The Lymph Node Ratio Optimizes Staging in Patients with Small Intestinal Neuroendocrine Tumors

Author:

Wu LunpoORCID,Chen Fei,Chen Shujie,Wang Liangjing

Abstract

Background: The effectiveness of the current Tumor, Lymph node, Metastases (TNM) staging system in small intestinal neuroendocrine tumors (SiNETs) is unsatisfactory. Current N classification only distinguishes between node-negative and node-positive status. We aim to refine the N classification for updated TNM stage. Methods: During the period from 1988 to 2012, patients with non-metastatic ­SiNETs were enrolled in the Surveillance, Epidemiology, and End Results database. Using the X-tile program, we calculated an optimal cutoff value for lymph node ratio (LNR) and proposed a novel Nr category. Survival outcomes were estimated using the Kaplan-Meier method and Cox regression model. Adjusted hazard ratio (HR) and cluster analysis were performed to differentiate TNrM stages. Results: Patients with existing TNM stage I and II had equivalent survival prognosis (p = 0.214). Current N classification was not a significant predictor of patient survival (p = 0.372). Multivariate analyses identified the revised Nr classification, based on LNR of 0.6 optimal cutoff value, as an independent prognostic factor (p = 0.020). By incorporating the Nr classification, a revised TNrM, which categorized patients into 3 new stages was proposed: stage I (T1–2Nr0–1), stage II (T3Nr0–1), and stage III (TxNr2 or T4Nrx). TNrM stage had better stratification according to the survival outcome (primary cohort: stage I: reference, II: HR 3.852, 95% CI 1.731–8.575; III: HR 7.169, 95% CI 3.220–15.963, p < 0.001; validation cohort: stage I: reference, II: HR 2.034; III: HR 3.815; p < 0.001). Conclusions: The Nr classification more accurately stratifies SiNET patients than current N classification. The new TNrM staging system could improve the ability to predict survival outcome of SiNET patients.

Publisher

S. Karger AG

Subject

Cellular and Molecular Neuroscience,Endocrine and Autonomic Systems,Endocrinology,Endocrinology, Diabetes and Metabolism

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