Prediction of the lymphatic, microvascular, and perineural invasion of pancreatic neuroendocrine tumors using preoperative magnetic resonance imaging

Abstract

BACKGROUND Significant correlation between lymphatic, microvascular, and perineural invasion (LMPI) and the prognosis of pancreatic neuroendocrine tumors (PENTs) was confirmed by previous studies. There was no previous study reported the relationship between magnetic resonance imaging (MRI) parameters and LMPI. AIM To determine the feasibility of using preoperative MRI of the pancreas to predict LMPI in patients with non-functioning PENTs (NFPNETs). METHODS A total of 61 patients with NFPNETs who underwent MRI scans and lymphadenectomy from May 2011 to June 2018 were included in this retrospective study. The patients were divided into group 1 (n = 34, LMPI negative) and group 2 (n = 27, LMPI positive). The clinical characteristics and qualitative MRI features were collected. In order to predict LMPI status in NF-PNETs, a multivariate logistic regression model was constructed. Diagnostic performance was evaluated by calculating the receiver operator characteristic (ROC) curve with area under ROC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. RESULTS There were significant differences in the lymph node metastasis stage, tumor grade, neuron-specific enolase levels, tumor margin, main pancreatic ductal dilatation, common bile duct dilatation, enhancement pattern, vascular and adjacent tissue involvement, synchronous liver metastases, the long axis of the largest lymph node, the short axis of the largest lymph node, number of the lymph nodes with short axis > 5 or 10 mm, and tumor volume between two groups (P < 0.05). Multivariate analysis showed that tumor margin (odds ratio = 11.523, P < 0.001) was a predictive factor for LMPI of NF-PNETs. The area under the receiver value for the predictive performance of combined predictive factors was 0.855. The sensitivity, specificity, PPV, NPV and accuracy of the model were 48.1% (14/27), 97.1% (33/34), 97.1% (13/14), 70.2% (33/47) and 0.754, respectively. CONCLUSION Using preoperative MRI, ill-defined tumor margins can effectively predict LMPI in patients with NF-PNETs.