Endoscopic ultrasonography with fine-needle aspiration for histological diagnosis of solid pancreatic masses: a meta-analysis of diagnostic accuracy studies

Omar Banafea, Fabian Pius Mghanga, Jinfang Zhao, Ruifeng Zhao, Liangru Zhu, Omar Banafea, Fabian Pius Mghanga, Jinfang Zhao, Ruifeng Zhao, Liangru Zhu

Abstract

Background: Previous studies have demonstrated that endoscopic ultrasound-fine needle aspiration (EUS-FNA) is a reliable tool for diagnosing pancreatic lesions; however, the reported sensitivity and specificity vary greatly across studies. The aim of this study was to pool the existing literature and assess the overall performance of EUS-FNA in the diagnosis of solid pancreatic lesions.

Methods: A systematic search of MEDLINE, Cochrane Database for Systematic Reviews, and EMBASE was performed to identify original and review articles published between January 1995 and January 2014 that reported the accuracy of EUS-FNA in the diagnosis of pancreatic masses. Quality of the included studies was assessed using the quality assessment of diagnosis accuracy studies score tool. Meta-DiSc software was used to calculate the pooled sensitivity and specificity, positive and negative likelihood ratios, and to construct the summary receiver operating characteristics curve.

Results: Twenty studies involving a total of 2,761 patients were included in the study. The pooled sensitivity and specificity of EUS-FNA in the diagnosis of solid pancreatic lesions were 90.8 % [95 % confidence interval (CI), 89.4-92 %] and 96.5 % (95 % CI, 94.8-97.7 %), respectively. The positive and negative likelihood ratios were 14.8 (95 % CI, 8.0-27.3) and 0.12 (95 % CI, 0.09-0.16), respectively. The overall diagnostic accuracy was 91.0 %.

Conclusions: Our findings suggest that EUS-FNA has high sensitivity and specificity in the diagnosis of solid pancreatic lesions.

Keywords: Endoscopic ultrasound; Fine needle aspiration; Pancreatic mass.

Figures

Fig. 1
Fig. 1
Flow diagram of the study selection process
Fig. 2
Fig. 2
Forest plots of sensitivity (a) and specificity (b)
Fig. 3
Fig. 3
Forest plots of positive likelihood ratio (a) and negative likelihood ratio (b)
Fig. 4
Fig. 4
Forest plot of diagnostic odds ratio
Fig. 5
Fig. 5
Summary receiver operating characteristic (SROC) curve analysis

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