Journal
FRONTIERS IN ENDOCRINOLOGY
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fendo.2022.925632
Keywords
pancreatic neuroendocrine tumor (PNET); prognosis; survival; recurrence; ENETS
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This study aimed to investigate the prognosis and optimal treatment of pancreatic neuroendocrine tumors (PNET). The results showed that larger tumor size, Ki67 index, tumor grade, and stage were predictive of shorter survival, and chromogranin A was a predictor of overall survival. Furthermore, the study supported the active surveillance for small, localized, low-grade PNETs.
IntroductionThe European Neuroendocrine Tumor Society, ENETS, reports variables of prognostic significance in pancreatic neuroendocrine tumors (PNET). However, studies have short follow-ups, and the optimal treatment remains controversial. We aimed to determine overall survival (OS), progression-free survival (PFS) after conservative treatment, and recurrence-free survival (RFS) after surgery and further to find predictors of aggressive PNET behavior to support treatment decisions. Methods174 patients with PNET treated at Aarhus University Hospital from 2011 to 2021 were included in a retrospective cohort study. Patients were divided into surgically resected (SUR, n=91) and medically or conservatively treated (MED, n=83). Variables were tested in univariate and multivariate survival analysis. Median follow-up time was 3.4 years in the MED group and 4.5 years in the SUR group. ResultsThe 5-year OS was 95% and 65% for the SUR and MED groups, respectively. The 5-year RFS in the SUR group was 80% whereas the 5-year PFS in the MED group was 41%. Larger tumor size, Ki67 index, tumor grade, and stage were predictive of shorter OS, RFS, and PFS. Further, chromogranin A was a predictor of OS. Larger tumor size was associated with higher stage and grade. Only 1 of 28 patients with stage 1 disease and size <= 2 cm developed progression on a watch-and-wait strategy during a median follow-up of 36 months. ConclusionThis study supported the ENETS staging and grading system to be useful to predict OS, PFS, and RFS in PNET. Further, our data support that small, localized, low-grade PNETS can be followed with active surveillance.
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