Literature DB >> 23465381

Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures.

Brad M Keller1, Diane L Nathan, Sara C Gavenonis, Jinbo Chen, Emily F Conant, Despina Kontos.   

Abstract

RATIONALE AND
OBJECTIVES: Mammographic breast density, a strong risk factor for breast cancer, may be measured as either a relative percentage of dense (ie, radiopaque) breast tissue or as an absolute area from either raw (ie, "for processing") or vendor postprocessed (ie, "for presentation") digital mammograms. Given the increasing interest in the incorporation of mammographic density in breast cancer risk assessment, the purpose of this study is to determine the inherent reader variability in breast density assessment from raw and vendor-processed digital mammograms, because inconsistent estimates could to lead to misclassification of an individual woman's risk for breast cancer.
MATERIALS AND METHODS: Bilateral, mediolateral-oblique view, raw, and processed digital mammograms of 81 women were retrospectively collected for this study (N = 324 images). Mammographic percent density and absolute dense tissue area estimates for each image were obtained from two radiologists using a validated, interactive software tool.
RESULTS: The variability of interreader agreement was not found to be affected by the image presentation style (ie, raw or processed, F-test: P > .5). Interreader estimates of relative and absolute breast density are strongly correlated (Pearson r > 0.84, P < .001) but systematically different (t-test, P < .001) between the two readers.
CONCLUSION: Our results show that mammographic density may be assessed with equal reliability from either raw or vendor postprocessed images. Furthermore, our results suggest that the primary source of density variability comes from the subjectivity of the individual reader in assessing the absolute amount of dense tissue present in the breast, indicating the need to use standardized tools to mitigate this effect.
Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2013        PMID: 23465381      PMCID: PMC3673702          DOI: 10.1016/j.acra.2013.01.003

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  30 in total

Review 1.  ABC of breast diseases. Breast cancer-epidemiology, risk factors, and genetics.

Authors:  K McPherson; C M Steel; J M Dixon
Journal:  BMJ       Date:  2000-09-09

2.  Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories.

Authors:  S Ciatto; N Houssami; A Apruzzese; E Bassetti; B Brancato; F Carozzi; S Catarzi; M P Lamberini; G Marcelli; R Pellizzoni; B Pesce; G Risso; F Russo; A Scorsolini
Journal:  Breast       Date:  2005-08       Impact factor: 4.380

3.  Volume of mammographic density and risk of breast cancer.

Authors:  John A Shepherd; Karla Kerlikowske; Lin Ma; Frederick Duewer; Bo Fan; Jeff Wang; Serghei Malkov; Eric Vittinghoff; Steven R Cummings
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2011-05-24       Impact factor: 4.254

4.  Statistical methods for assessing agreement between two methods of clinical measurement.

Authors:  J M Bland; D G Altman
Journal:  Lancet       Date:  1986-02-08       Impact factor: 79.321

5.  Women's features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN).

Authors:  Beatriz Pérez-Gómez; Franciso Ruiz; Inmaculada Martínez; María Casals; Josefa Miranda; Carmen Sánchez-Contador; Carmen Vidal; Rafael Llobet; Marina Pollán; Dolores Salas
Journal:  Breast Cancer Res Treat       Date:  2011-11-01       Impact factor: 4.872

6.  Quantitative assessment of percent breast density: analog versus digital acquisition.

Authors:  Jennifer A Harvey
Journal:  Technol Cancer Res Treat       Date:  2004-12

7.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually.

Authors:  M H Gail; L A Brinton; D P Byar; D K Corle; S B Green; C Schairer; J J Mulvihill
Journal:  J Natl Cancer Inst       Date:  1989-12-20       Impact factor: 13.506

8.  Breast percent density: estimation on digital mammograms and central tomosynthesis projections.

Authors:  Predrag R Bakic; Ann-Katherine Carton; Despina Kontos; Cuiping Zhang; Andrea B Troxel; Andrew D A Maidment
Journal:  Radiology       Date:  2009-05-06       Impact factor: 11.105

9.  Reproducibility of visual assessment on mammographic density.

Authors:  Jinnan Gao; Ruth Warren; Helen Warren-Forward; John F Forbes
Journal:  Breast Cancer Res Treat       Date:  2007-07-07       Impact factor: 4.872

10.  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
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  14 in total

1.  Consistency of visual assessments of mammographic breast density from vendor-specific "for presentation" images.

Authors:  Mohamed Abdolell; Kaitlyn Tsuruda; Christopher B Lightfoot; Eva Barkova; Melanie McQuaid; Judy Caines; Sian E Iles
Journal:  J Med Imaging (Bellingham)       Date:  2015-10-30

2.  Automated mammographic breast density estimation using a fully convolutional network.

Authors:  Juhun Lee; Robert M Nishikawa
Journal:  Med Phys       Date:  2018-02-19       Impact factor: 4.071

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.  Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.

Authors:  Abra M Jeffers; Weiva Sieh; Jafi A Lipson; Joseph H Rothstein; Valerie McGuire; Alice S Whittemore; Daniel L Rubin
Journal:  Radiology       Date:  2016-09-05       Impact factor: 11.105

5.  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

6.  Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations.

Authors:  Aimilia Gastounioti; Andrew Oustimov; Brad M Keller; Lauren Pantalone; Meng-Kang Hsieh; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2016-11       Impact factor: 4.071

7.  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

8.  Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications.

Authors:  Erin E E Fowler; Celine M Vachon; Christopher G Scott; Thomas A Sellers; John J Heine
Journal:  Acad Radiol       Date:  2014-08       Impact factor: 3.173

9.  Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction.

Authors:  Aimilia Gastounioti; Christine Damases Kasi; Christopher G Scott; Kathleen R Brandt; Matthew R Jensen; Carrie B Hruska; Fang F Wu; Aaron D Norman; Emily F Conant; Stacey J Winham; Karla Kerlikowske; Despina Kontos; Celine M Vachon
Journal:  Radiology       Date:  2020-05-12       Impact factor: 11.105

10.  The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006.

Authors:  Lin Chen; Shonket Ray; Brad M Keller; Said Pertuz; Elizabeth S McDonald; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2016-03-22       Impact factor: 11.105

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