Literature DB >> 23093428

Volumetric breast density characteristics as determined from digital mammograms.

O Alonzo-Proulx1, R A Jong, M J Yaffe.   

Abstract

In this paper we present the results of an automated and entirely reproducible algorithm that estimates the breast volume, dense tissue volume and the volumetric breast density from digital mammograms. The algorithm was applied to 55, 087 digital images (CC view only) from 15 351 individual women, acquired between 2008 and 2011 at the Sunnybrook Health Sciences Centre in Toronto, Canada. The algorithm is based on a prior calibration of the digital image signal versus tissue thickness and composition, and the thickness of the compressed breast is estimated using an empirical model that corrects the thickness readout of the mammography system as a function of compression force. The mean volumetric density and breast volumes for our study group were 30% and 687 cm(3), respectively. The left and right volumetric density and breast volume were strongly correlated, with a Pearson correlation of 0.92 and 0.91, respectively. The volumetric density decreased from 45% to 25% as age increased from 35 to 75 years, with an increase to 30% at 80 years. For a given woman, the volumetric density decreased at an average rate of -2 density percentage points per year while the breast volume increased by 2% per year.

Entities:  

Mesh:

Year:  2012        PMID: 23093428     DOI: 10.1088/0031-9155/57/22/7443

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  8 in total

1.  Radiation dose with digital breast tomosynthesis compared to digital mammography: per-view analysis.

Authors:  Gisella Gennaro; D Bernardi; N Houssami
Journal:  Eur Radiol       Date:  2017-08-17       Impact factor: 5.315

2.  Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer.

Authors:  Jeff Wang; Fumi Kato; Hiroko Yamashita; Motoi Baba; Yi Cui; Ruijiang Li; Noriko Oyama-Manabe; Hiroki Shirato
Journal:  J Digit Imaging       Date:  2017-04       Impact factor: 4.056

Review 3.  Risk-based Breast Cancer Screening: Implications of Breast Density.

Authors:  Christoph I Lee; Linda E Chen; Joann G Elmore
Journal:  Med Clin North Am       Date:  2017-07       Impact factor: 5.456

4.  Modelling mammography screening for breast cancer in the Canadian context: Modification and testing of a microsimulation model.

Authors:  Martin J Yaffe; Nicole Mittmann; Pablo Lee; Anna N A Tosteson; Amy Trentham-Dietz; Oguzhan Alagoz; Natasha K Stout
Journal:  Health Rep       Date:  2015-12       Impact factor: 4.796

Review 5.  A Review on Automatic Mammographic Density and Parenchymal Segmentation.

Authors:  Wenda He; Arne Juette; Erika R E Denton; Arnau Oliver; Robert Martí; Reyer Zwiggelaar
Journal:  Int J Breast Cancer       Date:  2015-06-11

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

7.  Volumetric breast density estimation from full-field digital mammograms: a validation study.

Authors:  Albert Gubern-Mérida; Michiel Kallenberg; Bram Platel; Ritse M Mann; Robert Martí; Nico Karssemeijer
Journal:  PLoS One       Date:  2014-01-21       Impact factor: 3.240

8.  Impact of COX2 genotype, ER status and body constitution on risk of early events in different treatment groups of breast cancer patients.

Authors:  Andrea Markkula; Maria Simonsson; Ann H Rosendahl; Alexander Gaber; Christian Ingvar; Carsten Rose; Helena Jernström
Journal:  Int J Cancer       Date:  2014-03-20       Impact factor: 7.396

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.