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  • Title: Improving survival prognostication of gastroenteropancreatic neuroendocrine neoplasms: Revised staging criteria.
    Author: Martin JA, Warner RR, Wisnivesky JP, Kim MK.
    Journal: Eur J Cancer; 2017 May; 76():197-204. PubMed ID: 28334622.
    Abstract:
    PURPOSE: Current staging criteria for gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs), while useful, have limitations. In this study, we used a population-based registry to evaluate the prognostic utility of the current staging systems and assess whether evidence-based modifications can improve survival predictions. METHODS: We identified patients with confirmed GEP-NENs from the Surveillance, Epidemiology and End Results registry. We assigned tumour-node-metastasis status according to American Joint Committee on Cancer and European Neuroendocrine Tumor Society criteria. We derived a revised staging classification using Kaplan-Meier methods and Cox regression to assess disease-specific survival and compared the accuracy of potential models based on the Akaike Information Criterion (AIC) and Harrell's C-index. The revised classification was validated in an independent set. RESULTS: We identified 10,268 patients with GEP-NENs. We found that multiple stages, as determined by current criteria, misclassified patients' prognosis. In particular, stage IIIB (T1-4N1) had overlapping survival with stage IIIA (T4N0). A revised system which reclassifies N1 disease into different stages based on T status (T1-2N1, T3N1 and T4N1) had an improved AIC (difference = 38) and C-index (0.86) compared to current staging. These revisions improved predictions in patients with both low and high-grade tumours from all primary sites. Results also were confirmed across all primary sites in the validation set. CONCLUSION: Current staging guidelines misclassify the prognosis of N1 patients. Our results suggest that a revised system could lead to better prognostication for GEP-NEN patients. Further validation followed by implementation of these revisions may improve treatment selection and design of clinical trials.
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