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Updated: Aug 26, 2025
Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
Published on: April 18, 2015
Preoperative prediction of lymph node metastasis in nonfunctioning pancreatic neuroendocrine tumors from clinical and
Hai-Bin Zhu1, Pei Nie2, Liu Jiang3,4
1Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
