Literature DB >> 30523909

Generalized breast density metrics.

Erin E E Fowler1, Autumn Smallwood, Cassandra Miltich, Jennifer Drukteinis, Thomas A Sellers, John Heine.   

Abstract

Mammograms represent data that can inform future risk of breast cancer. Data from two case-control study populations were analyzed. Population 1 included women (N  =  180 age matched case-control pairs) with mammograms acquired with one indirect x-ray conversion mammography unit. Population 2 included women (N  =  319 age matched case-control pairs) with mammograms acquired from 6 direct x-ray conversion units. The Fourier domain was decomposed into n concentric rings (radial spatial frequency bands). The power in each ring was summarized giving a set of measures. We investigated images in raw, for presentation (processed) and calibrated representations and made comparison with the percentage of breast density (BD) determined with the operator assisted Cumulus method. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs), per standard deviation increase derived from the respective breast density distributions and 95% confidence intervals (CIs) were estimated. A measure from a lower radial frequency ring, corresponding 0.083-0.166 cycles mm-1 and BD had significant associations with risk in both populations. In Population 1, the Fourier measure produced significant associations in each representation: OR  =  1.76 (1.33, 2.32) for raw; OR  =  1.43 (1.09, 1.87) for processed; and OR  =  1.68 (1.26, 2.25) for calibrated. BD also provided significant associations in Population 1: OR  =  1.72 (1.27, 2.33). In Population 2, the Fourier measure produced significant associations for each representation as well: OR  =  1.47 (1.19, 1.80) for raw; OR  =  1.38 (1.15, 1.67) for processed; and OR  =  1.42 (1.15, 1.75) for calibrated. BD provided significant associations in Population 2: OR  =  1.43 (1.17, 1.76). Other coincident spectral regions were also predictive of case-control status. In sum, generalized breast density measures were significantly associated with breast cancer in both FFDM technologies.

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Year:  2018        PMID: 30523909      PMCID: PMC7034052          DOI: 10.1088/1361-6560/aaf307

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


  35 in total

1.  Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

Authors:  Yuanjie Zheng; Brad M Keller; Shonket Ray; Yan Wang; Emily F Conant; James C Gee; Despina Kontos
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

2.  Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

Authors:  Kathleen R Brandt; Christopher G Scott; Lin Ma; Amir P Mahmoudzadeh; Matthew R Jensen; Dana H Whaley; Fang Fang Wu; Serghei Malkov; Carrie B Hruska; Aaron D Norman; John Heine; John Shepherd; V Shane Pankratz; Karla Kerlikowske; Celine M Vachon
Journal:  Radiology       Date:  2015-12-22       Impact factor: 11.105

Review 3.  Mammographic density and risk of breast cancer.

Authors:  Norman F Boyd
Journal:  Am Soc Clin Oncol Educ Book       Date:  2013

4.  Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes.

Authors:  Norman Boyd; Lisa Martin; Anoma Gunasekara; Olga Melnichouk; Gord Maudsley; Chris Peressotti; Martin Yaffe; Salomon Minkin
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-06       Impact factor: 4.254

5.  A comparison of calibration data from full field digital mammography units for breast density measurements.

Authors:  Erin E E Fowler; Beibei Lu; John J Heine
Journal:  Biomed Eng Online       Date:  2013-11-09       Impact factor: 2.819

6.  Mammographic texture resemblance generalizes as an independent risk factor for breast cancer.

Authors:  Mads Nielsen; Celine M Vachon; Christopher G Scott; Konstantin Chernoff; Gopal Karemore; Nico Karssemeijer; Martin Lillholm; Morten A Karsdal
Journal:  Breast Cancer Res       Date:  2014-04-08       Impact factor: 6.466

7.  Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods.

Authors:  Amanda Eng; Zoe Gallant; John Shepherd; Valerie McCormack; Jingmei Li; Mitch Dowsett; Sarah Vinnicombe; Steve Allen; Isabel dos-Santos-Silva
Journal:  Breast Cancer Res       Date:  2014-09-20       Impact factor: 6.466

Review 8.  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

9.  Effective x-ray attenuation coefficient measurements from two full field digital mammography systems for data calibration applications.

Authors:  John J Heine; Jerry A Thomas
Journal:  Biomed Eng Online       Date:  2008-03-28       Impact factor: 2.819

Review 10.  Raised mammographic density: causative mechanisms and biological consequences.

Authors:  Michael J Sherratt; James C McConnell; Charles H Streuli
Journal:  Breast Cancer Res       Date:  2016-05-03       Impact factor: 6.466

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  2 in total

1.  Spatial Correlation and Breast Cancer Risk.

Authors:  Erin E E Fowler; Cassandra Hathaway; Fabryann Tillman; Robert Weinfurtner; Thomas A Sellers; John Heine
Journal:  Biomed Phys Eng Express       Date:  2019-05-22

2.  Mammographic Variation Measures, Breast Density, and Breast Cancer Risk.

Authors:  John Heine; Erin Fowler; Christopher G Scott; Matthew R Jensen; John Shepherd; Carrie B Hruska; Stacey J Winham; Kathleen R Brandt; Fang F Wu; Aaron D Norman; Vernon S Pankratz; Diana L Miglioretti; Karla Kerlikowske; Celine M Vachon
Journal:  AJR Am J Roentgenol       Date:  2021-06-23       Impact factor: 6.582

  2 in total

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