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Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm

Visual assessments of mammographic breast density by radiologists are used in clinical practice; however, these assessments have shown weaker associations with breast cancer risk than area-based, quantitative methods. The purpose of this study is to present a statistical evaluation of a fully automa...

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Autores principales: Abdolell, Mohamed, Tsuruda, Kaitlyn, Schaller, Gerry, Caines, Judy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662119/
https://www.ncbi.nlm.nih.gov/pubmed/23737861
http://dx.doi.org/10.1155/2013/651091
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author Abdolell, Mohamed
Tsuruda, Kaitlyn
Schaller, Gerry
Caines, Judy
author_facet Abdolell, Mohamed
Tsuruda, Kaitlyn
Schaller, Gerry
Caines, Judy
author_sort Abdolell, Mohamed
collection PubMed
description Visual assessments of mammographic breast density by radiologists are used in clinical practice; however, these assessments have shown weaker associations with breast cancer risk than area-based, quantitative methods. The purpose of this study is to present a statistical evaluation of a fully automated, area-based mammographic density measurement algorithm. Five radiologists estimated density in 5% increments for 138 “For Presentation” single MLO views; the median of the radiologists' estimates was used as the reference standard. Agreement amongst radiologists was excellent, ICC = 0.884, 95% CI (0.854, 0.910). Similarly, the agreement between the algorithm and the reference standard was excellent, ICC = 0.862, falling within the 95% CI of the radiologists' estimates. The Bland-Altman plot showed that the reference standard was slightly positively biased (+1.86%) compared to the algorithm-generated densities. A scatter plot showed that the algorithm moderately overestimated low densities and underestimated high densities. A box plot showed that 95% of the algorithm-generated assessments fell within one BI-RADS category of the reference standard. This study demonstrates the effective use of several statistical techniques that collectively produce a comprehensive evaluation of the algorithm and its potential to provide mammographic density measures that can be used to inform clinical practice.
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spelling pubmed-36621192013-06-04 Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm Abdolell, Mohamed Tsuruda, Kaitlyn Schaller, Gerry Caines, Judy Comput Math Methods Med Research Article Visual assessments of mammographic breast density by radiologists are used in clinical practice; however, these assessments have shown weaker associations with breast cancer risk than area-based, quantitative methods. The purpose of this study is to present a statistical evaluation of a fully automated, area-based mammographic density measurement algorithm. Five radiologists estimated density in 5% increments for 138 “For Presentation” single MLO views; the median of the radiologists' estimates was used as the reference standard. Agreement amongst radiologists was excellent, ICC = 0.884, 95% CI (0.854, 0.910). Similarly, the agreement between the algorithm and the reference standard was excellent, ICC = 0.862, falling within the 95% CI of the radiologists' estimates. The Bland-Altman plot showed that the reference standard was slightly positively biased (+1.86%) compared to the algorithm-generated densities. A scatter plot showed that the algorithm moderately overestimated low densities and underestimated high densities. A box plot showed that 95% of the algorithm-generated assessments fell within one BI-RADS category of the reference standard. This study demonstrates the effective use of several statistical techniques that collectively produce a comprehensive evaluation of the algorithm and its potential to provide mammographic density measures that can be used to inform clinical practice. Hindawi Publishing Corporation 2013 2013-05-08 /pmc/articles/PMC3662119/ /pubmed/23737861 http://dx.doi.org/10.1155/2013/651091 Text en Copyright © 2013 Mohamed Abdolell et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Abdolell, Mohamed
Tsuruda, Kaitlyn
Schaller, Gerry
Caines, Judy
Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
title Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
title_full Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
title_fullStr Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
title_full_unstemmed Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
title_short Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
title_sort statistical evaluation of a fully automated mammographic breast density algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662119/
https://www.ncbi.nlm.nih.gov/pubmed/23737861
http://dx.doi.org/10.1155/2013/651091
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