Literature DB >> 22483607

Variability and errors when applying the BIRADS mammography classification.

Bruno Boyer1, Sandra Canale, Julia Arfi-Rouche, Quentin Monzani, Wassef Khaled, Corinne Balleyguier.   

Abstract

To standardize mammographic reporting, the American College of Radiology mammography developed the Breast Imaging Reporting and Data System (BIRADS) lexicon. However, wide variability is observed in practice in the application of the BIRADS terminology and this leads to classification errors. This review analyses the reasons for variations in BIRADS mammography, describes the types of errors made by readers with illustrated examples, and details BIRADS category 3 which is the most difficult category to use in practice.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Mesh:

Year:  2012        PMID: 22483607     DOI: 10.1016/j.ejrad.2012.02.005

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  12 in total

1.  Supine breast US: how to correlate breast lesions from prone MRI.

Authors:  Michele Telegrafo; Leonarda Rella; Amato A Stabile Ianora; Giuseppe Angelelli; Marco Moschetta
Journal:  Br J Radiol       Date:  2015-12-21       Impact factor: 3.039

2.  Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.

Authors:  Imon Banerjee; Selen Bozkurt; Emel Alkim; Hersh Sagreiya; Allison W Kurian; Daniel L Rubin
Journal:  J Biomed Inform       Date:  2019-02-23       Impact factor: 6.317

3.  Radiographers' performance in chest X-ray interpretation: the Nigerian experience.

Authors:  E U Ekpo; N O Egbe; B E Akpan
Journal:  Br J Radiol       Date:  2015-05-12       Impact factor: 3.039

4.  Impact of the digitalisation of mammography on performance parameters and breast dose in the Flemish Breast Cancer Screening Programme.

Authors:  Lore Timmermans; An De Hauwere; Klaus Bacher; Hilde Bosmans; Kim Lemmens; Luc Bleyen; Erik Van Limbergen; Patrick Martens; Andre Van Steen; Griet Mortier; Koen Van Herck; Hubert Thierens
Journal:  Eur Radiol       Date:  2014-05-10       Impact factor: 5.315

5.  Adding the power of iodinated contrast media to the credibility of mammography in breast cancer diagnosis.

Authors:  Alexandra Tsigginou; Christina Gkali; Athanasios Chalazonitis; Eleni Feida; Dimitrios Efthymios Vlachos; Flora Zagouri; Ioannis Rellias; Constantine Dimitrakakis
Journal:  Br J Radiol       Date:  2016-08-09       Impact factor: 3.039

6.  Impact of artificial intelligence in breast cancer screening with mammography.

Authors:  Lan-Anh Dang; Emmanuel Chazard; Edouard Poncelet; Teodora Serb; Aniela Rusu; Xavier Pauwels; Clémence Parsy; Thibault Poclet; Hugo Cauliez; Constance Engelaere; Guillaume Ramette; Charlotte Brienne; Sofiane Dujardin; Nicolas Laurent
Journal:  Breast Cancer       Date:  2022-06-28       Impact factor: 3.307

7.  Factors Indicating Surgical Excision in Classical Type of Lobular Neoplasia of the Breast.

Authors:  Constanze Elfgen; Christoph Tausch; Ann-Katrin Rodewald; Uwe Güth; Christoph Rageth; Vesna Bjelic-Radisic; Markus Fleisch; Claudia Kurtz; Jesus Gonzalez Diaz; Zsuzsanna Varga
Journal:  Breast Care (Basel)       Date:  2021-07-07       Impact factor: 2.268

8.  Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach.

Authors:  Steve Halligan; Douglas G Altman; Susan Mallett
Journal:  Eur Radiol       Date:  2015-01-20       Impact factor: 5.315

Review 9.  Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review.

Authors:  Dennis Jay Wong; Ziba Gandomkar; Wan-Jing Wu; Guijing Zhang; Wushuang Gao; Xiaoying He; Yunuo Wang; Warren Reed
Journal:  J Med Radiat Sci       Date:  2020-03-05

10.  A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms.

Authors:  Said Boumaraf; Xiabi Liu; Chokri Ferkous; Xiaohong Ma
Journal:  Biomed Res Int       Date:  2020-05-11       Impact factor: 3.411

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