Feng Jiaping, Lu Jianghao, Jin Chunchun, Chen Yihao, Chen Sihan, Guo Guoqiang, Gong Xuehao
Graduate School, Guangzhou Medical University, Guangzhou 510180, China.
Department of Ultrasound, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Sungang West Road 3002, Futian District, Shenzhen 518025, China.
Diagnostics (Basel). 2022 Oct 31;12(11):2648. doi: 10.3390/diagnostics12112648.
We performed a systematic review and meta-analysis of studies that investigated the diagnostic performance of Superb Microvascular Imaging (SMI) in differentiating between benign and malignant breast tumors.
Studies published between January 2010 and March 2022 were retrieved by online literature search conducted in PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine Disc, China National Knowledge Infrastructure, Wanfang, and Vip databases. Pooled sensitivity, specificity, and diagnostic odd ratios were calculated using Stata software 15.0. Heterogeneity among the included studies was assessed using statistic and Q test. Meta-regression and subgroup analyses were conducted to investigate potential sources of heterogeneity. Influence analysis was conducted to determine the robustness of the pooled conclusions. Deeks' funnel plot asymmetry test was performed to assess publication bias. A summary receiver operating characteristic curve (SROC) was constructed.
Twenty-three studies involving 2749 breast lesions were included in our meta-analysis. The pooled sensitivity and specificity were 0.80 (95% confidence interval [CI], 0.77-0.84, inconsistency index [] = 28.32%) and 0.84 (95% CI, 0.79-0.88, = 89.36%), respectively. The pooled diagnostic odds ratio was 19.95 (95% CI, 14.84-26.82). The area under the SROC (AUC) was 0.85 (95% CI, 0.81-0.87).
SMI has a relatively high sensitivity, specificity, and accuracy for differentiating between benign and malignant breast lesions. It represents a promising supplementary technique for the diagnosis of breast neoplasms.
我们对研究Superb微血管成像(SMI)在鉴别乳腺良恶性肿瘤诊断性能的研究进行了系统评价和荟萃分析。
通过在PubMed、Embase、Cochrane图书馆、科学网、中国生物医学光盘数据库、中国知网、万方和维普数据库中进行在线文献检索,检索2010年1月至2022年3月发表的研究。使用Stata软件15.0计算合并敏感性、特异性和诊断比值比。采用统计量和Q检验评估纳入研究之间的异质性。进行Meta回归和亚组分析以调查异质性的潜在来源。进行影响分析以确定合并结论的稳健性。进行Deeks漏斗图不对称性检验以评估发表偏倚。构建汇总受试者工作特征曲线(SROC)。
我们的荟萃分析纳入了23项涉及2749个乳腺病变的研究。合并敏感性和特异性分别为0.80(95%置信区间[CI],0.77 - 0.84,不一致指数[] = 28.32%)和0.84(95%CI,0.79 - 0.88, = 89.36%)。合并诊断比值比为19.95(95%CI,14.84 - 26.82)。SROC曲线下面积(AUC)为0.85(95%CI,0.81 - 0.87)。
SMI在鉴别乳腺良恶性病变方面具有较高的敏感性、特异性和准确性。它是一种有前景的乳腺肿瘤诊断辅助技术。