From the Center for Biomedical Imaging (K.J.G., L.M.), Center for Data Science (K.J.G.), Center for Advanced Imaging Innovation and Research (L.M.), and Laura and Isaac Perlmutter Cancer Center (L.M.), New York University School of Medicine, 160 E 34th St, 3rd Floor, New York, NY 10016; Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.).
Radiology. 2019 Nov;293(2):246-259. doi: 10.1148/radiol.2019182627. Epub 2019 Sep 24.
Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.
虽然计算机辅助诊断 (CAD) 在乳房 X 线摄影中得到广泛应用，但使用提示来指示乳房 X 光片中潜在癌症的传统 CAD 程序并未提高诊断准确性。由于机器学习的进步，特别是使用深度（多层）卷积神经网络，人工智能经历了一场变革，提高了模型预测的质量。最近，深度学习算法已应用于乳房 X 线摄影和数字乳腺断层合成术 (DBT)。在这篇综述中，作者解释了深度学习在乳房 X 线摄影和 DBT 中的工作原理，并定义了重要的技术挑战。随后，他们讨论了基于人工智能的乳房 X 线摄影、DBT 和放射组学的临床应用的现状和未来展望。现有的算法很先进，接近放射科医生的表现——特别是在癌症检测和乳腺 X 线摄影的风险预测方面。然而，临床验证在很大程度上是缺乏的，目前尚不清楚如何利用深度学习的强大功能来优化实践。需要进一步开发深度学习模型来进行 DBT，这需要收集更大的数据库。预计深度学习最终将在 DBT 中发挥重要作用，包括生成合成图像。
