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Artificial intelligence together with mechanical imaging in mammography

Bejnö, Anna LU orcid ; Hellgren, Gustav LU orcid ; Rodriguez-Ruiz, Alejandro ; Bakic, Predrag R. LU ; Zackrisson, Sophia LU ; Tingberg, Anders LU orcid and Dustler, Magnus LU orcid (2020) 15th International Workshop on Breast Imaging, IWBI 2020 In Proceedings of SPIE - The International Society for Optical Engineering 11513.
Abstract

Artificial intelligence (AI) applications are increasingly seeing use in breast imaging, particularly to assist in or automate the reading of mammograms. Another novel technique is mechanical imaging (MI) which estimates the relative stiffness of suspicious breast abnormalities by measuring the distribution of pressure on the compressed breast. This study investigates the feasibility of combining AI and MI information in breast imaging to provide further diagnostic information. Forty-six women recalled from screening were included in the analysis. Mammograms with findings scored on a suspiciousness scale by an AI tool, and corresponding pressure distributions were collected for each woman. The cases were divided into three groups by... (More)

Artificial intelligence (AI) applications are increasingly seeing use in breast imaging, particularly to assist in or automate the reading of mammograms. Another novel technique is mechanical imaging (MI) which estimates the relative stiffness of suspicious breast abnormalities by measuring the distribution of pressure on the compressed breast. This study investigates the feasibility of combining AI and MI information in breast imaging to provide further diagnostic information. Forty-six women recalled from screening were included in the analysis. Mammograms with findings scored on a suspiciousness scale by an AI tool, and corresponding pressure distributions were collected for each woman. The cases were divided into three groups by diagnosis; biopsy-proven cancer, biopsy-proven benign and non-biopsied, very likely benign. For all three groups, the relative increase of pressure at the location of the finding marked most suspicious by the AI software was recorded. A significant correlation between the relative pressure increase at the AI finding and the AI score was established in the group with cancer (p=0.043), but neither group of healthy women showed such a correlation. This study suggests that AI and MI indicate independent markers for breast cancer. The combination of these two methods has the potential to increase the accuracy of mammography screening, but further research is needed.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Breasts, Computer-aided detection, Deep learning, Mammography, Mechanical imaging
host publication
15th International Workshop on Breast Imaging, IWBI 2020
series title
Proceedings of SPIE - The International Society for Optical Engineering
editor
Bosmans, Hilde ; Marshall, Nicholas and Van Ongeval, Chantal
volume
11513
article number
1151320
publisher
SPIE
conference name
15th International Workshop on Breast Imaging, IWBI 2020
conference location
Leuven, Belgium
conference dates
2020-05-25 - 2020-05-27
external identifiers
  • scopus:85086141416
ISSN
0277-786X
1996-756X
ISBN
9781510638310
DOI
10.1117/12.2564107
project
Simultaneous Digital Breast Tomosynthesis and Mechanical Imaging
language
English
LU publication?
yes
id
475fcc8a-2147-4b20-9542-f2a32633d665
date added to LUP
2021-01-11 10:51:01
date last changed
2025-04-04 14:01:34
@inproceedings{475fcc8a-2147-4b20-9542-f2a32633d665,
  abstract     = {{<p>Artificial intelligence (AI) applications are increasingly seeing use in breast imaging, particularly to assist in or automate the reading of mammograms. Another novel technique is mechanical imaging (MI) which estimates the relative stiffness of suspicious breast abnormalities by measuring the distribution of pressure on the compressed breast. This study investigates the feasibility of combining AI and MI information in breast imaging to provide further diagnostic information. Forty-six women recalled from screening were included in the analysis. Mammograms with findings scored on a suspiciousness scale by an AI tool, and corresponding pressure distributions were collected for each woman. The cases were divided into three groups by diagnosis; biopsy-proven cancer, biopsy-proven benign and non-biopsied, very likely benign. For all three groups, the relative increase of pressure at the location of the finding marked most suspicious by the AI software was recorded. A significant correlation between the relative pressure increase at the AI finding and the AI score was established in the group with cancer (p=0.043), but neither group of healthy women showed such a correlation. This study suggests that AI and MI indicate independent markers for breast cancer. The combination of these two methods has the potential to increase the accuracy of mammography screening, but further research is needed.</p>}},
  author       = {{Bejnö, Anna and Hellgren, Gustav and Rodriguez-Ruiz, Alejandro and Bakic, Predrag R. and Zackrisson, Sophia and Tingberg, Anders and Dustler, Magnus}},
  booktitle    = {{15th International Workshop on Breast Imaging, IWBI 2020}},
  editor       = {{Bosmans, Hilde and Marshall, Nicholas and Van Ongeval, Chantal}},
  isbn         = {{9781510638310}},
  issn         = {{0277-786X}},
  keywords     = {{Breasts; Computer-aided detection; Deep learning; Mammography; Mechanical imaging}},
  language     = {{eng}},
  publisher    = {{SPIE}},
  series       = {{Proceedings of SPIE - The International Society for Optical Engineering}},
  title        = {{Artificial intelligence together with mechanical imaging in mammography}},
  url          = {{http://dx.doi.org/10.1117/12.2564107}},
  doi          = {{10.1117/12.2564107}},
  volume       = {{11513}},
  year         = {{2020}},
}