Literature DB >> 28657850

Comparison of Visual Assessment of Breast Density in BI-RADS 4th and 5th Editions With Automated Volumetric Measurement.

Ji Hyun Youk1, So Jung Kim1, Eun Ju Son1, Hye Mi Gweon1, Jeong-Ah Kim1.   

Abstract

OBJECTIVE: The purpose of this study was to compare visual assessments of mammographic breast density by radiologists using BI-RADS 4th and 5th editions in correlation with automated volumetric breast density measurements.
MATERIALS AND METHODS: A total of 337 consecutive full-field digital mammographic examinations with standard views were retrospectively assessed by two radiologists for mammographic breast density according to BI-RADS 4th and 5th editions. Fully automated measurement of the volume of fibroglandular tissue and total breast and percentage breast density was performed with a commercially available software program. Interobserver and intraobserver agreement was assessed with kappa statistics. The distributions of breast density categories for both editions of BI-RADS were compared and correlated with volumetric data.
RESULTS: Interobserver agreement on breast density category was moderate to substantial (κ = 0.58-0.63) with use of BI-RADS 4th edition and substantial (κ = 0.63-0.66) with use of the 5th edition but without significant difference between the two editions. For intraobserver agreement between the two editions, the distributions of density category were significantly different (p < 0.0001), the proportions of dense breast increased, and the proportion of fatty breast decreased with use of the 5th edition compared with the 4th edition (p < 0.0001). All volumetric breast density data, including percentage breast density, were significantly different among density categories (p < 0.0001) and had significant correlation with visual assessment for both editions of BI-RADS (p < 0.01).
CONCLUSION: Assessment using BI-RADS 5th edition revealed a higher proportion of dense breast than assessment using BI-RADS 4th edition. Nevertheless, automated volumetric density assessment had good correlation with visual assessment for both editions of BI-RADS.

Keywords:  BI-RADS; breast density; breast imaging; computer-assisted interpretation; mammography

Mesh:

Year:  2017        PMID: 28657850     DOI: 10.2214/AJR.16.17525

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


  6 in total

1.  Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium.

Authors:  B L Sprague; K Kerlikowske; E J A Bowles; G H Rauscher; C I Lee; A N A Tosteson; D L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2019-06-01       Impact factor: 13.506

Review 2.  Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

Authors:  Aimilia Gastounioti; Shyam Desai; Vinayak S Ahluwalia; Emily F Conant; Despina Kontos
Journal:  Breast Cancer Res       Date:  2022-02-20       Impact factor: 8.408

3.  Comparison of Qualitative and Volumetric Assessments of Breast Density and Analyses of Breast Compression Parameters and Breast Volume of Women in Bahcesehir Mammography Screening Project.

Authors:  Ayşegül Akdoğan Gemici; Erkin Arıbal; Ayşe Nilüfer Özaydın; Sibel Özkan Gürdal; Beyza Özçınar; Neslihan Cabioğlu; Vahit Özmen
Journal:  Eur J Breast Health       Date:  2020-04-01

4.  Comparison of breast density assessments according to BI-RADS 4th and 5th editions and experience level.

Authors:  Aysegul Akdogan Gemici; Ersoy Bayram; Elif Hocaoglu; Ercan Inci
Journal:  Acta Radiol Open       Date:  2020-07-20

5.  Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer.

Authors:  Pendem Saikiran; Ruqiya Ramzan; Nandish S; Phani Deepika Kamineni; Arathy Mary John
Journal:  J Clin Imaging Sci       Date:  2019-10-11

6.  Evaluation of the association between mammographic density and the risk of breast cancer using Quantra software and the BI-RADS classification.

Authors:  Jian Ming Wang; Hong Guang Zhao; Tong Tong Liu; Fei Yang Wang
Journal:  Medicine (Baltimore)       Date:  2020-11-13       Impact factor: 1.817

  6 in total

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