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  • Author: Hongyun Ma x
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Xu Han, Xuefeng Xu, Hongyun Ma, Yuan Ji, Dansong Wang, Tiantao Kuang, Wenchuan Wu, Bin Song, Gang Li, Gang Jin and Wenhui Lou

Purpose

Emerging evidence suggests G3 pancreatic neuroendocrine neoplasms (pNENs) present heterogeneous morphology and biology. The 2017 WHO classification has introduced a new category of well-differentiated pancreatic neuroendocrine tumors (WD-pNETs) G3, compared with poorly differentiated pancreatic neuroendocrine carcinomas (PD-pNECs) G3. We aim to analysis the demographics and outcomes of patients with resectable 2017 WHO G3 pNENs to facilitate the distinction between two entities.

Methods

The multi-institutional retrospective cohort involving 57 surgically treated patients affected by 2017 WHO G3 pNENs were morphologically identified and clinically analyzed. Patients having WD-pNETs G3 and those having PD-pNECs G3 were compared.

Results

Thirty patients had WD-pNETs and 27 patients had PD-pNECs. The distributions of Ki-67 and mitotic count in patients with PD-pNECs or WD-pNETs showed remarkable disparities. ROC indicated cut-off value of Ki-67 was 45. PD-pNECs were more common in patients with elevated Ki-67 and mitotic count, advanced AJCC TNM stage, vascular invasion, regional lymph-node metastases, elevated NSE and decreased CgA levels compared with WD-pNETs (P < 0.05). The association between 2017 WHO G3 grade and TTR was statistically significant (P < 0.05). Univariate analysis indicated OS rates were associated with morphologic differentiation (WD-pNETs vs PD-pNECs), Ki-67, TNM staging, synchronous distant metastases, initial treatments, vascular invasion, regional lymph nodes metastases, mitotic count and age (P < 0.05). Multivariate analyses illustrated Ki-67, differentiation, TNM staging and vascular invasion were independent predictors (P < 0.05).

Conclusions

PD-pNECs G3 presented malignant biological behavior and dismal outcome compared with WD-pNETs G3. These findings challenge 2010 WHO classification and suggest the categorization can be improved by refined tumor grading.