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Associate Professor Sophia Zackrisson, MD, PhD. Photo.

Sophia Zackrisson

Manager

Associate Professor Sophia Zackrisson, MD, PhD. Photo.

Workload reduction of digital breast tomosynthesis screening using artificial intelligence and synthetic mammography : a simulation study

Author

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

Summary, in English

Purpose: To achieve the high sensitivity of digital breast tomosynthesis (DBT), a time-consuming reading is necessary. However, synthetic mammography (SM) images, equivalent to digital mammography (DM), can be generated from DBT images. SM is faster to read and might be sufficient in many cases. We investigate using artificial intelligence (AI) to stratify examinations into reading of either SM or DBT to minimize workload and maximize accuracy. Approach: This is a retrospective study based on double-read paired DM and one-view DBT from the Malmö Breast Tomosynthesis Screening Trial. DBT examinations were analyzed with the cancer detection AI system ScreenPoint Transpara 1.7. For low-risk examinations, SM reading was simulated by assuming equality with DM reading. For high-risk examinations, the DBT reading results were used. Different combinations of single and double reading were studied. Results: By double-reading the DBT of 30% (4452/14,772) of the cases with the highest risk, and single-reading SM for the rest, 122 cancers would be detected with the same reading workload as DM double reading. That is 28% (27/95) more cancers would be detected than with DM double reading, and in total, 96% (122/127) of the cancers detectable with full DBT double reading would be found. Conclusions: In a DBT-based screening program, AI could be used to select high-risk cases where the reading of DBT is valuable, whereas SM is sufficient for low-risk cases. Substantially, more cancers could be detected compared with DM only, with only a limited increase in reading workload. Prospective studies are necessary.

Department/s

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

Publishing year

2025-11

Language

English

Publication/Series

Journal of Medical Imaging

Volume

12

Document type

Journal article

Publisher

SPIE

Topic

  • Cancer and Oncology

Keywords

  • artificial intelligence
  • breast cancer screening
  • digital breast tomosynthesis
  • synthetic mammography

Status

Published

Research group

  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö

ISBN/ISSN/Other

  • ISSN: 2329-4302