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Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI

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 M...

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Autores principales: Borde, Tabea, Wu, Mingming, Ruschke, Stefan, Boehm, Christof, Stelter, Jonathan, Weiss, Kilian, Metz, Stephan, Makowski, Marcus Richard, Karampinos, Dimitrios C., Fallenberg, Eva Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182116/
https://www.ncbi.nlm.nih.gov/pubmed/36538074
http://dx.doi.org/10.1007/s00330-022-09341-x
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author Borde, Tabea
Wu, Mingming
Ruschke, Stefan
Boehm, Christof
Stelter, Jonathan
Weiss, Kilian
Metz, Stephan
Makowski, Marcus Richard
Karampinos, Dimitrios C.
Fallenberg, Eva Maria
author_facet Borde, Tabea
Wu, Mingming
Ruschke, Stefan
Boehm, Christof
Stelter, Jonathan
Weiss, Kilian
Metz, Stephan
Makowski, Marcus Richard
Karampinos, Dimitrios C.
Fallenberg, Eva Maria
author_sort Borde, Tabea
collection PubMed
description 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 T(2)* decay and a pre-calibrated 7-peak fat spectrum resulting in volumetric fat-only, water-only images, PDFF- and T(2)*-values. After semi-automated breast segmentation, PDFF and T(2)* 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 T(2)* 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.
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spelling pubmed-101821162023-05-14 Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI Borde, Tabea Wu, Mingming Ruschke, Stefan Boehm, Christof Stelter, Jonathan Weiss, Kilian Metz, Stephan Makowski, Marcus Richard Karampinos, Dimitrios C. Fallenberg, Eva Maria Eur Radiol Breast 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 T(2)* decay and a pre-calibrated 7-peak fat spectrum resulting in volumetric fat-only, water-only images, PDFF- and T(2)*-values. After semi-automated breast segmentation, PDFF and T(2)* 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 T(2)* 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. Springer Berlin Heidelberg 2022-12-20 2023 /pmc/articles/PMC10182116/ /pubmed/36538074 http://dx.doi.org/10.1007/s00330-022-09341-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Breast
Borde, Tabea
Wu, Mingming
Ruschke, Stefan
Boehm, Christof
Stelter, Jonathan
Weiss, Kilian
Metz, Stephan
Makowski, Marcus Richard
Karampinos, Dimitrios C.
Fallenberg, Eva Maria
Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI
title Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI
title_full Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI
title_fullStr Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI
title_full_unstemmed Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI
title_short Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI
title_sort assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-t mri
topic Breast
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182116/
https://www.ncbi.nlm.nih.gov/pubmed/36538074
http://dx.doi.org/10.1007/s00330-022-09341-x
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