Cargando…
Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases
OBJECTIVES: To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. METHODS: In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121532/ https://www.ncbi.nlm.nih.gov/pubmed/36917260 http://dx.doi.org/10.1007/s00330-023-09461-y |
_version_ | 1785029390710603776 |
---|---|
author | Koch, Henrik Wethe Larsen, Marthe Bartsch, Hauke Kurz, Kathinka Dæhli Hofvind, Solveig |
author_facet | Koch, Henrik Wethe Larsen, Marthe Bartsch, Hauke Kurz, Kathinka Dæhli Hofvind, Solveig |
author_sort | Koch, Henrik Wethe |
collection | PubMed |
description | OBJECTIVES: To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. METHODS: In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. RESULTS: A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. CONCLUSIONS: The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. KEY POINTS: • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-023-09461-y. |
format | Online Article Text |
id | pubmed-10121532 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-101215322023-04-23 Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases Koch, Henrik Wethe Larsen, Marthe Bartsch, Hauke Kurz, Kathinka Dæhli Hofvind, Solveig Eur Radiol Breast OBJECTIVES: To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. METHODS: In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. RESULTS: A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. CONCLUSIONS: The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. KEY POINTS: • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-023-09461-y. Springer Berlin Heidelberg 2023-03-14 2023 /pmc/articles/PMC10121532/ /pubmed/36917260 http://dx.doi.org/10.1007/s00330-023-09461-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Breast Koch, Henrik Wethe Larsen, Marthe Bartsch, Hauke Kurz, Kathinka Dæhli Hofvind, Solveig Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases |
title | Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases |
title_full | Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases |
title_fullStr | Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases |
title_full_unstemmed | Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases |
title_short | Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases |
title_sort | artificial intelligence in breastscreen norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases |
topic | Breast |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121532/ https://www.ncbi.nlm.nih.gov/pubmed/36917260 http://dx.doi.org/10.1007/s00330-023-09461-y |
work_keys_str_mv | AT kochhenrikwethe artificialintelligenceinbreastscreennorwayaretrospectiveanalysisofacancerenrichedsampleincluding1254breastcancercases AT larsenmarthe artificialintelligenceinbreastscreennorwayaretrospectiveanalysisofacancerenrichedsampleincluding1254breastcancercases AT bartschhauke artificialintelligenceinbreastscreennorwayaretrospectiveanalysisofacancerenrichedsampleincluding1254breastcancercases AT kurzkathinkadæhli artificialintelligenceinbreastscreennorwayaretrospectiveanalysisofacancerenrichedsampleincluding1254breastcancercases AT hofvindsolveig artificialintelligenceinbreastscreennorwayaretrospectiveanalysisofacancerenrichedsampleincluding1254breastcancercases |