Pancreatic neuroendocrine tumors (pNETs): the predictive value of MDCT characteristics in the differentiation of histopathological grades

To investigate the correlation between multiple detector computed tomography (MDCT) features of pancreatic neuroendocrine tumors (pNETs) and histopathologic grade and find valuable imaging criteria for grade prediction.

MDCT of 61 patients with 65 masses, which pNETs were approved histopathologically, underwent revision retrospectively. Each MDCT was evaluated for various radiologic characteristics. Absolute and relative (R: tumor/pancreas, D: tumor–pancreas) tumor enhancements were calculated in multiple post contrast phases.

61 patients [mean age = 50.70 ± 14.28 y/o and 30(49.2%) were male] were evaluated and classified into 2 groups histopathologically: G₁: 32 (49.2%) and G_2,3: 33 (50.8%). Significant relationships were observed between histopathologic tumor grade regarding age (p = 0.006), the longest tumor size (p = 0.006), presence of heterogeneity (p < 0.0001), hypodense foci in delayed phase (p = 0.004), lobulation (p = 0.002), vascular encasement (p < 0.0001), adjacent organ invasion (p = 0.01), presence (p < 0.0001) and number (0.02) of liver metastases, presence of lymphadenopathy with short axis of more than 10 mm (LAP) (p = 0.008), pathologic lymph node size (p = 0.004), relative (R and D) (p = 0.05 and 0.02, respectively), and percentage of arterial hyper-enhancing area (p = <0.0001). Tumor grades, however, had no significant relationship with gender, tumor location, tumor outline, calcification, cystic change, or pancreatic (PD) or biliary duct (BD) dilation (p = 0.21, 0.60, 0.05, 0.05 1, 0.10, and 0.51, respectively). Then, we suggested a novel imaging criteria consisting of six parameters (tumor size > 33 mm, relative (R) tumor enhancement in arterial phase ≤ 1.33, relative (D) tumor enhancement in arterial phase ≤ 16.5, percentage of arterial hyper-enhancing area ≤ 75%, vascular encasement, and lobulation), which specificity and accuracy of combination of all findings (6/6) for predicting G_2,3 were 100% and 70.1%, respectively. The highest accuracy (84.21%) was seen in combinations of at least 4 of 6 findings, with 80.00% sensitivity, 87.5% specificity, 83.33% PPV, and 84.85% NPV.

We suggested reliable imaging criteria with high specificity and accuracy for predicting the histopathologic grade of pNETs.