Literature DB >> 19423260

Intraobserver interpretation of breast ultrasonography following the BI-RADS classification.

M J G Calas1, R M V R Almeida, B Gutfilen, W C A Pereira.   

Abstract

PURPOSE: To use the BI-RADS ultrasound classification in an intraobserver retrospective study of the interpretation of breast images.
MATERIALS AND METHODS: The study used 40 breast ultrasound images recorded in orthogonal planes, obtained from patients with an indication for surgery. Eight professionals experienced in breast imaging analysis retrospectively reviewed these lesions, in three rounds of image interpretation (with a 3-6 months interval between rounds). Observers had no access to information from medical records or histopathological results, and, without their knowledge, in each new round were assigned the same images previously interpreted by them. Fleiss-modified Kappa measures were the study main concordance index. Besides the BI-RADS, a scale grouping its categories 2-3 and 4-5 was also used. The statistical analysis concerned the intraobserver agreement.
RESULTS: Kappa values ranged from 0.37 to 0.75 (original categories) and from 0.73 to 0.87 (grouped categories). Overall, out of the 8 observers, 7 presented moderate to substantial concordance (Kappa values 0.51 to 0.74).
CONCLUSION: The BI-RADS is a reporting tool that provides a standardized terminology for US exams. In this study, moderate to substantial concordance in Kappa values was found, in agreement with other studies of the literature. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

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Mesh:

Year:  2009        PMID: 19423260     DOI: 10.1016/j.ejrad.2009.04.015

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


  11 in total

1.  Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network.

Authors:  Ruoyun Liu; Shichong Zhou; Yi Guo; Yuanyuan Wang; Cai Chang
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

2.  Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

Authors:  Kadayanallur Mahadevan Prabusankarlal; Palanisamy Thirumoorthy; Radhakrishnan Manavalan
Journal:  J Med Imaging (Bellingham)       Date:  2017-06-16

3.  A Retrospective Comparative Study of Image-Guided Excisional Biopsy in High-Risk Non-Palpable Breast Lesions: Predictive Factors for Malignancy.

Authors:  Nidal İflazoğlu; Orhan Üreyen; Murat Kemal Atahan; Ulvi Mehmet Meral; Gülten Sezgin; Ercüment Tarcan
Journal:  J Breast Health       Date:  2015-07-01

4.  Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound.

Authors:  Qiucheng Wang; He Chen; Gongning Luo; Bo Li; Haitao Shang; Hua Shao; Shanshan Sun; Zhongshuai Wang; Kuanquan Wang; Wen Cheng
Journal:  Eur Radiol       Date:  2022-04-30       Impact factor: 7.034

5.  Preliminary study of the technical limitations of automated breast ultrasound: from procedure to diagnosis.

Authors:  Maria Julia Gregório Calas; Fernanda Philadelpho Arantes Pereira; Leticia Pereira Gonçalves; Flávia Paiva Proença Lobo Lopes
Journal:  Radiol Bras       Date:  2020 Sep-Oct

6.  Breast ultrasound diagnostic performance and outcomes for mass lesions using Breast Imaging Reporting and Data System category 0 mammogram.

Authors:  Paulo Almazy Zanello; Andre Felipe Cica Robim; Tatiane Mendes Gonçalves de Oliveira; Jorge Elias Junior; Jurandyr Moreira de Andrade; Carlos Ribeiro Monteiro; Joaquim Moraes Sarmento Filho; Helio Humberto Angotti Carrara; Valdair Francisco Muglia
Journal:  Clinics (Sao Paulo)       Date:  2011       Impact factor: 2.365

7.  Interobserver concordance in the BI-RADS classification of breast ultrasound exams.

Authors:  Maria Julia G Calas; Renan M V R Almeida; Bianca Gutfilen; Wagner C A Pereira
Journal:  Clinics (Sao Paulo)       Date:  2012       Impact factor: 2.365

8.  Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses.

Authors:  Laith R Sultan; Ghizlane Bouzghar; Benjamin J Levenback; Nauroze A Faizi; Santosh S Venkatesh; Emily F Conant; Chandra M Sehgal
Journal:  Adv Breast Cancer Res       Date:  2015-01-09

9.  Interobserver and Intraobserver Agreement of Sonographic BIRADS Lexicon in the Assessment of Breast Masses.

Authors:  Eda Elverici; Betul Zengin; Ayse Nurdan Barca; Pinar Didem Yilmaz; Aysegul Alimli; Levent Araz
Journal:  Iran J Radiol       Date:  2013-08-30       Impact factor: 0.212

10.  A Simple Ultrasound Based Classification Algorithm Allows Differentiation of Benign from Malignant Breast Lesions by Using Only Quantitative Parameters.

Authors:  Panagiotis Kapetas; Ramona Woitek; Paola Clauser; Maria Bernathova; Katja Pinker; Thomas H Helbich; Pascal A Baltzer
Journal:  Mol Imaging Biol       Date:  2018-12       Impact factor: 3.488

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