Published online Dec 27, 2023. doi: 10.4240/wjgs.v15.i12.2809
Peer-review started: October 7, 2023
First decision: October 24, 2023
Revised: November 6, 2023
Accepted: December 6, 2023
Article in press: December 6, 2023
Published online: December 27, 2023
Processing time: 81 Days and 4.8 Hours
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 rela
To determine the feasibility of using preoperative MRI of the pancreas to predict LMPI in patients with non-functioning PENTs (NFPNETs).
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.
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.
Using preoperative MRI, ill-defined tumor margins can effectively predict LMPI in patients with NF-PNETs.
Core Tip: The correlation between comprehensive magnetic resonance imaging features and lymphatic, microvascular, and perineural invasion (LMPI) of non-functioning pancreatic neuroendocrine tumors (NF-PNETs) were analyzed. A multivariate model was constructed for predicting LMPI in NF-PNETs. Ill-defined tumor margins resulted as an independent risk factor for LMPI in patients with NF-PNETs.