Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI.
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| Title: | Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI. |
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| Authors: | Borde, Tabea1 (AUTHOR) tabea.borde@tum.de, Wu, Mingming1 (AUTHOR), Ruschke, Stefan1 (AUTHOR), Boehm, Christof1 (AUTHOR), Stelter, Jonathan1 (AUTHOR), Weiss, Kilian2 (AUTHOR), Metz, Stephan1 (AUTHOR), Makowski, Marcus Richard1 (AUTHOR), Karampinos, Dimitrios C.1 (AUTHOR), Fallenberg, Eva Maria1 (AUTHOR) |
| Source: | European Radiology. Jun2023, Vol. 33 Issue 6, p3810-3818. 9p. 1 Color Photograph, 1 Black and White Photograph, 1 Diagram, 2 Charts, 2 Graphs. |
| Subjects: | American College of Radiology, Adipose tissues, Magnetic resonance imaging, Breast exams, Density, Protons |
| Abstract: | Objectives: There is a clinical need for a non-ionizing, quantitative assessment of breast density, as one of the strongest independent risk factors for breast cancer. This study aims to establish proton density fat fraction (PDFF) as a quantitative biomarker for fat tissue concentration in breast MRI and correlate mean breast PDFF to mammography. Methods: In this retrospective study, 193 women were routinely subjected to 3-T MRI using a six-echo chemical shift encoding-based water-fat sequence. Water-fat separation was based on a signal model accounting for a single T2* decay and a pre-calibrated 7-peak fat spectrum resulting in volumetric fat-only, water-only images, PDFF- and T2*-values. After semi-automated breast segmentation, PDFF and T2* values were determined for the entire breast and fibroglandular tissue. The mammographic and MRI-based breast density was classified by visual estimation using the American College of Radiology Breast Imaging Reporting and Data System categories (ACR A-D). Results: The PDFF negatively correlated with mammographic and MRI breast density measurements (Spearman rho: −0.74, p <.001) and revealed a significant distinction between all four ACR categories. Mean T2* of the fibroglandular tissue correlated with increasing ACR categories (Spearman rho: 0.34, p <.001). The PDFF of the fibroglandular tissue showed a correlation with age (Pearson rho: 0.56, p =.03). Conclusion: The proposed breast PDFF as an automated tissue fat concentration measurement is comparable with mammographic breast density estimations. Therefore, it is a promising approach to an accurate, user-independent, and non-ionizing breast density assessment that could be easily incorporated into clinical routine breast MRI exams. Key Points: • The proposed PDFF strongly negatively correlates with visually determined mammographic and MRI-based breast density estimations and therefore allows for an accurate, non-ionizing, and user-independent breast density measurement. • In combination with T2*, the PDFF can be used to track structural alterations in the composition of breast tissue for an individualized risk assessment for breast cancer. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Objectives: There is a clinical need for a non-ionizing, quantitative assessment of breast density, as one of the strongest independent risk factors for breast cancer. This study aims to establish proton density fat fraction (PDFF) as a quantitative biomarker for fat tissue concentration in breast MRI and correlate mean breast PDFF to mammography. Methods: In this retrospective study, 193 women were routinely subjected to 3-T MRI using a six-echo chemical shift encoding-based water-fat sequence. Water-fat separation was based on a signal model accounting for a single T2* decay and a pre-calibrated 7-peak fat spectrum resulting in volumetric fat-only, water-only images, PDFF- and T2*-values. After semi-automated breast segmentation, PDFF and T2* values were determined for the entire breast and fibroglandular tissue. The mammographic and MRI-based breast density was classified by visual estimation using the American College of Radiology Breast Imaging Reporting and Data System categories (ACR A-D). Results: The PDFF negatively correlated with mammographic and MRI breast density measurements (Spearman rho: −0.74, p <.001) and revealed a significant distinction between all four ACR categories. Mean T2* of the fibroglandular tissue correlated with increasing ACR categories (Spearman rho: 0.34, p <.001). The PDFF of the fibroglandular tissue showed a correlation with age (Pearson rho: 0.56, p =.03). Conclusion: The proposed breast PDFF as an automated tissue fat concentration measurement is comparable with mammographic breast density estimations. Therefore, it is a promising approach to an accurate, user-independent, and non-ionizing breast density assessment that could be easily incorporated into clinical routine breast MRI exams. Key Points: • The proposed PDFF strongly negatively correlates with visually determined mammographic and MRI-based breast density estimations and therefore allows for an accurate, non-ionizing, and user-independent breast density measurement. • In combination with T2*, the PDFF can be used to track structural alterations in the composition of breast tissue for an individualized risk assessment for breast cancer. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09387994 |
| DOI: | 10.1007/s00330-022-09341-x |
