QT Ultrasound, LLC, Novato, CA, USA.
Med Phys. 2019 Jun;46(6):2610-2620. doi: 10.1002/mp.13503. Epub 2019 Apr 22.
Breast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two-dimensional projections from mammography with or without tomosynthesis using either (a) subjective assessment or (b) a computer-aided approach. The purpose of this work is twofold: (a) to establish an algorithm for quantitative assessment of breast density using quantitative three-dimensional transmission ultrasound imaging; and (b) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density.
We described and verified a threshold-based segmentation algorithm to give a quantitative breast density (QBD) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating fibroglandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments.
Quantitative breast density (QBD) measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between QBD and both subjective mammographic assessments of Breast Imaging - Reporting and Data System (BI-RADS) breast composition categories and Volpara density scores - the Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI: 0.71-0.96) and 0.96 (95% CI: 0.92-0.98), respectively.
The calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and Food and Drug Administration (FDA)-cleared objective assessments of breast density.
乳腺密度在乳腺癌风险评估中很重要。目前,乳腺密度是通过二维投影评估的,这些投影来自于有或没有断层合成的乳房 X 光摄影术,使用的方法有(a)主观评估或(b)计算机辅助方法。本研究的目的有两个:(a)建立一种使用定量三维透射超声成像技术评估乳腺密度的算法;(b)确定这些定量评估与主观和客观的乳腺密度乳房 X 光摄影术评估相比如何。
我们描述并验证了一种基于阈值的分割算法,该算法可以对具有已知几何形状的模型的超声断层图像给出定量的乳腺密度(QBD)。我们还使用该算法和透射超声断层扫描,根据成像的定量组织特征,将纤维腺体组织从脂肪和皮肤中分离出来,定量确定乳腺密度,并将定量断层扫描分割结果与主观和客观的乳房 X 光摄影术评估进行比较。
在模型中测量的定量乳腺密度(QBD)在几何体积方面具有很高的定量准确性,平均差异小于总模型体积的 0.1%。QBD 与乳腺成像报告和数据系统(BI-RADS)乳腺组成类别和 Volpara 密度评分的主观乳房 X 光摄影术评估有很强的相关性——这两种比较的斯皮尔曼相关系数分别计算为 0.90(95%置信区间:0.71-0.96)和 0.96(95%置信区间:0.92-0.98)。
使用超声断层扫描和描述的分割算法计算乳腺密度在模型中具有定量准确性,并且与主观和美国食品和药物管理局(FDA)批准的客观评估乳腺密度高度相关。