Department of Medical Imaging, Radboud University Medical Center, 6500 HB Geert Grooteplein-Zuid, Nijmegen, The Netherlands.
Vall d' Hebron Institute of Oncology (VHIO), Barcelona, Spain.
Med Phys. 2021 Mar;48(3):1436-1447. doi: 10.1002/mp.14716. Epub 2021 Feb 3.
To develop a patient-based breast density model by characterizing the fibroglandular tissue distribution in patient breasts during compression for mammography and digital breast tomosynthesis (DBT) imaging.
In this prospective study, 88 breast images were acquired using a dedicated breast computed tomography (CT) system. The breasts in the images were classified into their three main tissue components and mechanically compressed to mimic the positioning for mammographic acquisition of the craniocaudal (CC) and mediolateral oblique (MLO) views. The resulting fibroglandular tissue distribution during these compressions was characterized by dividing the compressed breast volume into small regions, for which the median and the 25th and 75th percentile values of local fibroglandular density were obtained in the axial, coronal, and sagittal directions. The best fitting function, based on the likelihood method, for the median distribution was obtained in each direction.
The fibroglandular tissue tends to concentrate toward the caudal (about 15% below the midline of the breast) and anterior regions of the breast, in both the CC- and MLO-view compressions. A symmetrical distribution was found in the MLO direction in the case of the CC-view compression, while a shift of about 12% toward the lateral direction was found in the MLO-view case.
The location of the fibroglandular tissue in the breast under compression during mammography and DBT image acquisition is a major factor for determining the actual glandular dose imparted during these examinations. A more realistic model of the parenchyma in the compressed breast, based on patient image data, was developed. This improved model more accurately reflects the fibroglandular tissue spatial distribution that can be found in patient breasts, and therefore might aid in future studies involving radiation dose and/or cancer development risk estimation.
通过对乳腺钼靶摄影和数字乳腺断层合成（DBT）成像中压迫下患者乳房内纤维腺体组织分布进行特征描述，建立一个基于患者的乳腺密度模型。
在这项前瞻性研究中，使用专用乳腺 CT 系统获取 88 例乳腺图像。将图像中的乳房分为三个主要组织成分，并进行机械压缩以模拟乳腺头尾位（CC）和内外斜位（MLO）采集的定位。在这些压缩过程中，通过将压缩后的乳房体积划分为小区域，获得每个区域在轴位、冠状位和矢状位上的局部纤维腺体密度中位数和 25%分位数及 75%分位数，从而对纤维腺体分布进行特征描述。基于似然法，在每个方向上获得中位数分布的最佳拟合函数。
在 CC 和 MLO 位压迫下，纤维腺体组织趋向于集中在乳房的尾部（约低于乳房中线 15%）和前部。在 CC 位压迫下，MLO 位发现对称分布，而在 MLO 位压迫下，向外侧方向偏移约 12%。
在乳腺钼靶摄影和 DBT 图像采集过程中，压迫下乳腺内纤维腺体组织的位置是决定这些检查中实际腺体剂量的主要因素。基于患者图像数据，开发了一种更真实的压缩乳房实质模型。该改进模型更准确地反映了在患者乳房中发现的纤维腺体组织空间分布，因此可能有助于未来涉及辐射剂量和/或癌症发展风险估计的研究。
