Literature DB >> 22993385

Inter- and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms.

A Redondo1, M Comas, F Macià, F Ferrer, C Murta-Nascimento, M T Maristany, E Molins, M Sala, X Castells.   

Abstract

OBJECTIVE: The aim of this study was to evaluate reader variability in screening mammograms according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) assessment and breast density categories.
METHODS: A stratified random sample of 100 mammograms was selected from a population-based breast cancer screening programme in Barcelona, Spain: 13 histopathologically confirmed breast cancers and 51 with true-negative and 36 with false-positive results. 21 expert radiologists from radiological units of breast cancer screening programmes in Catalonia, Spain, reviewed the mammography images twice within a 6-month interval. The readers described each mammography using BI-RADS assessment and breast density categories. Inter- and intraradiologist agreement was assessed using percentage of concordance and the kappa (κ) statistic.
RESULTS: Fair interobserver agreement was observed for the BI-RADS assessment [κ=0.37, 95% confidence interval (CI) 0.36-0.38]. When the categories were collapsed in terms of whether additional evaluation was required (Categories III, 0, IV, V) or not (I and II), moderate agreement was found (κ=0.53, 95% CI 0.52-0.54). Intra-observer agreement for BI-RADS assessment was moderate using all categories (κ=0.53, 95% CI 0.50-0.55) and substantial on recall (κ=0.66, 95% CI 0.63-0.70). Regarding breast density, inter- and intraradiologist agreement was substantial (κ=0.73, 95% CI 0.72-0.74 and κ=0.69, 95% CI 0.68-0.70, respectively).
CONCLUSION: We observed a substantial intra-observer agreement in the BI-RADS assessment but only moderate interobserver agreement. Both inter- and intra-observer agreement in mammographic interpretation of breast density was substantial. Advances in knowledge Educational efforts should be made to decrease radiologists' variability in BI-RADS assessment interpretation in population-based breast screening programmes.

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Year:  2012        PMID: 22993385      PMCID: PMC3500788          DOI: 10.1259/bjr/21256379

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


  17 in total

1.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment.

Authors:  W A Berg; C Campassi; P Langenberg; M J Sexton
Journal:  AJR Am J Roentgenol       Date:  2000-06       Impact factor: 3.959

2.  Reader variability in reporting breast imaging according to BI-RADS assessment categories (the Florence experience).

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:  2005-08-01       Impact factor: 4.380

3.  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

4.  BI-RADS lexicon for US and mammography: interobserver variability and positive predictive value.

Authors:  Elizabeth Lazarus; Martha B Mainiero; Barbara Schepps; Susan L Koelliker; Linda S Livingston
Journal:  Radiology       Date:  2006-03-28       Impact factor: 11.105

5.  Comparing the performance of mammography screening in the USA and the UK.

Authors:  Rebecca Smith-Bindman; Rachel Ballard-Barbash; Diana L Miglioretti; Julietta Patnick; Karla Kerlikowske
Journal:  J Med Screen       Date:  2005       Impact factor: 2.136

6.  BI-RADS categorization as a predictor of malignancy.

Authors:  S G Orel; N Kay; C Reynolds; D C Sullivan
Journal:  Radiology       Date:  1999-06       Impact factor: 11.105

7.  International comparison of performance measures for screening mammography: can it be done?

Authors:  B C Yankaskas; C N Klabunde; R Ancelle-Park; G Renner; H Wang; J Fracheboud; G Pou; J-L Bulliard
Journal:  J Med Screen       Date:  2004       Impact factor: 2.136

8.  Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting and Data System.

Authors:  K Kerlikowske; D Grady; J Barclay; S D Frankel; S H Ominsky; E A Sickles; V Ernster
Journal:  J Natl Cancer Inst       Date:  1998-12-02       Impact factor: 13.506

9.  Use of the American College of Radiology BI-RADS guidelines by community radiologists: concordance of assessments and recommendations assigned to screening mammograms.

Authors:  Constance Lehman; Sarah Holt; Susan Peacock; Emily White; Nicole Urban
Journal:  AJR Am J Roentgenol       Date:  2002-07       Impact factor: 3.959

10.  Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: findings of a systematic review.

Authors:  J Dinnes; S Moss; J Melia; R Blanks; F Song; J Kleijnen
Journal:  Breast       Date:  2001-12       Impact factor: 4.380

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  36 in total

1.  Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

Authors:  Said Pertuz; Elizabeth S McDonald; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2015-10-21       Impact factor: 11.105

2.  Breast density across a regional screening population: effects of age, ethnicity and deprivation.

Authors:  Samantha L Heller; Sue Hudson; Louise S Wilkinson
Journal:  Br J Radiol       Date:  2015-09-02       Impact factor: 3.039

3.  Intra- and interreader reproducibility of PI-RADSv2: A multireader study.

Authors:  Clayton P Smith; Stephanie A Harmon; Tristan Barrett; Leonardo K Bittencourt; Yan Mee Law; Haytham Shebel; Julie Y An; Marcin Czarniecki; Sherif Mehralivand; Mehmet Coskun; Bradford J Wood; Peter A Pinto; Joanna H Shih; Peter L Choyke; Baris Turkbey
Journal:  J Magn Reson Imaging       Date:  2018-12-21       Impact factor: 4.813

4.  Inaccurate Labels in Weakly-Supervised Deep Learning: Automatic Identification and Correction and Their Impact on Classification Performance.

Authors:  Degan Hao; Lei Zhang; Jules Sumkin; Aly Mohamed; Shandong Wu
Journal:  IEEE J Biomed Health Inform       Date:  2020-02-17       Impact factor: 5.772

5.  Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset.

Authors:  Daniela Sacchetto; Lia Morra; Silvano Agliozzo; Daniela Bernardi; Tomas Björklund; Beniamino Brancato; Patrizia Bravetti; Luca A Carbonaro; Loredana Correale; Carmen Fantò; Elisabetta Favettini; Laura Martincich; Luisella Milanesio; Sara Mombelloni; Francesco Monetti; Doralba Morrone; Marco Pellegrini; Barbara Pesce; Antonella Petrillo; Gianni Saguatti; Carmen Stevanin; Rubina M Trimboli; Paola Tuttobene; Marvi Valentini; Vincenzo Marra; Alfonso Frigerio; Alberto Bert; Francesco Sardanelli
Journal:  Eur Radiol       Date:  2015-05-01       Impact factor: 5.315

6.  Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.

Authors:  Shara I Feld; Kaitlin M Woo; Roxana Alexandridis; Yirong Wu; Jie Liu; Peggy Peissig; Adedayo A Onitilo; Jennifer Cox; C David Page; Elizabeth S Burnside
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

7.  Intercountry analysis of breast density classification using visual grading.

Authors:  Christine N Damases; Peter Hogg; Mark F McEntee
Journal:  Br J Radiol       Date:  2017-06-14       Impact factor: 3.039

8.  Characterizing Architectural Distortion in Mammograms by Linear Saliency.

Authors:  Fabián Narváez; Jorge Alvarez; Juan D Garcia-Arteaga; Jonathan Tarquino; Eduardo Romero
Journal:  J Med Syst       Date:  2016-12-22       Impact factor: 4.460

Review 9.  Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force.

Authors:  Joy Melnikow; Joshua J Fenton; Evelyn P Whitlock; Diana L Miglioretti; Meghan S Weyrich; Jamie H Thompson; Kunal Shah
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

10.  An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance.

Authors:  Yanpeng Li; Patrick C Brennan; Warwick Lee; Carolyn Nickson; Mariusz W Pietrzyk; Elaine A Ryan
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

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