The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Sophia Zackrisson, MD, PhD. Photo.

Sophia Zackrisson

Manager

Sophia Zackrisson, MD, PhD. Photo.

Correspondence between areas causing recall in breast cancer screening and artificial intelligence findings

Author

  • Victor Dahlblom
  • Anders Tingberg
  • Sophia Zackrisson
  • Magnus Dustler

Editor

  • Hilde Bosmans
  • Nicholas Marshall
  • Chantal Van Ongeval

Summary, in English

False positive recall is a major issue in breast cancer screening and the introduction of artificial intelligence (AI) might affect which women who are unnecessarily recalled. We have investigated how an AI system works on false positive recalls at screening and compared with radiologist findings. Two-view digital mammography (DM) examinations from 656 recalled women (136 with screening detected cancer), were analysed with a commercial AI system. The AI findings were matched with the areas on the images causing the recalls. The agreement was studied both at the examination level and for individual findings. Scores were compared between true positive and false positive recalls. ROC analysis was used to study the AI-system's ability to distinguish between true and false positive recalls. It was also studied how the AI system performed on cases where there were discordant readings. AI identified the same areas as radiologists in 80% of the cases recalled on DM. For true positives both the proportion of matching areas and AI scores were higher than for false positive recalls. The AI system also had a relatively large AUC (0.83) for differentiating between false positive recalls and cancers. Further, the AI system identified most of the findings leading to recall in cases where only one of the readers had marked the case for discussion. There is a relatively large agreement between the AI system and radiologists. The AI system scores the false positives lower than true positives. AI complements a single reader in a way similar to a second reader.

Department/s

  • Radiology Diagnostics, Malmö
  • LUCC: Lund University Cancer Centre
  • Medical Radiation Physics, Malmö
  • LTH Profile Area: Photon Science and Technology
  • EpiHealth: Epidemiology for Health

Publishing year

2022

Language

English

Publication/Series

Proceedings of SPIE - The International Society for Optical Engineering

Volume

12286

Document type

Conference paper

Publisher

SPIE

Topic

  • Cancer and Oncology

Keywords

  • artificial intelligence
  • breast cancer screening
  • False positive recalls

Conference name

16th International Workshop on Breast Imaging, IWBI 2022

Conference date

2022-05-22 - 2022-05-25

Conference place

Leuven, Belgium

Status

Published

Research group

  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö

ISBN/ISSN/Other

  • ISSN: 1996-756X
  • ISSN: 0277-786X
  • ISBN: 9781510655843