Literature DB >> 28613915

Intercountry analysis of breast density classification using visual grading.

Christine N Damases1,2, Peter Hogg3, Mark F McEntee1.   

Abstract

OBJECTIVE: Disagreement in mammographic breast density (MBD) assessment can impact breast cancer risk stratification, choices of further breast cancer screening intervals and pathways. This study examines whether intercountry MBD expectations and assessment approaches are associated with differences in MBD assessment.
METHODS: 20 American Board of Radiology (ABR) examiners and 24 UK practitioners using the 4th edition BI-RADS® lexicon assessed 40 mammogram cases of 20 females. 26 Royal Australian and New Zealand College of Radiologists (RANZCR) registered radiologists also assessed the same cases. Interobserver correlation and agreement were assessed using Spearman's correlation (ρ) and weighted kappa (κw), respectively.
RESULTS: Strong positive correlation was observed between the study cohorts on a binary scale (1-2 vs 3-4) [ABR examiners and RANZCR radiologists (ρ = 0.950); ABR examiners and UK practitioners (ρ = 0.940); and RANZCR radiologists and UK practitioners (ρ = 0.958)]. ABR and RANZCR radiologists demonstrated slight agreement [κw = 0.10; 95% confidence interval (CI) = -1.13-0.43], whereas ABR and UK practitioners showed a fair agreement [κw = 0.25; 95% CI = -0.42-0.61], and an almost perfect agreement was observed between RANZCR radiologists and UK practitioners [κw = 0.95; 95% CI = 0.91-0.97].
CONCLUSION: Findings demonstrate wide international and interobserver variability in MBD assessment. This level of variability underscores the need for automation and standardization of MBD assessment. Advances in knowledge: Intercountry analysis of MBD assessment shows variations, with less variation on the binary scale than on the 4-point scale. With this level of variation, automation and standardization of MBD assessment becomes more appropriate.

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Year:  2017        PMID: 28613915      PMCID: PMC5603951          DOI: 10.1259/bjr.20170064

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  35 in total

1.  From the laboratory to the clinic: the "prevalence effect".

Authors:  David Gur; Howard E Rockette; Thomas Warfel; Joan M Lacomis; Carl R Fuhrman
Journal:  Acad Radiol       Date:  2003-11       Impact factor: 3.173

2.  An observational study to evaluate the performance of units using two radiographers to read screening mammograms.

Authors:  R L Bennett; S J Sellars; R G Blanks; S M Moss
Journal:  Clin Radiol       Date:  2011-11-08       Impact factor: 2.350

3.  Interobserver agreement in breast radiological density attribution according to BI-RADS quantitative classification.

Authors:  D Bernardi; M Pellegrini; S Di Michele; P Tuttobene; C Fantò; M Valentini; M Gentilini; S Ciatto
Journal:  Radiol Med       Date:  2012-01-07       Impact factor: 3.469

4.  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

5.  A software tool for increased efficiency in observer performance studies in radiology.

Authors:  Sara Börjesson; Markus Håkansson; Magnus Båth; Susanne Kheddache; Sune Svensson; Anders Tingberg; Anna Grahn; Mark Ruschin; Bengt Hemdal; Sören Mattsson; Lars Gunnar Månsson
Journal:  Radiat Prot Dosimetry       Date:  2005       Impact factor: 0.972

6.  The effect of abnormality-prevalence expectation on expert observer performance and visual search.

Authors:  Warren M Reed; John T Ryan; Mark F McEntee; Michael G Evanoff; Patrick C Brennan
Journal:  Radiology       Date:  2011-01-19       Impact factor: 11.105

7.  Impact of the new density reporting laws: radiologist perceptions and actual behavior.

Authors:  David Gur; Amy H Klym; Jill L King; Andriy I Bandos; Jules H Sumkin
Journal:  Acad Radiol       Date:  2015-03-30       Impact factor: 3.173

8.  Inter-observer variability in mammographic density assessment using Royal Australian and New Zealand College of Radiologists (RANZCR) synoptic scales.

Authors:  Christine N Damases; Claudia Mello-Thoms; Mark F McEntee
Journal:  J Med Imaging Radiat Oncol       Date:  2016-04-05       Impact factor: 1.735

9.  Misclassification of Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density and Implications for Breast Density Reporting Legislation.

Authors:  Charlotte C Gard; Erin J Aiello Bowles; Diana L Miglioretti; Stephen H Taplin; Carolyn M Rutter
Journal:  Breast J       Date:  2015-07-01       Impact factor: 2.431

Review 10.  Predictors of interobserver agreement in breast imaging using the Breast Imaging Reporting and Data System.

Authors:  Anna Liza M Antonio; Catherine M Crespi
Journal:  Breast Cancer Res Treat       Date:  2010-02-21       Impact factor: 4.872

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  3 in total

1.  Automated mammographic density measurement using Quantra™: comparison with the Royal Australian and New Zealand College of Radiology synoptic scale.

Authors:  Inez Yeo; Judith Akwo; Ernest Ekpo
Journal:  J Med Imaging (Bellingham)       Date:  2020-05-29

2.  Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.

Authors:  Veronica Magni; Matteo Interlenghi; Andrea Cozzi; Marco Alì; Christian Salvatore; Alcide A Azzena; Davide Capra; Serena Carriero; Gianmarco Della Pepa; Deborah Fazzini; Giuseppe Granata; Caterina B Monti; Giulia Muscogiuri; Giuseppe Pellegrino; Simone Schiaffino; Isabella Castiglioni; Sergio Papa; Francesco Sardanelli
Journal:  Radiol Artif Intell       Date:  2022-03-16

3.  Subjective Versus Quantitative Methods of Assessing Breast Density.

Authors:  Wijdan Alomaim; Desiree O'Leary; John Ryan; Louise Rainford; Michael Evanoff; Shane Foley
Journal:  Diagnostics (Basel)       Date:  2020-05-21
  3 in total

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