Literature DB >> 12202727

Does training in the Breast Imaging Reporting and Data System (BI-RADS) improve biopsy recommendations or feature analysis agreement with experienced breast imagers at mammography?

Wendie A Berg1, Carl J D'Orsi, Valerie P Jackson, Lawrence W Bassett, Craig A Beam, Rebecca S Lewis, Philip E Crewson.   

Abstract

PURPOSE: To determine whether training in the Breast Imaging Reporting and Data System (BI-RADS) improves observer performance and agreement with the consensus of experienced breast imagers with regard to mammographic feature analysis and final assessment.
MATERIALS AND METHODS: A test set of mammograms was developed, with 54 proven lesions consisting of 28 masses (nine [32%] malignancies) and 26 microcalcifications (10 [38%] malignancies). Three experienced breast imagers reviewed cases independently and by means of consensus. Twenty-three practicing mammogram-interpreting physicians reviewed mammograms before and after a day's lectures on BI-RADS. Observer performance before and after training was measured by means of agreement (kappa) with consensus description and assessments, rate of biopsy of malignant and benign lesions, and areas under receiver operating characteristic (ROC) curves. Performance was also measured for 11 participants 2-3 months after training.
RESULTS: Improved agreement with consensus feature analysis was found for mass margins and/or asymmetries, with a pretraining generalized kappa value of 0.36 and a posttraining generalized kappa value of 0.41. Similar improvement was seen for description of calcification morphology (pretraining kappa value of 0.36 improving to 0.44 after training). No improvement was seen in describing calcification distribution. Final assessments were more consistent after training, with a pretraining kappa value of 0.31, as compared with 0.45 after training. The mean biopsy rate for malignant lesions improved from 73% (range, 53%-89%) before training to 88% (range, 74%-100%) after training, with minimal increase in mean biopsy rate of benign lesions (43% [range, 26%-60%] before to 51% [range, 31%-63%] after training), and no net change in area under the ROC curve, as compared with histopathologic findings. For the subset of participants with delayed follow-up, no significant decline in posttraining results was seen.
CONCLUSION: BI-RADS training resulted in improved agreement with the consensus of experienced breast imagers for feature analysis and final assessment. It is important that trainees showed improved rates of recommending biopsy for malignant lesions. This effect was maintained over 2-3 months. Copyright RSNA, 2002

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Year:  2002        PMID: 12202727     DOI: 10.1148/radiol.2243011626

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


  45 in total

1.  The positive predictive value of the breast imaging reporting and data system (BI-RADS) as a method of quality assessment in breast imaging in a hospital population.

Authors:  Harmine M Zonderland; Thomas L Pope; Arend J Nieborg
Journal:  Eur Radiol       Date:  2004-07-09       Impact factor: 5.315

2.  The importance of standardized interpretation of molecular breast imaging with dedicated gamma cameras.

Authors:  Orazio Schillaci
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-06       Impact factor: 9.236

3.  Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents.

Authors:  Swatee Singh; Jeff Maxwell; Jay A Baker; Jennifer L Nicholas; Joseph Y Lo
Journal:  Radiology       Date:  2010-10-22       Impact factor: 11.105

4.  Impact of an educational intervention designed to reduce unnecessary recall during screening mammography.

Authors:  Patricia A Carney; Linn Abraham; Andrea Cook; Stephen A Feig; Edward A Sickles; Diana L Miglioretti; Berta M Geller; Bonnie C Yankaskas; Joann G Elmore
Journal:  Acad Radiol       Date:  2012-06-23       Impact factor: 3.173

5.  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

6.  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 7.  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

8.  Observer variability in screen-film mammography versus full-field digital mammography with soft-copy reading.

Authors:  Per Skaane; Felix Diekmann; Corinne Balleyguier; Susanne Diekmann; Jean-Charles Piguet; Kari Young; Michael Abdelnoor; Loren Niklason
Journal:  Eur Radiol       Date:  2008-02-27       Impact factor: 5.315

9.  Computer-assisted mammography feedback program (CAMFP) an electronic tool for continuing medical education.

Authors:  Nicole Urban; Gary M Longton; Andrea D Crowe; Mariann J Drucker; Constance D Lehman; Susan Peacock; Kimberly A Lowe; Steve B Zeliadt; Marcia A Gaul
Journal:  Acad Radiol       Date:  2007-09       Impact factor: 3.173

10.  "Hippocrates-mst": a prototype for computer-aided microcalcification analysis and risk assessment for breast cancer.

Authors:  George Spyrou; Smaragda Kapsimalakou; Antonis Frigas; Konstantinos Koufopoulos; Stamatios Vassilaros; Panos Ligomenides
Journal:  Med Biol Eng Comput       Date:  2006-10-27       Impact factor: 2.602

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