Literature DB >> 28565960

Comparison of subjective and fully automated methods for measuring mammographic density.

Nataliia Moshina1, Marta Roman1, Sofie Sebuødegård1, Gunvor G Waade2, Giske Ursin1,3,4, Solveig Hofvind1,2.   

Abstract

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (kw). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was kw = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Entities:  

Keywords:  BI-RADS density categories; Digital mammography; automated volumetric breast density measurements; breast cancer screening; mammographic density classification

Mesh:

Year:  2017        PMID: 28565960     DOI: 10.1177/0284185117712540

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  5 in total

Review 1.  A review of the influence of mammographic density on breast cancer clinical and pathological phenotype.

Authors:  Michael S Shawky; Cecilia W Huo; Kara Britt; Erik W Thompson; Michael A Henderson; Andrew Redfern
Journal:  Breast Cancer Res Treat       Date:  2019-06-08       Impact factor: 4.872

2.  Performance measures among non-immigrants and immigrants attending BreastScreen Norway: a population-based screening programme.

Authors:  Sameer Bhargava; Lars Andreas Akslen; Ida Rashida Khan Bukholm; Solveig Hofvind
Journal:  Eur Radiol       Date:  2019-02-14       Impact factor: 5.315

3.  Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment.

Authors:  Si Eun Lee; Nak-Hoon Son; Myung Hyun Kim; Eun-Kyung Kim
Journal:  J Digit Imaging       Date:  2022-01-11       Impact factor: 4.056

4.  Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.

Authors:  Veronica Magni; Matteo Interlenghi; Andrea Cozzi; Marco Alì; Christian Salvatore; Alcide A Azzena; Davide Capra; Serena Carriero; Gianmarco Della Pepa; Deborah Fazzini; Giuseppe Granata; Caterina B Monti; Giulia Muscogiuri; Giuseppe Pellegrino; Simone Schiaffino; Isabella Castiglioni; Sergio Papa; Francesco Sardanelli
Journal:  Radiol Artif Intell       Date:  2022-03-16

5.  Screening mammography: benefit of double reading by breast density.

Authors:  My von Euler-Chelpin; Martin Lillholm; George Napolitano; Ilse Vejborg; Mads Nielsen; Elsebeth Lynge
Journal:  Breast Cancer Res Treat       Date:  2018-07-04       Impact factor: 4.872

  5 in total

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