Behrens A, Fasching P, Schwenke E, Gaß P, Häberle L, Heindl F, Heusinger K, Lotz L, Lubrich H, Preuß C, Schneider M, Schulz-Wendtland R, Stumpfe F, Uder M, Wunderle M, Zahn A, Hack C, Beckmann M, Emons J (2023)
Publication Type: Journal article
Publication year: 2023
Book Volume: 28
Article Number: 384
Journal Issue: 1
DOI: 10.1186/s40001-023-01327-9
Background: High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments. Methods: We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models. Results: Gray level bins and PMD correlated to a certain extent. Spearman’s ρ ranged from − 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R 2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms. Conclusions: In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).
APA:
Behrens, A., Fasching, P., Schwenke, E., Gaß, P., Häberle, L., Heindl, F.,... Emons, J. (2023). Predicting mammographic density with linear ultrasound transducers. European Journal of Medical Research, 28(1). https://doi.org/10.1186/s40001-023-01327-9
MLA:
Behrens, Annika, et al. "Predicting mammographic density with linear ultrasound transducers." European Journal of Medical Research 28.1 (2023).
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