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Who could benefit the most from using a computer-aided detection system in full-field digital mammography?

BACKGROUND: The computer-aided detection (CAD) system on mammography has the potential to assist radiologists in breast cancer screening. The purpose of this study is to evaluate the diagnostic performance of the CAD system in full-field digital mammography for detecting breast cancer when used by d...

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Autores principales: Jung, Na Young, Kang, Bong Joo, Kim, Hyeon Sook, Cha, Eun Suk, Lee, Jae Hee, Park, Chang Suk, Whang, In Young, Kim, Sung Hun, An, Yeong Yi, Choi, Jae Jeong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4046038/
https://www.ncbi.nlm.nih.gov/pubmed/24885214
http://dx.doi.org/10.1186/1477-7819-12-168
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author Jung, Na Young
Kang, Bong Joo
Kim, Hyeon Sook
Cha, Eun Suk
Lee, Jae Hee
Park, Chang Suk
Whang, In Young
Kim, Sung Hun
An, Yeong Yi
Choi, Jae Jeong
author_facet Jung, Na Young
Kang, Bong Joo
Kim, Hyeon Sook
Cha, Eun Suk
Lee, Jae Hee
Park, Chang Suk
Whang, In Young
Kim, Sung Hun
An, Yeong Yi
Choi, Jae Jeong
author_sort Jung, Na Young
collection PubMed
description BACKGROUND: The computer-aided detection (CAD) system on mammography has the potential to assist radiologists in breast cancer screening. The purpose of this study is to evaluate the diagnostic performance of the CAD system in full-field digital mammography for detecting breast cancer when used by dedicated breast radiologist (BR) and radiology resident (RR), and to reveal who could benefit the most from a CAD application. METHODS: We retrospectively chose 100 image sets from mammographies performed with CAD between June 2008 and June 2010. Thirty masses (15 benign and 15 malignant), 30 microcalcifications (15 benign and 15 malignant), and 40 normal mammography images were included. The participating radiologists consisted of 7 BRs and 13 RRs. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for total, normal plus microcalcification and normal plus mass both with and without CAD use for each reader. We compared the diagnostic performance values obtained with and without CAD use for the BR and RR groups, respectively. The reading time reviewing one set of 100 images and time reduction with CAD use for the BR and RR groups were also evaluated. RESULTS: The diagnostic performance was generally higher in the BR group than in the RR group. Sensitivity improved with CAD use in the BR and RR groups (from 81.10 to 84.29% for BR; 75.38 to 77.95% for RR). A tendency for improvement in all diagnostic performance values was observed in the BR group, whereas in the RR group, sensitivity improved but specificity, PPV, and NPV did not. None of the diagnostic performance parameters were significantly different. The mean reading time was shortened with CAD use in both the BR and RR groups (111.6 minutes to 94.3 minutes for BR; 135.5 minutes to 109.8 minutes for RR). The mean time reduction was higher for the RR than that in the BR group. CONCLUSIONS: CAD was helpful for dedicated BRs to improve their diagnostic performance and for RRs to improve the sensitivity in a screening setting. CAD could be essential for radiologists by decreasing reading time without decreasing diagnostic performance.
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spelling pubmed-40460382014-06-06 Who could benefit the most from using a computer-aided detection system in full-field digital mammography? Jung, Na Young Kang, Bong Joo Kim, Hyeon Sook Cha, Eun Suk Lee, Jae Hee Park, Chang Suk Whang, In Young Kim, Sung Hun An, Yeong Yi Choi, Jae Jeong World J Surg Oncol Research BACKGROUND: The computer-aided detection (CAD) system on mammography has the potential to assist radiologists in breast cancer screening. The purpose of this study is to evaluate the diagnostic performance of the CAD system in full-field digital mammography for detecting breast cancer when used by dedicated breast radiologist (BR) and radiology resident (RR), and to reveal who could benefit the most from a CAD application. METHODS: We retrospectively chose 100 image sets from mammographies performed with CAD between June 2008 and June 2010. Thirty masses (15 benign and 15 malignant), 30 microcalcifications (15 benign and 15 malignant), and 40 normal mammography images were included. The participating radiologists consisted of 7 BRs and 13 RRs. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for total, normal plus microcalcification and normal plus mass both with and without CAD use for each reader. We compared the diagnostic performance values obtained with and without CAD use for the BR and RR groups, respectively. The reading time reviewing one set of 100 images and time reduction with CAD use for the BR and RR groups were also evaluated. RESULTS: The diagnostic performance was generally higher in the BR group than in the RR group. Sensitivity improved with CAD use in the BR and RR groups (from 81.10 to 84.29% for BR; 75.38 to 77.95% for RR). A tendency for improvement in all diagnostic performance values was observed in the BR group, whereas in the RR group, sensitivity improved but specificity, PPV, and NPV did not. None of the diagnostic performance parameters were significantly different. The mean reading time was shortened with CAD use in both the BR and RR groups (111.6 minutes to 94.3 minutes for BR; 135.5 minutes to 109.8 minutes for RR). The mean time reduction was higher for the RR than that in the BR group. CONCLUSIONS: CAD was helpful for dedicated BRs to improve their diagnostic performance and for RRs to improve the sensitivity in a screening setting. CAD could be essential for radiologists by decreasing reading time without decreasing diagnostic performance. BioMed Central 2014-05-29 /pmc/articles/PMC4046038/ /pubmed/24885214 http://dx.doi.org/10.1186/1477-7819-12-168 Text en Copyright © 2014 Jung et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Jung, Na Young
Kang, Bong Joo
Kim, Hyeon Sook
Cha, Eun Suk
Lee, Jae Hee
Park, Chang Suk
Whang, In Young
Kim, Sung Hun
An, Yeong Yi
Choi, Jae Jeong
Who could benefit the most from using a computer-aided detection system in full-field digital mammography?
title Who could benefit the most from using a computer-aided detection system in full-field digital mammography?
title_full Who could benefit the most from using a computer-aided detection system in full-field digital mammography?
title_fullStr Who could benefit the most from using a computer-aided detection system in full-field digital mammography?
title_full_unstemmed Who could benefit the most from using a computer-aided detection system in full-field digital mammography?
title_short Who could benefit the most from using a computer-aided detection system in full-field digital mammography?
title_sort who could benefit the most from using a computer-aided detection system in full-field digital mammography?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4046038/
https://www.ncbi.nlm.nih.gov/pubmed/24885214
http://dx.doi.org/10.1186/1477-7819-12-168
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