Nonfunctioning pancreatic neuroendocrine tumors (NF-PanNETs) are often indolent neoplasms without lymph node (LN) metastasis at diagnosis. Therefore, in patients with low risk of LN metastasis, the extent of surgery and lymphadenectomy could be limited and follow-up adjusted to the very low risk of relapse.
To construct a predicting model to assess the risk of pN+ prior to surgical resection for NF-PanNETs using preoperative retrievable variables.
Retrospective review using multiple logistic regression analysis to construct predictive model of pN+ based on preoperatively available data.
The combined prospective databases of the Surgical Departments of the University of Verona, Verona, Italy, and Beaujon Hospital, Clichy, France, were queried for clinical and pathological data.
All patients with resected (R0 or R1), pathologically confirmed NF-PanNETs between January 1, 1993 and December 31, 2009.
Main Outcome and Measure
Risk of lymph node metastases in patients with pancreatic neuroendocrine tumors.
Among 181 patients, nodal metastases were reported in 55 patients (30%) and were associated with decreased 5-year disease-free survival (70% vs 97%, P < .001). Multivariable analysis showed that independent factors associated with nodal metastasis were radiological nodal status (rN) (odds ratio [OR], 5.58; P < .001) and tumor grade (NET-G2 vs NET-G1: OR, 4.87; P < .001) (first model). When the tumor grade was excluded, rN (OR, 4.73; P = .001) and radiological tumor size larger than 4 cm (OR, 2.67; P = .03) were independent predictors of nodal metastasis (second model). The area under the receiver operating characteristic curve for the first and second models were 80% and 74%, respectively.
Conclusions and Relevance
Patients with NF-PanNET-G1 have a very low risk of pN+ in the absence of radiological signs of node involvement. When preoperative grading assessment is not achieved, the radiological size of the lesion is a powerful alternative predictor of pN+. The risk of pathological nodal involvement in patients with NF-PanNETs can be accurately estimated by a clinical predictive model.