Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China.
Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University,The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China.
Eur J Radiol. 2021 Nov;144:109991. doi: 10.1016/j.ejrad.2021.109991. Epub 2021 Oct 2.
This systematic review and meta-analysis aimed to evaluate the diagnostic performance of ultrasound elastography in the differentiation of benign and malignant breast non-mass lesions (NMLs).
PubMed, Cochrane Library, and Embase databases were searched for eligible studies up to end of June 2021. The diagnostic performance of elastography for NMLs was investigated using pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio (DOR), post-test probability, and the area under hierarchical summary receiver operating characteristic curve (HSROC).
Eleven studies involving 812 NMLs (malignant 414) were included. The pooled sensitivity, specificity, DOR, positive likelihood ratio, and negative likelihood of elastography for the differentiation of benign and malignant breast NMLs were 79% (95 %CI: 71-85), 86% (95 %CI: 79-91), 23.32 (95 %CI: 13.38-40.66), 5.67 (95 %CI: 3.79-8.47), and 0.24 (95 %CI: 0.17-0.34), respectively. No significant publication bias existed. The area under the HSROC curve was 90% (95 %CI: 87-92). Fagan plots demonstrated good clinical utility. However, substantial heterogeneity existed. Country, measurement index, and number of lesions served as potential sources of heterogeneity.
The results of this study suggest that elastography has high diagnostic accuracy in differentiating between malignant and benign NMLs. Elastography can be a feasible and non-invasive tool for breast NMLs.
本系统评价和荟萃分析旨在评估超声弹性成像在鉴别良性和恶性乳腺非肿块病变（NML）中的诊断性能。
检索 PubMed、Cochrane 图书馆和 Embase 数据库，以获取截至 2021 年 6 月底的合格研究。使用汇总敏感性和特异性、似然比、诊断优势比（DOR）、后验概率和分层汇总受试者工作特征曲线下面积（HSROC）来研究弹性成像对 NML 的诊断性能。
纳入了 11 项涉及 812 个 NML（恶性 414 个）的研究。弹性成像鉴别良性和恶性乳腺 NML 的汇总敏感性、特异性、DOR、阳性似然比和阴性似然比分别为 79%（95%CI：71-85）、86%（95%CI：79-91）、23.32（95%CI：13.38-40.66）、5.67（95%CI：3.79-8.47）和 0.24（95%CI：0.17-0.34）。不存在显著的发表偏倚。HSROC 曲线下面积为 90%（95%CI：87-92）。Fagan 图表明具有良好的临床实用性。然而，存在很大的异质性。国家、测量指标和病变数量是异质性的潜在来源。
本研究结果表明，弹性成像在鉴别恶性和良性 NML 方面具有较高的诊断准确性。弹性成像可为乳腺 NML 提供一种可行的非侵入性工具。
Zotero 插件