Literature DB >> 10845521

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

W A Berg1, C Campassi, P Langenberg, M J Sexton.   

Abstract

OBJECTIVE: We sought to evaluate the use of the Breast Imaging Reporting and Data System (BI-RADS) standardized mammography lexicon among and within observers and to distinguish variability in feature analysis from variability in lesion management.
MATERIALS AND METHODS: Five experienced mammographers, not specifically trained in BI-RADS, used the lexicon to describe and assess 103 screening mammograms, including 30 (29%) showing cancer, and a subset of 86 mammograms with diagnostic evaluation, including 23 (27%) showing cancer. A subset of 13 screening mammograms (two with malignant findings, 11 with diagnostic evaluation) were rereviewed by each observer 2 months later. Kappa statistics were calculated as measures of agreement beyond chance.
RESULTS: After diagnostic evaluation, the interobserver kappa values for describing features were as follows: breast density, 0.43; lesion type, 0.75; mass borders, 0.40; special cases, 0.56; mass density, 0.40; mass shape, 0.28; microcalcification morphology, 0.36; and microcalcification distribution, 0.47. Lesion management was highly variable, with a kappa value for final assessment of 0.37. When we grouped assessments recommending immediate additional evaluation and biopsy (BI-RADS categories 0, 4, and 5 combined) versus follow-up (categories 1, 2, and 3 combined), five observers agreed on management for only 47 (55%) of 86 lesions. Intraobserver agreement on management (additional evaluation or biopsy versus follow-up) was seen in 47 (85%) of 55 interpretations, with a kappa value of 0.35-1.0 (mean, 0.60) for final assessment.
CONCLUSION: Inter- and intraobserver variability in mammographic interpretation is substantial for both feature analysis and management. Continued development of methods to improve standardization in mammographic interpretation is needed.

Entities:  

Mesh:

Year:  2000        PMID: 10845521     DOI: 10.2214/ajr.174.6.1741769

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  83 in total

1.  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

2.  External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

Authors:  Matthias Benndorf; Elizabeth S Burnside; Christoph Herda; Mathias Langer; Elmar Kotter
Journal:  Med Phys       Date:  2015-08       Impact factor: 4.071

3.  The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions.

Authors:  Mehmet U S Ayvaci; Oguzhan Alagoz; Elizabeth S Burnside
Journal:  Manuf Serv Oper Manag       Date:  2012-04       Impact factor: 7.600

4.  Lexicon for standardized interpretation of gamma camera molecular breast imaging: observer agreement and diagnostic accuracy.

Authors:  Amy Lynn Conners; Carrie B Hruska; Cindy L Tortorelli; Robert W Maxwell; Deborah J Rhodes; Judy C Boughey; Wendie A Berg
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-06       Impact factor: 9.236

Review 5.  Applications and literature review of the BI-RADS classification.

Authors:  S Obenauer; K P Hermann; E Grabbe
Journal:  Eur Radiol       Date:  2005-01-26       Impact factor: 5.315

6.  Conceptual approach for the design of radiology reporting interfaces: the talking template.

Authors:  Chris L Sistrom
Journal:  J Digit Imaging       Date:  2005-09       Impact factor: 4.056

7.  The influence of statin use on breast density.

Authors:  Denise M Boudreau; Carolyn M Rutter; Diana S M Buist
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2006-05       Impact factor: 4.254

8.  Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Authors:  Songfeng Li; Jun Wei; Heang-Ping Chan; Mark A Helvie; Marilyn A Roubidoux; Yao Lu; Chuan Zhou; Lubomir M Hadjiiski; Ravi K Samala
Journal:  Phys Med Biol       Date:  2018-01-09       Impact factor: 3.609

9.  Heart murmurs recorded by a sensor based electronic stethoscope and e-mailed for remote assessment.

Authors:  L B Dahl; P Hasvold; E Arild; T Hasvold
Journal:  Arch Dis Child       Date:  2002-10       Impact factor: 3.791

10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.