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Identifying normal mammograms in a large screening population using artificial intelligence

Lång, Kristina LU ; Dustler, Magnus LU orcid ; Dahlblom, Victor LU orcid ; Åkesson, Anna LU ; Andersson, Ingvar LU and Zackrisson, Sophia LU (2020) In European Radiology
Abstract

Objectives: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. Methods: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning–based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and... (More)

Objectives: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. Methods: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning–based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). Results: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3–19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1–8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0–54.0) exams, including 7 (10.3%; 95% CI 3.1–17.5) cancers and 52 (27.8%; 95% CI 21.4–34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. Conclusions: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. Key Points: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial intelligence, Breast cancer, Mammography, Mass screening
in
European Radiology
publisher
Springer Science and Business Media B.V.
external identifiers
  • pmid:32876835
  • scopus:85090114026
ISSN
0938-7994
DOI
10.1007/s00330-020-07165-1
project
Can breast cancer screening be improved with artificial intelligence?
language
English
LU publication?
yes
id
140af394-6390-41fe-a3b7-e438191af2c1
date added to LUP
2020-09-24 15:20:13
date last changed
2025-07-27 00:35:36
@article{140af394-6390-41fe-a3b7-e438191af2c1,
  abstract     = {{<p>Objectives: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. Methods: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning–based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). Results: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3–19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1–8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0–54.0) exams, including 7 (10.3%; 95% CI 3.1–17.5) cancers and 52 (27.8%; 95% CI 21.4–34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. Conclusions: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. Key Points: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.</p>}},
  author       = {{Lång, Kristina and Dustler, Magnus and Dahlblom, Victor and Åkesson, Anna and Andersson, Ingvar and Zackrisson, Sophia}},
  issn         = {{0938-7994}},
  keywords     = {{Artificial intelligence; Breast cancer; Mammography; Mass screening}},
  language     = {{eng}},
  publisher    = {{Springer Science and Business Media B.V.}},
  series       = {{European Radiology}},
  title        = {{Identifying normal mammograms in a large screening population using artificial intelligence}},
  url          = {{http://dx.doi.org/10.1007/s00330-020-07165-1}},
  doi          = {{10.1007/s00330-020-07165-1}},
  year         = {{2020}},
}