EUS-Guided FNA for Diagnosis of Pancreatic Cystic Lesions: a Meta-Analysis

Author:

Wang Qi-Xian,Xiao Jun,Orange Matthew,Zhang Hu,Zhu You-Qing

Abstract

Background: Preoperative diagnosis of pancreatic cystic lesions (PCLs) must be reliable as the current standard treatment, major or total pancreatectomy, dramatically affects quality of life. Additionally, early diagnosis of malignancy is essential to an improved prognosis. The diagnostic accuracy of fluid analysis using endoscopic ultrasonography-guided fine-needle aspiration (EUS-FNA) has been demonstrated in pancreatic solid lesions. The utility of this technique in the diagnosis of PCLs is still unknown. Methods: A comprehensive search was performed in multiple databases. Studies differentiating benign and malignant PCLs via EUS-FNA were included in this meta-analysis. The quality of diagnostic accuracy studies (QUADAS) was adopted to evaluate the selected studies. Pooled sensitivity, specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristic (sROC) curve analyses were conducted. Two main classification types of malignancy were characterized and analyzed. We also generated a subgroup analysis of available clinical factors. Publication bias was evaluated by Begg's and Egger's tests. Results: Sixteen studies containing 1024 subjects have been published. The pooled sensitivity for malignant cytology according to classification 1 was 0.51 (95% CI, 0.45-0.58), and pooled specificity was 0.94 (95% CI, 0.92-0.96). When the detected PCLs were identified as classification 2, suspicious malignancy or potential malignancy, sensitivity and specificity were similar, 0.52 (95% CI, 0.46-0.57) and 0.97 (95% CI, 0.95-0.98) respectively. Conclusion: This meta-analysis demonstrates that EUS-FNA is a reliable clinical tool for the diagnosis of PCLs. However, a more accurate algorithm is needed to reduce various biases and to improve the sensitivity of EUS-FNA in the detection of malignant PCLs.

Publisher

S. Karger AG

Subject

Physiology

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