Prognostic role and predictors of lymph node involvement in pancreatic neuroendocrine tumors.

PURPOSE: To define the prognostic role of lymph node involvement (LNI) in patients with pancreatic neuroendocrine tumors (PNETs) and identify predictors of LNI using a comprehensive multifactor analysis focusing on preoperative radiological features. METHODS: This study included 236 patients with preoperative computed tomography who underwent radical surgical resection of PNETs at our hospital between 2009 and 2019. Univariate and multivariable logistic regression analyses were performed to investigate the risk factors associated with LNI and tumor recurrence. The disease-free survival (DFS) rates with and without LNI were compared. RESULTS: Forty-four of the 236 patients (18.6%) had LNI. Biliopancreatic duct dilatation (odds ratio [OR], 2.295; 95% confidence interval [CI], 1.046-5.035; p = 0.038), tumor margin (OR, 2.189; 95% CI, 1.034-4.632; p = 0.041), and WHO grade (G2: OR, 2.923; 95% CI, 1.005-8.507; p = 0.049; G3: OR, 12.067; 95% CI, 3.057-47.629; p < 0.001) were independent risk factors of LNI in PNETs. Multivariable analysis showed that LNI (OR, 2.728; 95% CI, 1.070-6.954; p = 0.036), G3 (OR, 4.894; 95% CI, 1.047-22.866; p = 0.044), and biliopancreatic duct dilatation (OR, 2.895; 95% CI, 1.124-7.458; p = 0.028) were associated with PNET recurrence in patients after surgery. Patients with LNI had a significantly worse DFS than those without LNI (3-year DFS: 85.9 vs. 96.7%; p < 0.001; 5-year DFS: 65.1 vs. 93.9%; p < 0.001). CONCLUSION: LNI was associated with decreased DFS. Biliopancreatic duct dilatation, irregular tumor margins, and grades G2 and G3 were independent risk factors for LNI.

Clinicopathological features and long-term prognosis of purely cystic pancreatic neuroendocrine tumors: A single-center experience.

BACKGROUND: Pancreatic neuroendocrine tumors (pNETs) are heterogenous neoplasms, of which the prognosis varies widely. Purely cystic pancreatic neuroendocrine tumors (C-pNETs) are a small subset of pNETs in which data are extremely rare. This study aimed to compare clinicopathological and long-term survival differences between C-pNETs and solid pNETs (S-pNETs). METHODS: A retrospective review of 242 patients with pNETs underwent resection in our institution from 2009 to 2019 was conducted. Demography characteristics, clinicopathological features and long-term outcomes of them were analyzed. RESULTS: Sixteen out of 242 patients (6.6%) were identified as C-pNETs. Compared with S-pNETs, C-pNETs were more frequently non-functional (75% vs 45%, P = 0.02), and the median tumor diameter of C-pNETs was smaller (36 mm vs. 47 mm, P = 0.001). And the accuracy of preoperative diagnosis of C-pNETs was significantly lower (31% vs 78%, P = 0.001). Of note, the majority of C-pNETs were well-differentiated with G1 (81% vs 35%, P = 0.001). And there were no G3 (0 vs 7%, P = 0.001) in C-pNETs. No T4 stage or R1/R2 surgical margin detected in C-pNETs. And only one C-pNETs (6%) had regional lymph node metastasis (N) or synchronous distant metastasis (M). Additionally, only one patient with C-pNETs (6%) suffered tumor recurrence, compared with 24 (13%) for S-pNETs. And survival analysis showed the patients with C-pNETs seemed to be with better disease-free survival (P = 0.26). CONCLUSION: C-pNETs are rare subtype with possibly less aggressive behavior comparing with their solid counterparts. Recurrence and tumor-related death still occurs in patients with resected C-pNETs, although they tend to be with more favorable prognosis.

Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.

Backgroud: Tumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited. Methods: The models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram. Results: A total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908-0.914) and 0.837 (95% CI, 0.827-0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916-0.922; validation set: AUC = 0.875, 95% CI = 0.867-0.883). Conclusions: Our developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.