Medical University of Vienna, Center for Medical Physics and Biomedical Engineering, Vienna, 1090, Austria.
Radiologische Gruppenpraxis Baden, Baden, 2500, Austria.
Sci Rep. 2019 Apr 10;9(1):5866. doi: 10.1038/s41598-019-42144-7.
Average glandular dose (AGD) in digital mammography crucially depends on the estimation of breast glandularity. In this study we compared three different methods of estimating glandularities according to Wu, Dance and Volpara with respect to resulting AGDs. Exposure data from 3050 patient images, acquired with a GE Senographe Essential constituted the study population of this work. We compared AGD (1) according to Dance et al. applying custom g, c, and s factors using HVL, breast thickness, patient age and incident air kerma (IAK) from the DICOM headers; (2) according to Wu et al. as determined by the GE system; and (3) AGD derived with the Dance model with personalized c factors using glandularity determined with the Volpara (Volpara Solutions, Wellington, New Zealand) software (Volpare AGD). The ratios of the resulting AGDs were analysed versus parameters influencing dose. The highest deviation between the resulting AGDs was found in the ratio of GE AGD to Volpara AGD for breast thicknesses between 20 and 40 mm (ratio: 0.80). For thicker breasts this ratio is close to one (1 ± 0.02 for breast thicknesses >60 mm). The Dance to Volpara ratio was between 0.86 (breast thickness 20-40 mm) and 0.99 (>80 mm), and Dance/GE AGD was between 1.07 (breast thickness 20-40 mm) and 0.98 (41-60, and >80 mm). Glandularities by Volpara were generally smaller than the one calculated with the Dance method. This effect is most pronounced for small breast thickness and older ages. Taking the considerable divergences between the AGDs from different methods into account, the selection of the method should by done carefully. As the Volpara method provides an analysis of the individual breast tissue, while the Wu and the Dance methods use look up tables and custom parameter sets, the Volpara method might be more appropriate if individual ADG values are sought. For regulatory purposes and comparison with diagnostic reference values, the method to be used needs to be defined exactly and clearly be stated. However, it should be accepted that dose values calculated with standardized models, like AGD and also effective dose, are afflicted with a considerable uncertainty budgets that need to be accounted for in the interpretation of these values.
乳腺平均腺体剂量（AGD）在数字乳腺摄影中主要取决于对乳腺密度的估计。在这项研究中，我们根据 Wu、Dance 和 Volpara 三种不同的方法，比较了这三种方法在 AGD 方面的差异。这项工作的研究人群是由 3050 名患者的图像组成的，这些图像是使用 GE Senographe Essential 采集的。我们根据以下方法比较了 AGD：（1）根据 Dance 等人的方法，使用 HVL、乳房厚度、患者年龄和来自 DICOM 头的入射空气比释动能（IAK），应用定制的 g、c 和 s 因子；（2）根据 GE 系统确定的 Wu 等人的方法；（3）使用 Volpara 软件（Volpara Solutions，Wellington，新西兰）确定的腺体密度，利用 Dance 模型计算具有个性化 c 因子的 AGD（Volpara AGD）。分析了所得 AGD 与影响剂量的参数之间的比值。发现所得 AGD 之间的最大差异出现在 GE AGD 与 Volpara AGD 之间的比值中，对于乳房厚度在 20-40mm 之间（比值：0.80）。对于更厚的乳房，该比值接近 1（1±0.02，对于乳房厚度>60mm）。Dance 与 Volpara 的比值在 0.86（乳房厚度 20-40mm）和 0.99（>80mm）之间，Dance/GE AGD 在 1.07（乳房厚度 20-40mm）和 0.98（41-60mm 和>80mm）之间。Volpara 的腺体密度通常小于 Dance 方法计算的腺体密度。这种影响在乳房厚度较小和年龄较大时最为明显。考虑到不同方法的 AGD 之间存在很大的差异，应谨慎选择方法。由于 Volpara 方法提供了对个体乳房组织的分析，而 Wu 和 Dance 方法使用查找表和定制参数集，因此如果需要个体 ADG 值，Volpara 方法可能更合适。出于监管目的和与诊断参考值的比较，需要明确规定并清楚地说明要使用的方法。然而，应该接受这样一个事实，即使用标准化模型（如 AGD 和有效剂量）计算的剂量值存在相当大的不确定性预算，需要在解释这些值时加以考虑。
