Berg Wendie A, Vourtsis Athina
University of Pittsburgh School of Medicine, Magee-Womens Hospital of the University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA.
Diagnostic Mammography Medical Diagnostic Imaging Unit, Athens, Greece.
J Breast Imaging. 2019 Dec 5;1(4):283-296. doi: 10.1093/jbi/wbz055.
In women with dense breasts (heterogeneously or extremely dense), adding screening ultrasound to mammography increases detection of node-negative invasive breast cancer. Similar incremental cancer detection rates averaging 2.1-2.7 per 1000 have been observed for physician- and technologist-performed handheld ultrasound (HHUS) and automated ultrasound (AUS). Adding screening ultrasound (US) for women with dense breasts significantly reduces interval cancer rates. Training is critical before interpreting examinations for both modalities, and a learning curve to achieve optimal performance has been observed. On average, about 3% of women will be recommended for biopsy on the prevalence round because of screening US, with a wide range of 2%-30% malignancy rates for suspicious findings seen only on US. Breast Imaging Reporting and Data System 3 lesions identified only on screening HHUS can be safely followed at 1 year rather than 6 months. Computer-aided detection and diagnosis software can augment performance of AUS and HHUS; ongoing research on machine learning and deep learning algorithms will likely improve outcomes and workflow with screening US.
对于乳腺致密（不均匀致密或极度致密）的女性，在乳腺钼靶检查基础上增加超声筛查可提高对腋窝淋巴结阴性浸润性乳腺癌的检出率。对于医生和技术人员操作的手持超声（HHUS）和自动超声（AUS），也观察到了类似的平均每1000例增加2.1 - 2.7例的癌症额外检出率。对乳腺致密的女性增加超声（US）筛查可显著降低间期癌发生率。在解读这两种检查结果之前，培训至关重要，并且已观察到存在达到最佳表现的学习曲线。平均而言，约3%的女性在普查轮次中会因超声筛查而被建议进行活检，仅超声检查发现的可疑结果的恶性率在2% - 30%之间波动。仅在筛查HHUS时发现的乳腺影像报告和数据系统3类病变可安全地随访1年而非6个月。计算机辅助检测和诊断软件可提高AUS和HHUS的性能；正在进行的关于机器学习和深度学习算法的研究可能会改善超声筛查的结果和工作流程。
