title: Acoustic Radiation Force Impulse Imaging for the Differentiation of Benign and Malignant Lymph Nodes: A Systematic Review and Meta-Analysis.
abstract: OBJECTIVE
  To 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.
  
  
  METHODS
  PubMed, 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.
  
  
  RESULTS
  Nine 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.
  
  
  CONCLUSIONS
  ARFI is a promising tool for the differentiation of benign and malignant LNs with high sensitivity and specificity.
authors.affiliation: Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
authors.forename: Peige
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
authors.forename: Shaoping
authors.initials: S
authors.lastname: Zheng
authors.affiliation: Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Yu
authors.affiliation: Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
authors.forename: Mingxing
authors.initials: M
authors.lastname: Xie
authors.affiliation: Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lv
mesh_terms: D003937:Diagnosis, Differential; D054459:Elasticity Imaging Techniques / Q000379:methods*; D018740:Genetic Heterogeneity; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D017594:Publication Bias; D012372:ROC Curve; D012680:Sensitivity and Specificity; D014584:User-Computer Interface