Preoperative prediction of lymph node metastasis in nonfunctioning pancreatic neuroendocrine tumors from clinical and MRI features: a multicenter study.

in Insights into imaging by Hai-Bin Zhu, Pei Nie, Liu Jiang, Juan Hu, Xiao-Yan Zhang, Xiao-Ting Li, Ming Lu, Ying-Shi Sun

TLDR

  • A novel MRI-based model was developed to predict lymph node metastasis in non-functioning pancreatic neuroendocrine tumors (NF-PNETs), achieving high accuracy in both the training and validation groups.

Abstract

The extent of surgery in nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs) has not well established, partly owing to the dilemma of precise prediction of lymph node metastasis (LNM) preoperatively. This study proposed to develop and validate the value of MRI features for predicting LNM in NF-PNETs. A total of 187 patients with NF-PNETs who underwent MR scan and subsequent lymphadenectomy from 4 hospitals were included and divided into training group (n = 66, 1 center) and validation group (n = 121, 3 centers). The clinical characteristics and qualitative MRI features were collected. Multivariate logistic regression model for predicting LNM in NF-PNETs was constructed using the training group and further tested using validation group. Nodal metastases were reported in 41 patients (21.9%). Multivariate analysis showed that regular shape of primary tumor (odds ratio [OR], 4.722; p = .038) and the short axis of the largest lymph node in the regional area (OR, 1.488; p = .002) were independent predictors for LNM in the training group. The area under the receiver operating characteristic curve in the training group and validation group were 0.890 and 0.849, respectively. Disease-free survival was significantly different between model-defined LNM and non-LNM group. The novel MRI-based model considering regular shape of primary tumor and short axis of largest lymph node in the regional area can accurately predict lymph node metastases preoperatively in NF-PNETs patients, which might facilitate the surgeons' decision on risk stratification.

Overview

  • The study aimed to develop and validate a method using MRI features to predict lymph node metastasis (LNM) in non-functioning pancreatic neuroendocrine tumors (NF-PNETs).
  • The study included 187 patients with NF-PNETs who underwent MRI scan and lymphadenectomy from 4 hospitals, divided into a training group (n=66) and a validation group (n=121).
  • The primary objective was to predict LNM in NF-PNETs patients preoperatively using MRI features, which could facilitate surgeons' decision-making on risk stratification.

Comparative Analysis & Findings

  • The multivariate analysis showed that regular shape of primary tumor (OR, 4.722; p=0.038) and short axis of the largest lymph node in the regional area (OR, 1.488; p=0.002) were independent predictors for LNM in the training group.
  • The area under the receiver operating characteristic curve (AUC) was 0.890 in the training group and 0.849 in the validation group, indicating a high accuracy of the MRI-based model.
  • The disease-free survival was significantly different between the model-defined LNM and non-LNM group, emphasizing the clinical relevance of the novel MRI-based model.

Implications and Future Directions

  • The study's findings suggest that the novel MRI-based model can accurately predict LNM in NF-PNETs patients, which can facilitate surgeons' decision-making on risk stratification.
  • Future studies can investigate the use of this model in different clinical settings, such as patients with multiple endocrine neoplasia type 1 (MEN1) or von Hippel-Lindau disease (VHL).
  • The study highlights the importance of developing personalized treatment plans for NF-PNETs patients based on their individual risk of LNM, which can lead to improved patient outcomes.
Read Full Article