S-Detect 对 US BI RADS 词汇表中乳腺局灶性病变的特征描述：影像学研究。
S-Detect characterization of focal breast lesions according to the US BI RADS lexicon: a pictorial essay.
机构信息
Department of Biomedicine, Neuroscience and Advanced Diagnostic (Bi.N.D.), University Hospital "Policlinico P. Giaccone", Via Del Vespro 129, 90127, Palermo, Italy.
Fondazione Istituto G.Giglio di Cefalù Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, PA, Italy.
出版信息
J Ultrasound. 2020 Jun;23(2):207-215. doi: 10.1007/s40477-020-00447-w. Epub 2020 Mar 17.
Abstract
High-resolution ultrasonography (US) is a valuable tool in breast imaging. Nevertheless, US is an operator-dependent technique: to overcome this issue, the American College of Radiology (ACR) has developed the breast imaging-reporting and data system (BI-RADS) US lexicon. Despite this effort, the variability in the assessment of focal breast lesions (FBLs) with the use of BI-RADS US lexicon is still an issue. Within this framework, evidence shows that computer-aided image analysis may be effective in improving the radiologist's assessment of FBLs. In particular, S-Detect is a newly developed image-analytic computer program that provides assistance in morphologic analysis of FBLs seen on US according to the BI-RADS US lexicon. This pictorial essay describes state-of-the-art of sonographic characterization of FBLs by using S-Detect.
摘要
高分辨率超声（US）是乳腺成像中一种有价值的工具。然而，US 是一种依赖于操作者的技术：为了克服这个问题，美国放射学院（ACR）已经开发了乳腺成像报告和数据系统（BI-RADS）US 词汇。尽管做出了这些努力，但使用 BI-RADS US 词汇评估局灶性乳腺病变（FBL）的变异性仍然是一个问题。在这个框架内，有证据表明，计算机辅助图像分析可能有助于提高放射科医生对 FBL 的评估。特别是，S-Detect 是一种新开发的图像分析计算机程序，根据 BI-RADS US 词汇，为 US 上所见 FBL 的形态学分析提供帮助。本文通过使用 S-Detect 描述了 FBL 的超声特征的最新技术。
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