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Portrait of Daniel Förnvik. Photo

Daniel Förnvik

Associate professor

Portrait of Daniel Förnvik. Photo

Assessing mammographic density change within individuals across screening rounds using deep learning–based software

Author

  • Jakob Olinder
  • Daniel Förnvik
  • Victor Dahlblom
  • Viktor Lu
  • Anna Åkesson
  • Kristin Johnson
  • Sophia Zackrisson

Summary, in English

Purpose: The purposes are to evaluate the change in mammographic density within individuals across screening rounds using automatic density software, to evaluate whether a change in breast density is associated with a future breast cancer diagnosis, and to provide insight into breast density evolution. Approach: Mammographic breast density was analyzed in women screened in Malmö, Sweden, between 2010 and 2015 who had undergone at least two consecutive screening rounds <30 months apart. The volumetric and area-based densities were measured with deep learning–based software and fully automated software, respectively. The change in volumetric breast density percentage (VBD%) between two consecutive screening examinations was determined. Multiple linear regression was used to investigate the association between VBD% change in percentage points and future breast cancer, as well as the initial VBD%, adjusting for age group and the time between examinations. Examinations with potential positioning issues were removed in a sensitivity analysis. Results: In 26,056 included women, the mean VBD% decreased from 10.7% [95% confidence interval (CI) 10.6 to 10.8] to 10.3% (95% CI: 10.2 to 10.3) (p < 0.001) between the two examinations. The decline in VBD% was more pronounced in women with initially denser breasts (adjusted β ¼ −0.10, p < 0.001) and less pronounced in women with a future breast cancer diagnosis (adjusted β ¼ 0.16, p ¼ 0.02). Conclusions: The demonstrated density changes over time support the potential of using breast density change in risk assessment tools and provide insights for future risk-based screening.

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

Publishing year

2025-11

Language

English

Publication/Series

Journal of Medical Imaging

Volume

12

Document type

Journal article

Publisher

SPIE

Topic

  • Radiology and Medical Imaging

Keywords

  • breast cancer risk
  • breast cancer screening
  • breast density
  • deep learning
  • longitudinal trends
  • mammography

Status

Published

Research group

  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö

ISBN/ISSN/Other

  • ISSN: 2329-4302