Shim Sojin, Unkelbach Jan, Landsmann Anna, Boss Andreas
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland.
Department of Radiation Oncology, University Hospital Zurich, 8091 Zurich, Switzerland.
Diagnostics (Basel). 2023 Oct 30;13(21):3343. doi: 10.3390/diagnostics13213343.
Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age.
The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method.
The BTV increased from 545 ± 345 to 676 ± 412 cm, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group ( < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient's age with residual standard errors of 386 cm, 67 cm, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast.
The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features-BTV, MGV, and PBD, as a function of the patient's age.
乳腺密度被认为是乳腺癌发生的一个独立危险因素。本研究旨在根据患者年龄和乳腺象限定量评估乳腺密度百分比（PBD）和乳腺体积（MGV）。我们提出一个回归模型来估计PBD和MGV作为患者年龄的函数。
对517名女性（57±8岁）的1027个无软组织肿块、钙化或植入物的螺旋乳腺CT（BCT）数据集的乳腺成分进行分割。在整个乳腺以及四个象限中的每一个象限测量乳房的乳腺组织体积（BTV）、MGV和PBD。在7个年龄组中分析了这三种乳腺成分特征，年龄范围为40至74岁，间隔为5年。对BTV拟合对数模型，并使用最小二乘法建立MGV和PBD作为年龄函数的乘法逆模型。
从最年轻组到最年长组，BTV从545±345增加到676±412 cm，MGV和PBD分别从111±164减少到57±43 cm以及从21±21减少到11±9%（P<0.05）。所有年龄的平均PBD为14±13%。回归模型可以根据患者年龄预测BTV、MGV和PBD，剩余标准误差分别为386 cm、67 cm和13%。每个象限中MGV和PBD的减少情况与整个乳腺中的情况一致。
通过BCT检查计算得到的PBD和MGV为女性乳腺癌风险评估提供了重要信息。该研究量化了整个乳腺中乳腺组织的减少和密度的降低。它建立了数学模型来估计乳腺成分特征——BTV、MGV和PBD作为患者年龄的函数。
