Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
Philips GmbH, Hamburg, Germany.
Eur Radiol. 2023 Jun;33(6):3810-3818. doi: 10.1007/s00330-022-09341-x. Epub 2022 Dec 20.
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.
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* decay and a pre-calibrated 7-peak fat spectrum resulting in volumetric fat-only, water-only images, PDFF- and T*-values. After semi-automated breast segmentation, PDFF and T* 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).
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* 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).
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.
• 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.
由于乳腺密度是乳腺癌最强的独立危险因素之一，因此临床需要一种非电离、定量的评估方法。本研究旨在建立质子密度脂肪分数（PDFF）作为乳腺 MRI 中脂肪组织浓度的定量生物标志物，并将平均乳腺 PDFF 与乳房 X 线照相术相关联。
在这项回顾性研究中，193 名女性常规接受了使用基于六回波化学位移编码的水脂序列的 3-T MRI 检查。水脂分离基于考虑单个 T衰减和经过预校准的 7 峰脂肪谱的信号模型，从而产生容积脂肪仅、水仅图像、PDFF 和 T-值。在半自动乳腺分割后，确定整个乳房和纤维腺体组织的 PDFF 和 T*值。使用美国放射学院乳腺成像报告和数据系统分类（ACR A-D）进行视觉估计来分类乳房 X 线照相术和 MRI 基于的乳腺密度。
PDFF 与乳房 X 线照相术和 MRI 乳腺密度测量呈负相关（Spearman rho：-0.74，p<.001），并在所有四个 ACR 类别之间显示出显著差异。纤维腺体组织的平均 T*与 ACR 类别增加相关（Spearman rho：0.34，p<.001）。纤维腺体组织的 PDFF 与年龄呈相关性（Pearson rho：0.56，p=0.03）。
所提出的作为自动组织脂肪浓度测量的乳腺 PDFF 与乳房 X 线照相术的乳腺密度估计相当。因此，它是一种有前途的方法，可以实现准确、用户独立且非电离的乳腺密度评估，并且可以很容易地纳入临床常规乳腺 MRI 检查中。
所提出的 PDFF 与视觉确定的乳房 X 线照相术和 MRI 基于的乳腺密度估计强烈负相关，因此允许进行准确、非电离和用户独立的乳腺密度测量。
与 T2*结合使用，PDFF 可用于跟踪乳腺组织组成的结构变化，以进行乳腺癌的个体化风险评估。
