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Updated: Jan 31, 2026

Human Neuroendocrine Tumor Cell Lines as a Three-Dimensional Model for the Study of Human Neuroendocrine Tumor Therapy
Published on: August 14, 2012
Factors Affecting Survival in Neuroendocrine Tumors: A 15-Year Single Center Experience
Abdullah Sakin1, Makbule Tambas, Saban Secmeler
1Department of Medical Oncology, Yuzuncu Yil University Medical School, 65090, Van, Turkey.
Abstract:
Background: Neuroendocrine tumors are a heterogeneous group of tumors that can originate from all of the neuroendocrine cells in the body, mostly from the gastrointestinal tract. In addition to early diagnosis, streaming patients into appropriate prognostic groups is an important component of treatment. In this study, we examined the factors that affect survival in patients we followed in our center between 2000-2016. Methods: The demographic data, clinical and pathological features of patients were obtained from their medical files. TNM staging and tumor grading were performed according to AJCC and WHO 2010 classification. SPSS 15.0 for Windows programme was used for statistical analysis. Results: 85 patients (32 male, 53 female) were included into the study. The median age of the patients was 55,7 (27-83) years. Eighty percent of the tumors were of gastroenteropancreatic system, most commonly stomach (27.1%) origin. Nineteen patients (22.4%) died during follow-up. In univariate analysis; age (p<0,001), stage (p=0.002), primary tumor localization (p=0.005), grade (p<0.001), Ki-67 value (p<0.001), number of metastases (p=0.001) and type of surgery (p<0.001) were found to be factors affecting survival. Age (p=0.024) and Ki67 (p <0.001) were the independent prognostic factors for survival in multivariate analysis. For the cut-off value of 6%, Ki-67 had a sensitivity of 83.3% and specifity of 71.4% for survival determination. Conclusion: Ki-67 ratio and age were the most important factors affecting survival in neuroendocrine tumors in our study. Ki-67 ratio has a high sensitivity and specificity for predicting survival, a cut-off value of 6% may be used to predict survival.
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