Llobet Rafael, Pollán Marina, Antón Joaquín, Miranda-García Josefa, Casals María, Martínez Inmaculada, Ruiz-Perales Francisco, Pérez-Gómez Beatriz, Salas-Trejo Dolores, Pérez-Cortés Juan-Carlos
Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain.
Comput Methods Programs Biomed. 2014 Sep;116(2):105-15. doi: 10.1016/j.cmpb.2014.01.021. Epub 2014 Feb 20.
The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC=0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC=0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.
由于乳腺密度与乳腺癌风险相关，乳腺密度量化任务变得越来越重要。在这项工作中，提出了一种用于从全视野数字化乳腺钼靶片中评估乳腺密度的半自动和全自动工具。第一个工具基于一种监督交互式阈值处理程序，用于从脂肪组织中分割出致密组织，其使用有两个目标：以比基于视觉的方法更客观、准确的方式评估乳腺钼靶密度（MD），以及标记随后用于训练全自动工具的乳腺钼靶片。尽管大多数自动化方法依赖基于乳腺钼靶片全局标记的监督方法，但所提出的方法依赖于像素级标记，从而允许在连续尺度上进行更好的组织分类和密度测量。所提出的全自动方法结合了基于局部特征的分类方案和阈值操作，提高了分类器的性能。使用一个包含655张乳腺钼靶片的数据集来测试两种方法在测量MD方面的一致性。三位专家放射科医生使用半自动工具（DM-Scan）测量每张乳腺钼靶片的MD。然后由全自动系统进行测量，并计算两种方法之间的相关性。然后使用一个由230张乳腺钼靶片组成的病例对照数据集分析MD与乳腺癌之间的关系。使用组内相关系数（ICC）来计算评估者之间以及技术之间的可靠性。所获得的结果表明，使用半自动工具时评估者之间的平均ICC = 0.922，而半自动测量和自动测量之间的平均相关性为ICC = 0.838。在病例对照研究中，所获得的结果表明，使用半自动和全自动方法时，MD每增加10%，优势比（OR）分别为1.38和1.50。因此可以得出结论，自动和半自动MD评估具有良好的相关性。这两种方法还都发现了MD与乳腺癌风险之间的关联，这证明了所提出的用于乳腺癌风险预测和临床决策的工具的合理性。DM-Scan的完整版本可免费获取。
