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Behrens, Annika; Fasching, Peter A.; Schwenke, Eva; Gass, Paul; Häberle, Lothar; Heindl, Felix; Heusinger, Katharina; Lotz, Laura; Lubrich, Hannah; Preuß, Caroline; Schneider, Michael O.; Schulz-Wendtland, Rüdiger; Stumpfe, Florian M.; Uder, Michael; Wunderle, Marius; Zahn, Anna L.; Hack, Carolin C.; Beckmann, Matthias W.; Emons, Julius

Predicting mammographic density with linear ultrasound transducers

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  • Media type: E-Article
  • Title: Predicting mammographic density with linear ultrasound transducers
  • Contributor: Behrens, Annika; Fasching, Peter A.; Schwenke, Eva; Gass, Paul; Häberle, Lothar; Heindl, Felix; Heusinger, Katharina; Lotz, Laura; Lubrich, Hannah; Preuß, Caroline; Schneider, Michael O.; Schulz-Wendtland, Rüdiger; Stumpfe, Florian M.; Uder, Michael; Wunderle, Marius; Zahn, Anna L.; Hack, Carolin C.; Beckmann, Matthias W.; Emons, Julius
  • Published: Springer Science and Business Media LLC, 2023
  • Published in: European Journal of Medical Research, 28 (2023) 1
  • Language: English
  • DOI: 10.1186/s40001-023-01327-9
  • ISSN: 2047-783X
  • Keywords: General Medicine
  • Origination:
  • Footnote:
  • Description: Abstract 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 R2, 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).
  • Access State: Open Access