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Can artificial intelligence reduce the interval cancer rate in mammography screening?
OBJECTIVES: To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. MATERIALS AND METHODS: Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learni...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270858/ https://www.ncbi.nlm.nih.gov/pubmed/33486604 http://dx.doi.org/10.1007/s00330-021-07686-3 |
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author | Lång, Kristina Hofvind, Solveig Rodríguez-Ruiz, Alejandro Andersson, Ingvar |
author_facet | Lång, Kristina Hofvind, Solveig Rodríguez-Ruiz, Alejandro Andersson, Ingvar |
author_sort | Lång, Kristina |
collection | PubMed |
description | OBJECTIVES: To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. MATERIALS AND METHODS: Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning–based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates. RESULTS: A statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9–23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5–14.5) and 4.7% (95% CI 3.0–7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12–39). CONCLUSION: The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities. KEY POINTS: • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities. • AI could potentially reduce a proportion of particularly aggressive interval cancers. • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative. |
format | Online Article Text |
id | pubmed-8270858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-82708582021-07-20 Can artificial intelligence reduce the interval cancer rate in mammography screening? Lång, Kristina Hofvind, Solveig Rodríguez-Ruiz, Alejandro Andersson, Ingvar Eur Radiol Breast OBJECTIVES: To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. MATERIALS AND METHODS: Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning–based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates. RESULTS: A statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9–23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5–14.5) and 4.7% (95% CI 3.0–7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12–39). CONCLUSION: The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities. KEY POINTS: • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities. • AI could potentially reduce a proportion of particularly aggressive interval cancers. • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative. Springer Berlin Heidelberg 2021-01-23 2021 /pmc/articles/PMC8270858/ /pubmed/33486604 http://dx.doi.org/10.1007/s00330-021-07686-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Lång, Kristina Hofvind, Solveig Rodríguez-Ruiz, Alejandro Andersson, Ingvar Can artificial intelligence reduce the interval cancer rate in mammography screening? |
title | Can artificial intelligence reduce the interval cancer rate in mammography screening? |
title_full | Can artificial intelligence reduce the interval cancer rate in mammography screening? |
title_fullStr | Can artificial intelligence reduce the interval cancer rate in mammography screening? |
title_full_unstemmed | Can artificial intelligence reduce the interval cancer rate in mammography screening? |
title_short | Can artificial intelligence reduce the interval cancer rate in mammography screening? |
title_sort | can artificial intelligence reduce the interval cancer rate in mammography screening? |
topic | Breast |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270858/ https://www.ncbi.nlm.nih.gov/pubmed/33486604 http://dx.doi.org/10.1007/s00330-021-07686-3 |
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