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
Pancreatic neuroendocrine tumors (PNETs) are comparatively rare neoplasms. Lymphatic, microvascular, and perineural invasion (LMPI) was significantly correlated with the prognosis of PENTs which was confirmed by previous studies. There was no previous study reported the relationship between magnetic resonance imaging (MRI) parameters and LMPI.
The key problem is whether preoperative MRI of the pancreas can predict LMPI in patients with non-functioning NF-PNETs.
The main objective is to determine the feasibility to predict lymphatic, microvascular and perineural invasion in patients with non-functioning PENTs (NF-PNETs) by using preoperative MRI of the pancreas. MRI is a non-invasive imaging modality, and there will be more broad application prospects.
The comprehensive clinical indicators and MRI parameters of patients with NF-PNETs were collected. A multivariate logistic regression model was established and the diagnostic performance was evaluated.
Patients were divided into two groups according to the LMPI state. Irregular margin (P < 0.001) and heterogeneous enhancement pattern (P = 0.011) were more likely to be seen in a patient with LMPI. The long axis of the largest lymph node was significantly larger (7.26 ± 5.27 vs 4.15 ± 3.29, P = 0.006) in patients with LMPI. According to the multivariate logistic regression analysis, tumor margin (odds ratio = 11.523; 95% confidence interval: 2.966-44.761, P < 0.001) was an independent factor associated with LMPI of NF-PNETs.
The relationship between MRI features and LMPI of NF-PNETs was elaborated. The tumor margin, which is one of the MRI features, has an important role in predicting LMPI in patients with NF-PNETs.
This study evaluated the relationship between preoperative clinical indicators, MRI parameters and LMPI in NF-PNETs. To evaluate the correlation between MRI features and lymphatic invasion, microvascular invasion and perineural invasion respectively.