Risk of Recurrence in Pituitary Neuroendocrine Tumors: A Prospective Study Using a Five-Tiered Classification.
prospective_cohort · Level II
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- Record sourced from PubMed, PMID 28651368.
- Also identified by DOI 10.1210/jc.2017-00773.
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Abstract
Most pituitary neuroendocrine tumors (PitNETs) show benign behavior, but a substantial number are invasive, recur, or resist medical treatment. Based on a retrospective case-control study, we recently proposed a classification of PitNETs of prognostic relevance. This prospective study aims to test the value of this classification in an independent patient cohort. All patients who underwent PitNET surgery from 2007 to 2012 in one single center were included. Using a grading system based on invasion on magnetic resonance imaging, immunocytochemical profile, Ki-67, mitotic index, and p53 positivity, tumors were classified. Progression-free survival of the graded tumors was calculated by the Kaplan-Meier method and compared using the log-rank test. A multivariate analysis, using a Cox regression model, was also performed. In total, 365 patients had grade 1a PitNETs (51.2%), followed by grade 2a (32.3%), 2b (8.8%), and 1b tumors (7.7%). Of 213 patients with a follow-up, 42% had recurrent (n = 52) or progressive disease (n = 37) at 3.5 years. Grade was a significant predictor of progression-free survival (P < 0.001). Multivariate analysis indicated grade (P < 0.001), age (P = 0.035), and tumor type (P = 0.028) as independent predictors of recurrence and/progression. This risk was 3.72-fold higher for a grade 2b tumor compared with grade 1a tumor. Our data suggest that classification of PitNETs into five grades is of prognostic value to predict postoperative tumor behavior and identifies patients who have a high risk of early recurrence or progression. It therefore will allow clinicians to adapt their therapeutic strategies and stratify patients in future clinical trials.
Medical subject headings
- Neoplasm Recurrence, Local
- Neuroendocrine Tumors
- Pituitary Gland
- Pituitary Neoplasms