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Erschienen in:
23.03.2020 | Endocrine Tumors
Accuracy of Grading in Pancreatic Neuroendocrine Neoplasms and Effect on Survival Estimates: An Institutional Experience
Erschienen in: Annals of Surgical Oncology | Ausgabe 9/2020
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Background
Accurate grading of neuroendocrine neoplasms (NENs) is crucial for proper assessment of prognosis. Estimation of the proliferative indices, if not performed properly, is largely erroneous due to significant intratumoral heterogeneity. We sought to establish the degree of error in the grading of a cohort of curatively resected pancreatic NENs (PanNENs) and the theoretical impact of that in a larger cohort of Surveillance, Epidemiology, and End Results (SEER) patients.
Methods
A retrospective query of an institutional surgical database was performed from 2000 to 2018 to identify optimally resected PanNENs, which were reviewed by two gastrointestinal pathologists and regraded according to the WHO 2017 classification. Overall survival and recurrence-free survival were estimated using the Kaplan–Meier method for original and new grading systems, respectively and Cox proportional hazards models were used to evaluate the effect of the interested variables, including new grading systems.
Results
A total of 176 cases were identified. After regrading, 17/64 (26.6%) G1 neoplasms were classified as G2 and 12/95 (12.6%) G2 neoplasms were classified as G1, while 1/11 (9.1%) G3 neoplasms were classified as G2. Our expert gastrointestinal pathologists agreed on 97% of reclassified cases by blind review. Application of the G1/G2 misclassification errors on various groups, including PanNENs, in a SEER database of 1385 patients rendered the reported survival differences nonsignificant (1000 repetitions; p = 0.063, 95% confidence interval 0.056–0.070).
Conclusions
Mischaracterization of grade is common in optimally resected PanNENs but is eliminated with proper training and adherence to guidelines. The discrepancy rates can cast doubt on the generally accepted survival differences between G1 and G2 patients, as surmised by large database analyses.
