Risk factors for lymph node metastasis in rectal neuroendocrine tumors: A recursive partitioning analysis based on multicenter data.

Background: The well-differentiated rectal neuroendocrine tumors (RNETs) can also have lymph node metastasis (LNM). Large multicenter data were reviewed to explore the risk factors for LNM in RNETs. Further, we developed a model to predict the risk of LNM in RNETs.

Methods: In total, 223 patients with RNETs from the Fujian Medical University Union Hospital, the First Affiliated Hospital of Fujian Medical University, and the First Affiliated Hospital of Xiamen University were retrospectively enrolled. Logistic regression analysis was performed to study the factors affecting LNM, and recursive partitioning analysis (RPA) was performed to stratify the risk of LNM.

Results: Among the 223 patients diagnosed with RNETs, the incidence of LNM was 10.8%. Univariate and multivariate regression analyses revealed that tumor size, World Health Organization (WHO) grade, and depth of tumor invasion were independent risk factors for LNM (p < 0.05). The area under the curve was 0.948 (95% confidence interval: 0.890-1.000). Furthermore, the incidence of LNM in patients divided into low- and high-risk groups according to RPA was 1.1% and 56.4%, respectively.

Conclusion: Compared with tumor size, the depth of tumor invasion and WHO grade are more important factors in predicting LNM. Then, we developed a model based on RPA to predict the risk of LNM in RNETs and identify patients who are suitable for local resection.