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Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA)

BACKGROUND: Earlier studies have shown that the superb microvascular imaging (SMI) can detect tumor angiogenesis to distinguish thyroid nodules, but there is no systematic review. This meta-analysis aimed to identify the accuracy of ultrasound SMI for the diagnosis of thyroid nodules. METHODS: We se...

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Autores principales: Jin, Hui, Wang, Cong, Jin, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276439/
https://www.ncbi.nlm.nih.gov/pubmed/35713460
http://dx.doi.org/10.1097/MD.0000000000029505
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author Jin, Hui
Wang, Cong
Jin, Xin
author_facet Jin, Hui
Wang, Cong
Jin, Xin
author_sort Jin, Hui
collection PubMed
description BACKGROUND: Earlier studies have shown that the superb microvascular imaging (SMI) can detect tumor angiogenesis to distinguish thyroid nodules, but there is no systematic review. This meta-analysis aimed to identify the accuracy of ultrasound SMI for the diagnosis of thyroid nodules. METHODS: We searched PubMed, Cochrane Library, and CBM databases. A meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 software. We calculated the summary statistics for sensitivity, specificity, positive and negative likelihood ratio (LR(+)/LR(−)), diagnostic odds ratio, and the synthetic receiver operating characteristic curve. Data will be pooled by either a fixed-effects model or a random-effects model according to the results of heterogeneity identification. RESULTS: 11 studies that met the inclusion criteria were included in this meta-analysis. The quality assessment of the study of diagnostic accuracy studies scores of all included studies were ≥22. A total of 1003 thyroid malignant nodules and 957 thyroid benign nodules were assessed. The main outcome included: the pooled sensitivity was 0.81 (95% confidence intervals (CI) = 0.79–0.84), and the pooled specificity was 0.86 (95% CI = 0.84–0.88); the pooled LR(+) was 5.79 (95% CI = 4.44–7.54), and the pooled negative LR(−) was 0.23 (95% CI = 0.20–0.26); the pooled diagnostic odds ratio of SMI in the diagnosis of thyroid nodules was 26.84 (95% CI = 19.13–37.60). The area under the synthetic receiver operating characteristic curve was 0.89 (95% CI = 0.86–0.91). We found no evidence for publication bias (t = 0.72, P = .49). CONCLUSION: Our meta-analysis indicates that SMI may have high diagnostic accuracy in distinguishing benign and malignant thyroid nodules. SYSTEMATIC REVIEW REGISTRATION: INPLASY202080084.
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spelling pubmed-92764392022-08-01 Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA) Jin, Hui Wang, Cong Jin, Xin Medicine (Baltimore) 4100 BACKGROUND: Earlier studies have shown that the superb microvascular imaging (SMI) can detect tumor angiogenesis to distinguish thyroid nodules, but there is no systematic review. This meta-analysis aimed to identify the accuracy of ultrasound SMI for the diagnosis of thyroid nodules. METHODS: We searched PubMed, Cochrane Library, and CBM databases. A meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 software. We calculated the summary statistics for sensitivity, specificity, positive and negative likelihood ratio (LR(+)/LR(−)), diagnostic odds ratio, and the synthetic receiver operating characteristic curve. Data will be pooled by either a fixed-effects model or a random-effects model according to the results of heterogeneity identification. RESULTS: 11 studies that met the inclusion criteria were included in this meta-analysis. The quality assessment of the study of diagnostic accuracy studies scores of all included studies were ≥22. A total of 1003 thyroid malignant nodules and 957 thyroid benign nodules were assessed. The main outcome included: the pooled sensitivity was 0.81 (95% confidence intervals (CI) = 0.79–0.84), and the pooled specificity was 0.86 (95% CI = 0.84–0.88); the pooled LR(+) was 5.79 (95% CI = 4.44–7.54), and the pooled negative LR(−) was 0.23 (95% CI = 0.20–0.26); the pooled diagnostic odds ratio of SMI in the diagnosis of thyroid nodules was 26.84 (95% CI = 19.13–37.60). The area under the synthetic receiver operating characteristic curve was 0.89 (95% CI = 0.86–0.91). We found no evidence for publication bias (t = 0.72, P = .49). CONCLUSION: Our meta-analysis indicates that SMI may have high diagnostic accuracy in distinguishing benign and malignant thyroid nodules. SYSTEMATIC REVIEW REGISTRATION: INPLASY202080084. Lippincott Williams & Wilkins 2022-06-17 /pmc/articles/PMC9276439/ /pubmed/35713460 http://dx.doi.org/10.1097/MD.0000000000029505 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
spellingShingle 4100
Jin, Hui
Wang, Cong
Jin, Xin
Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA)
title Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA)
title_full Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA)
title_fullStr Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA)
title_full_unstemmed Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA)
title_short Superb microvascular imaging for distinguishing thyroid nodules: A meta-analysis (PRISMA)
title_sort superb microvascular imaging for distinguishing thyroid nodules: a meta-analysis (prisma)
topic 4100
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276439/
https://www.ncbi.nlm.nih.gov/pubmed/35713460
http://dx.doi.org/10.1097/MD.0000000000029505
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