Sorin Vera, Sklair-Levy Miri, Glicksberg Benjamin S, Konen Eli, Nadkarni Girish N, Klang Eyal
Department of Radiology, Mayo Clinic, Rochester, MN (V.S.).
Department of Diagnostic Imaging, Chaim Sheba Medical Center, affiliated to the Sackler School of Medicine, Tel-Aviv University, Israel (M.S-L., E.K.).
Acad Radiol. 2025 May;32(5):2497-2508. doi: 10.1016/j.acra.2024.11.035. Epub 2024 Dec 5.
BACKGROUND/AIM: Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is to systematically review the literature on deep learning (DL) applications for CEM, exploring how these models can further enhance CEM diagnostic potential.
This systematic review was reported according to the PRISMA guidelines. We searched for studies published up to April 2024. MEDLINE, Scopus and Google Scholar were used as search databases. Two reviewers independently implemented the search strategy. We included all types of original studies published in English that evaluated DL algorithms for automatic analysis of contrast-enhanced mammography CEM images. The quality of the studies was independently evaluated by two reviewers based on the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria.
Sixteen relevant studies published between 2018 and 2024 were identified. All but one used convolutional neural network models (CNN) models. All studies evaluated DL algorithms for lesion classification, while six studies also assessed lesion detection or segmentation. Segmentation was performed manually in three studies, both manually and automatically in two studies and automatically in ten studies. For lesion classification on retrospective datasets, CNN models reported varied areas under the curve (AUCs) ranging from 0.53 to 0.99. Models incorporating attention mechanism achieved accuracies of 88.1% and 89.1%. Prospective studies reported AUC values of 0.89 and 0.91. Some studies demonstrated that combining DL models with radiomics featured improved classification. Integrating DL algorithms with radiologists' assessments enhanced diagnostic performance.
While still at an early research stage, DL can improve CEM diagnostic precision. However, there is a relatively small number of studies evaluating different DL algorithms, and most studies are retrospective. Further prospective testing to assess performance of applications at actual clinical setting is warranted.
背景/目的：对比增强乳腺X线摄影（CEM）是一种相对新颖的成像技术，能够实现乳腺的解剖和功能成像，与标准二维乳腺X线摄影相比，其诊断性能有所提高。本研究的目的是系统回顾关于深度学习（DL）在CEM中的应用的文献，探讨这些模型如何进一步提高CEM的诊断潜力。
本系统评价按照PRISMA指南进行报告。我们检索了截至2024年4月发表的研究。使用MEDLINE、Scopus和谷歌学术作为检索数据库。两名评价者独立实施检索策略。我们纳入了所有以英文发表的评估DL算法用于自动分析对比增强乳腺X线摄影CEM图像的原始研究类型。研究质量由两名评价者根据诊断准确性研究质量评估（QUADAS-2）标准独立评估。
确定了2018年至2024年期间发表的16项相关研究。除一项研究外，其余均使用卷积神经网络模型（CNN）。所有研究均评估了DL算法用于病变分类，而六项研究还评估了病变检测或分割。三项研究中分割是手动进行的，两项研究中分割是手动和自动结合进行的，十项研究中分割是自动进行的。对于回顾性数据集上的病变分类，CNN模型报告的曲线下面积（AUC）各不相同，范围从0.53到0.99。纳入注意力机制的模型准确率分别为88.1%和89.1%。前瞻性研究报告的AUC值为0.89和0.91。一些研究表明，将DL模型与放射组学相结合可提高分类效果。将DL算法与放射科医生的评估相结合可提高诊断性能。
虽然DL仍处于早期研究阶段，但它可以提高CEM的诊断精度。然而，评估不同DL算法的研究数量相对较少，且大多数研究是回顾性的。有必要进行进一步的前瞻性测试，以评估在实际临床环境中应用的性能。
