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Meta-Analysis
. 2018 Jun;163(6):1272-1279.
doi: 10.1016/j.surg.2018.01.009. Epub 2018 Feb 14.

Systematic review, meta-analysis, and a high-volume center experience supporting the new role of mural nodules proposed by the updated 2017 international guidelines on IPMN of the pancreas

Affiliations
Meta-Analysis

Systematic review, meta-analysis, and a high-volume center experience supporting the new role of mural nodules proposed by the updated 2017 international guidelines on IPMN of the pancreas

Giovanni Marchegiani et al. Surgery. 2018 Jun.

Abstract

Background: Mural nodules (MNs) have a predominant role in the 2016 revision of the international guidelines on intraductal papillary mucinous neoplasms (IPMN) of the pancreas. The aim of this study was to evaluate MNs as predictors of invasive cancer (iCa) or high-grade dysplasia (HGD) in IPMNs and to investigate the role of MN size in risk prediction.

Methods: A PRISMA-compliant systematic review of the literature and meta-analysis on selected studies were conducted. The random effect model was adopted, and the pooled SMD (standardized mean difference) obtained. The surgical series of IPMNs at a single high-volume institution was reviewed.

Results: This review included 70 studies and 2297 resected IPMNs. MNs have a positive predictive value for malignancy of 62.2%. The meta-analysis suggested that MN size has a considerable effect on predicting IPMNs with both iCa or HGD with a mean SMD of 0.79. All studies included in the meta-analysis used contrast-enhanced endosonography (CE-EUS) to assess MNs. Due to the heterogeneity of the proposed thresholds, no reliable MN size cut-off was identified. Of 317 IPMNs resected at our institution, 102 (32.1%) had a preoperative diagnosis of MN. Multivariate analysis showed that MN is the only independent predictor of iCa and HGD for all types of IPMNs.

Conclusion: MNs are reliable predictors of iCa and HGD in IPMNs as proposed by the 2016 IAP guidelines. CE-EUS seems to be the best tool for characterizing size and has the best accuracy for predicting malignancy. Further studies should determine potential MN dimensional cut-offs.

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