Assessing mammographic density change within individuals across screening rounds using deep learning–based software
(2025) In Journal of Medical Imaging 12.- Abstract
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.... (More)
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.
(Less)
- author
- Olinder, Jakob
LU
; Förnvik, Daniel
LU
; Dahlblom, Victor
LU
; Lu, Viktor
LU
; Åkesson, Anna
LU
; Johnson, Kristin
LU
and Zackrisson, Sophia
LU
- organization
- publishing date
- 2025-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- breast cancer risk, breast cancer screening, breast density, deep learning, longitudinal trends, mammography
- in
- Journal of Medical Imaging
- volume
- 12
- article number
- S22017
- publisher
- SPIE
- external identifiers
-
- pmid:40823522
- scopus:105015380487
- ISSN
- 2329-4302
- DOI
- 10.1117/1.JMI.12.S2.S22017
- language
- English
- LU publication?
- yes
- id
- 8a19f04a-07f9-4c03-9fc0-74d405315af3
- date added to LUP
- 2025-10-03 13:30:54
- date last changed
- 2025-10-17 15:25:03
@article{8a19f04a-07f9-4c03-9fc0-74d405315af3,
abstract = {{<p>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.</p>}},
author = {{Olinder, Jakob and Förnvik, Daniel and Dahlblom, Victor and Lu, Viktor and Åkesson, Anna and Johnson, Kristin and Zackrisson, Sophia}},
issn = {{2329-4302}},
keywords = {{breast cancer risk; breast cancer screening; breast density; deep learning; longitudinal trends; mammography}},
language = {{eng}},
publisher = {{SPIE}},
series = {{Journal of Medical Imaging}},
title = {{Assessing mammographic density change within individuals across screening rounds using deep learning–based software}},
url = {{http://dx.doi.org/10.1117/1.JMI.12.S2.S22017}},
doi = {{10.1117/1.JMI.12.S2.S22017}},
volume = {{12}},
year = {{2025}},
}