Qualitative and quantitative analysis of enhanced computed tomography in differentiating and grading pancreatic...

Accurate preoperative differentiating and grading of pancreatic neuroendocrine neoplasms (pNENs) is crucial. This study aims to explore the efficacy of Computed Tomography (CT) in distinguishing Grade1 (G1) pancreatic neuroendocrine tumors (pNETs) and pancreatic neuroendocrine carcinomas (pNECs) from other pNENs before appropriate interventions.

A total of 241 patients with pNENs were enrolled in this study. All pathological diagnoses were re-reviewed according to the 2022 WHO Classification of Endocrine and Neuroendocrine Tumours. CT image characteristics including size, location, morphology, distal pancreatic duct dilation, vascular involvement, homogeneity, cystic or necrotic change, conspicuity, calcification, the Hounsfield Unit (HU) value of the tumor and the parenchyma, arterial phase (AP) ratio and portal venous phase (PVP) ratio were assessed. Univariate and multivariate analyses were performed between G1 and G2/G3/NEC, and between NET and NEC.

Tumor size (per 1-cm increase; OR = 0.60, 95% CI: 0.50-0.83; p = 0.023), tumor location (head/neck vs. body/tail; OR = 0.39, 95% CI: 0.17-0.61; p < 0.001), peripancreatic vascular involvement (present vs. absent; OR = 0.13, 95% CI: 0.06-0.48; p = 0.005), cystic or necrotic changes (present vs. absent; OR = 0.24, 95% CI: 0.07-0.83; p = 0.036) were four independently associated features for predicting G1. Age (OR = 1.08, 95% CI: 1.02-1.14; p = 0.017) and PVP ratio (per 0.1 increase; OR = 0.43, 95% CI: 0.29-0.65; p = 0.021) were independent factors for differentiating pNECs.

Qualitative variables, particularly tumor location, peripancreatic vascular involvement, and cystic or necrotic changes, played a crucial role in distinguishing G1 pNETs. Quantitative variables, especially PVP ratio, were more significant in differentiating pNECs.