Clinicopathological features and prognostic implications of KI-67 in advanced gastroenteropancreatic neuroendocrine tumors grade 2
DOI:
https://doi.org/10.69482/onkoresearch.v3i1.60Keywords:
Neuroendocrine tumors, Grade 2, Ki-67 Antigen, ROC Curve, Survival AnalysisAbstract
Introduction: Well-differentiated gastroenteropancreatic neuroendocrine tumors (GEP NET) of grade 2 (G2) are a heterogeneous group of tumors with variable clinicopathological features and survival outcomes. Objective: The primary objective of the study was to describe the clinicopathological characteristics and overall survival (OS) in an advanced GEP NET G2 cohort. The secondary objective was to determine the optimal Ki-67 index cutoff for predicting prognosis using ROC curve analysis. Methods: A retrospective analysis was conducted on patients diagnosed with advanced GEP NET G2 between 2012 and 2024 at the Instituto Nacional de Enfermedades Neoplásicas (INEN). A descriptive analysis was performed for qualitative and quantitative clinicopathological variables. The optimal Ki-67 index cutoff for discriminating OS was identified through ROC curve analysis. OS was estimated using the Kaplan-Meier method and compared using the log-rank test. Results: A total of 41 patients with advanced GEP NET G2 were included. The mean age was 53.4 years; 53.7% of the patients were female, and 85% had metastatic disease at diagnosis. The most common primary site was the rectum (36.6%). Firstline treatment was given to most patients (95.1%), with TEMCAP (temozolomide/capecitabine) being the most commonly used (94.9%). ROC curve analysis identified 5.5% as the optimal Ki-67 index cutoff (AUC:0.646; 95%CI:0.468–0.825; sensitivity:60%; specificity:71.4%). Patients with Ki-67 ≥6% had a median OS of 41 months vs 53 months in those with Ki-67 <6% (p=0.053). Conclusions: Our findings suggest a trend toward shorter OS in patients with higher Ki-67 values.
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Copyright (c) 2025 Wagner Eduardo Cruz-Diaz, Alexandra Saavedra, Victor Paitan, Juan Haro-Varas, Raul Mantilla, Angela Leonardo, Jackeline Macetas, Eder Veramendi, Cristian Pacheco, Monica Calderon, Tatiana Vidaurre, Victor Castro-Oliden

This work is licensed under a Creative Commons Attribution 4.0 International License.







