Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk

Abstract We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo—DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists’ visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from...

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Autores principales:	Paolo Giorgi Rossi, Olivera Djuric, Valerie Hélin, Susan Astley, Paola Mantellini, Andrea Nitrosi, Elaine F. Harkness, Emilien Gauthier, Donella Puliti, Corinne Balleyguier, Camille Baron, Fiona J. Gilbert, André Grivegnée, Pierpaolo Pattacini, Stefan Michiels, Suzette Delaloge
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ae46de7556848ca9eefd8ac6c0d1149
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spelling	oai:doaj.org-article:2ae46de7556848ca9eefd8ac6c0d11492021-12-02T18:37:10ZValidation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk10.1038/s41598-021-99433-32045-2322https://doaj.org/article/2ae46de7556848ca9eefd8ac6c0d11492021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99433-3https://doaj.org/toc/2045-2322Abstract We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo—DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists’ visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48–55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5–4.4) and a 3.6 (95% CI 1.4–9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists’ visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580–0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623–0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists’ visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity.Paolo Giorgi RossiOlivera DjuricValerie HélinSusan AstleyPaola MantelliniAndrea NitrosiElaine F. HarknessEmilien GauthierDonella PulitiCorinne BalleyguierCamille BaronFiona J. GilbertAndré GrivegnéePierpaolo PattaciniStefan MichielsSuzette DelalogeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paolo Giorgi Rossi Olivera Djuric Valerie Hélin Susan Astley Paola Mantellini Andrea Nitrosi Elaine F. Harkness Emilien Gauthier Donella Puliti Corinne Balleyguier Camille Baron Fiona J. Gilbert André Grivegnée Pierpaolo Pattacini Stefan Michiels Suzette Delaloge Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk
description	Abstract We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo—DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists’ visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48–55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5–4.4) and a 3.6 (95% CI 1.4–9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists’ visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580–0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623–0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists’ visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity.
format	article
author	Paolo Giorgi Rossi Olivera Djuric Valerie Hélin Susan Astley Paola Mantellini Andrea Nitrosi Elaine F. Harkness Emilien Gauthier Donella Puliti Corinne Balleyguier Camille Baron Fiona J. Gilbert André Grivegnée Pierpaolo Pattacini Stefan Michiels Suzette Delaloge
author_facet	Paolo Giorgi Rossi Olivera Djuric Valerie Hélin Susan Astley Paola Mantellini Andrea Nitrosi Elaine F. Harkness Emilien Gauthier Donella Puliti Corinne Balleyguier Camille Baron Fiona J. Gilbert André Grivegnée Pierpaolo Pattacini Stefan Michiels Suzette Delaloge
author_sort	Paolo Giorgi Rossi
title	Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk
title_short	Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk
title_full	Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk
title_fullStr	Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk
title_full_unstemmed	Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk
title_sort	validation of a new fully automated software for 2d digital mammographic breast density evaluation in predicting breast cancer risk
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2ae46de7556848ca9eefd8ac6c0d1149
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Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk

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