SynthesisPloS one2016
Acoustic Radiation Force Impulse Imaging for the Differentiation of Benign and Malignant Lymph Nodes: A Systematic Review and Meta-Analysis.
Synthesis in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Virtual touch tissue imaging quantification shear wave elastography for determining benign versus malignant cervical lymph nodes: a comparison with conventional ultrasound.Diagnostic and interventional radiology (Ankara, Turkey) · 2019Article
- Utility of Ultrasound Elastography to Differentiate Benign from Malignant Cervical Lymph Nodes.Journal of medical ultrasoundArticle
- Evaluation of Acoustic Radiation Force Impulse Imaging in Differentiating Benign and Malignant Cervical Lymphadenopathy.Journal of medical ultrasoundArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the overall performance of acoustic radiation force impulse imaging (ARFI) in differentiating between benign and malignant lymph nodes (LNs) by conducting a meta-analysis.
methodsPubMed, Embase, Web of Science, the Cochrane Library and the China National Knowledge Infrastructure were comprehensively searched for potential studies through August 13th, 2016. Studies that investigated the diagnostic power of ARFI for the differential diagnosis of benign and malignant LNs by using virtual touch tissue quantification (VTQ) or virtual touch tissue imaging quantification (VTIQ) were collected. The included articles were published in English or Chinese. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to evaluate the methodological quality. The pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve (AUC) were calculated by means of a bivariate mixed-effects regression model. Meta-regression analysis was performed to identify the potential sources of between study heterogeneity. Fagan plot analysis was used to explore the clinical utilities. Publication bias was assessed using Deek's funnel plot.
resultsNine studies involving 1084 LNs from 929 patients were identified to analyze in the meta-analysis. The summary sensitivity and specificity of ARFI in detecting malignant LNs were 0.87 (95% confidence interval [CI], 0.83-0.91) and 0.88 (95% CI, 0.82-0.92), respectively. The AUC was 0.93 (95% CI, 0.90-0.95). The pooled DOR was 49.59 (95% CI, 26.11-94.15). Deek's funnel plot revealed no significant publication bias.
conclusionARFI is a promising tool for the differentiation of benign and malignant LNs with high sensitivity and specificity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.