This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study
1
Faculty of Medical Sciences, Private University in the Principality of Liechtenstein (UFL), 9495 Triesen, Liechtenstein
2
St. Gallen Radiology Network, Cantonal Hospital of St. Gallen, 9007 St. Gallen, Switzerland
3
St. Gallen Radiology Network, Grabs Hospital, 9472 Grabs, Switzerland
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(19), 2212; https://doi.org/10.3390/diagnostics14192212 (registering DOI)
Submission received: 25 June 2024
/
Revised: 25 September 2024
/
Accepted: 29 September 2024
/
Published: 3 October 2024
Abstract
Background/Objectives: High breast density is a risk factor for breast cancer and can reduce the sensitivity of mammography. Given the influence of breast density on patient risk stratification and screening accuracy, it is crucial to monitor the prevalence of extremely dense breasts within local populations. Moreover, there is a lack of comprehensive understanding regarding breast density prevalence in Switzerland. Therefore, this study aimed to determine the prevalence of breast density in a selected Swiss population. Methods: To overcome the potential variability in breast density classifications by human readers, this study utilized commercially available deep convolutional neural network breast classification software. A retrospective analysis of mammographic images of women aged 40 years and older was performed. Results: A total of 4698 mammograms from women (58 ± 11 years) were included in this study. The highest prevalence of breast density was in category C (heterogeneously dense), which was observed in 41.5% of the cases. This was followed by category B (scattered areas of fibroglandular tissue), which accounted for 22.5%. Conclusion: Notably, extremely dense breasts (category D) were significantly more common in younger women, with a prevalence of 34%. However, this rate dropped sharply to less than 10% in women over 55 years of age.
Share and Cite
MDPI and ACS Style
Kaiser, A.V.; Zanolin-Purin, D.; Chuck, N.; Enaux, J.; Wruk, D.
Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study. Diagnostics 2024, 14, 2212.
https://doi.org/10.3390/diagnostics14192212
AMA Style
Kaiser AV, Zanolin-Purin D, Chuck N, Enaux J, Wruk D.
Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study. Diagnostics. 2024; 14(19):2212.
https://doi.org/10.3390/diagnostics14192212
Chicago/Turabian Style
Kaiser, Adergicia V., Daniela Zanolin-Purin, Natalie Chuck, Jennifer Enaux, and Daniela Wruk.
2024. "Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study" Diagnostics 14, no. 19: 2212.
https://doi.org/10.3390/diagnostics14192212
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article Access Statistics
For more information on the journal statistics, click
here.
Multiple requests from the same IP address are counted as one view.