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Development and validation of the surmising model for volumetric breast density using X-ray exposure conditions in digital mammography.

Authors :
Yamamuro, Mika
Asai, Yoshiyuki
Yamada, Takahiro
Kimura, Yuichi
Ishii, Kazunari
Kondo, Yohan
Source :
Medical & Biological Engineering & Computing. Sep2024, p1-11.
Publication Year :
2024

Abstract

The use of breast density as a biomarker for breast cancer treatment has not been well established owing to the difficulty in measuring time-series changes in breast density. In this study, we developed a surmising model for breast density using prior mammograms through a multiple regression analysis, enabling a time series analysis of breast density. We acquired 1320 mediolateral oblique view mammograms to construct the surmising model using multiple regression analysis. The dependent variable was the breast density of the mammary gland region segmented by certified radiological technologists, and independent variables included the compressed breast thickness (CBT), exposure current times exposure second (mAs), tube voltage (kV), and patients’ age. The coefficient of determination of the surmising model was 0.868. After applying the model, the correlation coefficients of the three groups based on the CBT (thin group, 18–36 mm; standard group, 38–46 mm; and thick group, 48–78 mm) were 0.913, 0.945, and 0.867, respectively, suggesting that the thick breast group had a significantly low correlation coefficient (<italic>p</italic> = 0.00231). In conclusion, breast density can be accurately surmised using the CBT, mAs, tube voltage, and patients’ age, even in the absence of a mammogram image.Graphical Abstract: The use of breast density as a biomarker for breast cancer treatment has not been well established owing to the difficulty in measuring time-series changes in breast density. In this study, we developed a surmising model for breast density using prior mammograms through a multiple regression analysis, enabling a time series analysis of breast density. We acquired 1320 mediolateral oblique view mammograms to construct the surmising model using multiple regression analysis. The dependent variable was the breast density of the mammary gland region segmented by certified radiological technologists, and independent variables included the compressed breast thickness (CBT), exposure current times exposure second (mAs), tube voltage (kV), and patients’ age. The coefficient of determination of the surmising model was 0.868. After applying the model, the correlation coefficients of the three groups based on the CBT (thin group, 18–36 mm; standard group, 38–46 mm; and thick group, 48–78 mm) were 0.913, 0.945, and 0.867, respectively, suggesting that the thick breast group had a significantly low correlation coefficient (<italic>p</italic> = 0.00231). In conclusion, breast density can be accurately surmised using the CBT, mAs, tube voltage, and patients’ age, even in the absence of a mammogram image. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01400118
Database :
Academic Search Index
Journal :
Medical & Biological Engineering & Computing
Publication Type :
Academic Journal
Accession number :
179347440
Full Text :
https://doi.org/10.1007/s11517-024-03186-w