Literature DB >> 28121523

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

Emily F Conant1, Brad M Keller1, Lauren Pantalone1, Aimilia Gastounioti1, Elizabeth S McDonald1, Despina Kontos1.   

Abstract

Purpose To evaluate agreement between automated estimates of breast density made from standard-dose versus synthetic digital mammograms in a large cohort of women undergoing screening. Materials and Methods This study received institutional review board approval with waiver of consent. A total of 3668 negative (Breast Imaging Reporting and Data System category 1 or 2) digital breast tomosynthesis (DBT) screening examinations consecutively performed over a 4-month period at one institution for which both standard-dose and synthetic mammograms were available for analysis were retrospectively analyzed. All mammograms were acquired with a Selenia Dimensions system (Hologic, Bedford, Mass), and synthetic mammograms were generated by using the U.S. Food and Drug Administration-approved "C-View" software module. The "For Presentation" standard-dose mammograms and synthetic images were analyzed by using a fully automated algorithm. Agreement between density estimates was assessed by using Pearson correlation, linear regression, and Bland-Altman analysis. Differences were evaluated by using the paired Student t test. Results Breast percentage density (PD) estimates from synthetic and standard-dose mammograms were highly correlated (r = 0.92, P < .001), and the 95% Bland-Altman limits of agreement between PD estimates were -6.4% to 9.9%. Synthetic mammograms had PD estimates by an average of 1.7% higher than standard-dose mammograms (P < .001), with a larger disagreement by 1.56% in women with highly dense breast tissue (P < .0001). Conclusion Fully automated estimates of breast density made from synthetic mammograms are generally comparable to those made from standard-dose mammograms. This may be important, as standard two-dimensional mammographic images are increasingly being replaced by synthetic mammograms in DBT screening in an attempt to reduce radiation dose. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28121523      PMCID: PMC5452882          DOI: 10.1148/radiol.2016161286

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  34 in total

1.  Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches.

Authors:  Marta Cecilia Busana; Bianca L De Stavola; Ulla Sovio; Jingmei Li; Sue Moss; Keith Humphreys; Isabel dos-Santos-Silva
Journal:  Cancer Causes Control       Date:  2016-02-04       Impact factor: 2.506

2.  Breast-density legislation--practical considerations.

Authors:  Priscilla J Slanetz; Phoebe E Freer; Robyn L Birdwell
Journal:  N Engl J Med       Date:  2015-02-12       Impact factor: 91.245

3.  An automated approach for estimation of breast density.

Authors:  John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-11       Impact factor: 4.254

4.  Assessment of a fully automated, high-throughput mammographic density measurement tool for use with processed digital mammograms.

Authors:  A M Couwenberg; H M Verkooijen; J Li; R M Pijnappel; K R Charaghvandi; M Hartman; C H van Gils
Journal:  Cancer Causes Control       Date:  2014-06-25       Impact factor: 2.506

5.  Racial Differences in Quantitative Measures of Area and Volumetric Breast Density.

Authors:  Anne Marie McCarthy; Brad M Keller; Lauren M Pantalone; Meng-Kang Hsieh; Marie Synnestvedt; Emily F Conant; Katrina Armstrong; Despina Kontos
Journal:  J Natl Cancer Inst       Date:  2016-04-29       Impact factor: 13.506

6.  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

7.  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

8.  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

9.  Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

Authors:  Brad M Keller; Jinbo Chen; Dania Daye; Emily F Conant; Despina Kontos
Journal:  Breast Cancer Res       Date:  2015-08-25       Impact factor: 6.466

10.  Reliability of the percent density in digital mammography with a semi-automated thresholding method.

Authors:  Guiyun Sohn; Jong Won Lee; Sung Won Park; Jihoon Park; Jiyoung Woo; Hwa Jung Kim; Hee Jung Shin; Hak Hee Kim; Kyung Hae Jung; Joohon Sung; Seung Wook Lee; Byung Ho Son; Sei-Hyun Ahn
Journal:  J Breast Cancer       Date:  2014-06-27       Impact factor: 3.588

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  7 in total

1.  Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment.

Authors:  Maxine Tan; Mundher Al-Shabi; Wai Yee Chan; Leya Thomas; Kartini Rahmat; Kwan Hoong Ng
Journal:  Med Biol Eng Comput       Date:  2021-01-14       Impact factor: 2.602

2.  Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI.

Authors:  Anton Niukkanen; Otso Arponen; Aki Nykänen; Amro Masarwah; Anna Sutela; Timo Liimatainen; Ritva Vanninen; Mazen Sudah
Journal:  J Digit Imaging       Date:  2018-08       Impact factor: 4.056

3.  Breast-density assessment with hand-held ultrasound: A novel biomarker to assess breast cancer risk and to tailor screening?

Authors:  Sergio J Sanabria; Orcun Goksel; Katharina Martini; Serafino Forte; Thomas Frauenfelder; Rahel A Kubik-Huch; Marga B Rominger
Journal:  Eur Radiol       Date:  2018-03-19       Impact factor: 5.315

4.  Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis.

Authors:  Aimilia Gastounioti; Lauren Pantalone; Christopher G Scott; Eric A Cohen; Fang F Wu; Stacey J Winham; Matthew R Jensen; Andrew D A Maidment; Celine M Vachon; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2021-09-14       Impact factor: 11.105

Review 5.  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

6.  Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation.

Authors:  Raymond J Acciavatti; Eric A Cohen; Omid Haji Maghsoudi; Aimilia Gastounioti; Lauren Pantalone; Meng-Kang Hsieh; Emily F Conant; Christopher G Scott; Stacey J Winham; Karla Kerlikowske; Celine Vachon; Andrew D A Maidment; Despina Kontos
Journal:  Cancers (Basel)       Date:  2021-11-01       Impact factor: 6.639

Review 7.  Synthesized Mammography: Clinical Evidence, Appearance, and Implementation.

Authors:  Melissa A Durand
Journal:  Diagnostics (Basel)       Date:  2018-04-04
  7 in total

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