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Can artificial intelligence reduce the interval cancer rate in mammography screening?

Lång, Kristina LU ; Hofvind, Solveig ; Rodriguez-Ruiz, Alejandro and Andersson, Ingvar LU (2021) In European Radiology 31(8). p.5940-5947
Abstract
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... (More)
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. (Less)
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
European Radiology
volume
31
issue
8
pages
5940 - 5947
publisher
Springer
external identifiers
  • scopus:85099911510
  • pmid:33486604
ISSN
0938-7994
DOI
10.1007/s00330-021-07686-3
language
English
LU publication?
yes
id
914f6e4e-8901-4086-b66e-0371069d31b0
date added to LUP
2021-01-31 20:44:28
date last changed
2022-05-12 18:03:49
@article{914f6e4e-8901-4086-b66e-0371069d31b0,
  abstract     = {{Objectives<br>
To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening.<br>
Materials and methods<br>
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.<br>
Results<br>
A statistically significant correlation between interval cancer classification groups and AI risk score was observed (p &lt; .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).<br>
Conclusion<br>
The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities.}},
  author       = {{Lång, Kristina and Hofvind, Solveig and Rodriguez-Ruiz, Alejandro and Andersson, Ingvar}},
  issn         = {{0938-7994}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{8}},
  pages        = {{5940--5947}},
  publisher    = {{Springer}},
  series       = {{European Radiology}},
  title        = {{Can artificial intelligence reduce the interval cancer rate in mammography screening?}},
  url          = {{http://dx.doi.org/10.1007/s00330-021-07686-3}},
  doi          = {{10.1007/s00330-021-07686-3}},
  volume       = {{31}},
  year         = {{2021}},
}