Universidad Industrial de Santander, Bucaramanga, Colombia.
Universidad Industrial de Santander, Bucaramanga, Colombia.
Comput Methods Programs Biomed. 2021 Nov;212:106443. doi: 10.1016/j.cmpb.2021.106443. Epub 2021 Sep 29.
The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today.
We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face.
Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered.
Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.
利用计算机分析乳腺 X 光片以开发定量生物标志物是一个不断发展的领域，在乳腺癌风险评估中具有广泛的应用。计算机图像分析提供了从筛查和诊断图像中提取额外信息的可能性，以帮助评估乳腺癌风险。在这项工作中，我们回顾了用于上述任务的乳腺 X 光自动计算机分析的算法和方法，并讨论了当今这些方法的开发和改进所面临的主要挑战。
我们回顾了基于乳腺 X 光片的风险评估的两个主要分支的最新进展：实质分析和乳腺密度估计，包括所考虑的大多数研究的性能指标。实质分析方法分为基于特征的方法和基于深度学习的方法；乳腺密度方法分为基于面积的、基于体积的和乳腺分类方法。此外，我们确定了这些研究领域目前面临的挑战。
基于深度学习算法的实质分析呈上升趋势，一些研究显示出高性能指标，例如接收器操作特征曲线下的面积高达 90。使用乳腺密度进行风险评估的方法报告了更多样化的性能指标；然而，我们也可以发现，使用深度学习方法的方法在考虑的每个细分领域都能产生高性能。
乳腺密度估计和实质分析都是乳腺癌风险评估任务的有前途的工具；深度学习方法的性能可与其他考虑的方法相媲美或优于其他方法。所有考虑的方法都面临着一些挑战，例如缺乏它们之间的客观比较以及缺乏来自不同人群的数据集的访问权限。
