Identifying normal mammograms in a large screening population using artificial intelligence
Lång, Kristina LU ; Dustler, Magnus LU
; Dahlblom, Victor
LU
; 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.
(Less)
- author
- Lång, Kristina
LU
; Dustler, Magnus
LU
; Dahlblom, Victor
LU
; Åkesson, Anna
LU
; Andersson, Ingvar
LU
and Zackrisson, Sophia
LU
- organization
- publishing date
- 2020
- 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
-
- scopus:85090114026
- pmid:32876835
- 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
- 2025-08-08 12:19:18
@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}},
}