Prognostic relevance of a novel TNM classification system for upper gastroenteropancreatic neuroendocrine tumors
- PMID: 18506737
- DOI: 10.1002/cncr.23549
Prognostic relevance of a novel TNM classification system for upper gastroenteropancreatic neuroendocrine tumors
Abstract
Background: Neuroendocrine tumors (NETs) of the gastroenteropancreatic (GEP) system comprise a rare but challenging group of malignant neoplasms and occur at virtually any site of the GEP system. In 2006, a new TNM classification system was proposed for the staging and grading of upper GEP NETs.
Methods: The prognostic relevance of the TNM classification system was analyzed retrospectively in 202 patients from a referral center with histologically proven foregut NET. Patients were classified according to previous classification systems and the TNM classification. Survival data were acquired and statistical analyses were performed by using log-rank and Cox regression testing.
Results: Primary tumors were gastric (n = 48), duodenal (n = 23), and pancreatic (n = 131). During the observation period, 21% of patients died. The overall 5- and 10-year survival rates were 75% and 64%, respectively. Previous classification systems discriminated between low-grade and high-grade malignant NETs but did not allow further prognostic differentiation. In contrast, the proposed TNM classification was able to differentiate significantly between different tumor stages (stages I-III vs stage IV; P < .01) and cellular proliferation rates according to Ki-67 labeling (grade 1 vs grade 2, P = .04; grade 1 vs grade 3 and grade 2 vs grade 3, P < .01). Cox regression analysis confirmed an increased risk of reduced survival for patients with stage III or IV NET and grade 2 or 3 NET.
Conclusions: The current results demonstrated the prognostic relevance of the newly proposed TNM classification system for foregut NETs with statistical significance for the subgroups of both the staging classification and the grading system. Thus, the new classification system provides a valid and powerful tool for prognostic stratification of GEP NETs in clinical practice and research.
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