  Mammographic Breast Density Evaluation in Korean Women Using Fully Automated Volumetric Assessment.

Authors:  Inyoung Youn; SeonHyeong Choi; Shin Ho Kook; Yoon Jung Choi
Journal:  J Korean Med Sci       Date:  2016-02-23       Impact factor: 2.153

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