Delakis Ioannis
Biomedical Technology Services, Queensland Health, Brisbane, Queensland, Australia.
J Appl Clin Med Phys. 2025 Oct;26(10):e70260. doi: 10.1002/acm2.70260.
Accurate mean glandular dose (MGD) estimation is important in breast cancer screening programs to balance diagnostic benefit with radiation risk.
This study aimed to compare the performance of the Dance and AAPM/EFOMP Task Group 282 (TG282) breast dosimetry methodologies using model versus image-derived breast density metrics.
This study analyzed over 80,000 digital mammography images acquired in 2023 from BreastScreen Queensland (BSQ). Data were obtained from Siemens and Hologic systems and included 2D cranio-caudal and mediolateral oblique views. Images with compressed breast thickness (CBT) between 20 and 100 mm were included. Volumetric breast density (VBD) and glandularity were extracted using Volpara software. MGD was estimated using both Dance and AAPM/EFOMP TG282 models, employing model-based median and image-measured breast density metrics. The ratios of MGD estimated using model medians to those using measured values ( and ) were analyzed across CBT, and Pearson correlations (r) were computed.
The Dance model median glandularity overestimates population-derived glandularity for most CBT, resulting in > 1 at low CBT, with the trend reversed for CBT > 80 mm. showed moderate positive correlation with CBT (r = 0.57 Hologic; r = 0.63 Siemens, p < 0.001). remained close to unity across CBT, with weak negative correlations (r = -0.17 Hologic; r = -0.04 Siemens, p < 0.001), indicating consistency between model and measured VBD.
The AAPM/EFOMP TG282 dosimetry model exhibited stronger agreement between median model-predicted and population-specific measured breast density metrics than the Dance model. This resulted in improved consistency in ratios of estimated MGD values based on median model-to-measured breast density metrics across the full range of CBT, when using the AAPM/EFOMP TG282 methodology.
在乳腺癌筛查项目中，准确估计平均腺体剂量（MGD)对于平衡诊断益处与辐射风险至关重要。
本研究旨在比较使用基于模型的与源自图像的乳房密度指标的Dance和美国物理医学与康复学会/欧洲医学物理组织任务组282（TG282）乳房剂量测定方法的性能。
本研究分析了2023年从昆士兰乳腺癌筛查（BSQ）获取的80,000多张数字乳腺钼靶图像。数据来自西门子和Hologic系统，包括二维头尾位和内外侧斜位视图。纳入了压缩乳房厚度（CBT）在20至100毫米之间的图像。使用Volpara软件提取体积乳房密度（VBD）和腺体密度。使用Dance和美国物理医学与康复学会/欧洲医学物理组织TG282模型，采用基于模型的中位数和图像测量的乳房密度指标来估计MGD。分析了基于模型中位数估计的MGD与基于测量值估计的MGD的比率（ 和 ）随CBT的变化情况，并计算了Pearson相关性（r）。
对于大多数CBT，Dance模型的中位数腺体密度高估了总体来源的腺体密度，导致在低CBT时 > 1，而当CBT > 80毫米时趋势相反。 在CBT范围内呈中度正相关（霍利克公司设备：r = 0.57；西门子公司设备：r = 0.63，p < 0.001）。 在整个CBT范围内均接近1，呈弱负相关（霍利克公司设备：r = -0.17；西门子公司设备：r = -0.04，p < 0.001），表明模型与测量的VBD之间具有一致性。
与Dance模型相比，美国物理医学与康复学会/欧洲医学物理组织TG282剂量测定模型在模型预测中位数与特定人群测量的乳房密度指标之间表现出更强一致性。当使用美国物理医学与康复学会/欧洲医学物理组织TG282方法时，这导致在整个CBT范围内基于模型中位数到测量乳房密度指标的估计MGD值比率的一致性得到改善。
