Pancreatic neuroendocrine tumor: Correlations between MRI features, tumor biology, and clinical outcome after surgery

Abstract

Purpose: To assess which magnetic resonance imaging (MRI) features are associated with pNETs (pancreatic neuroendocrine tumors) grade based on the WHO classification, as well as identify MRI features related to disease progression after surgery. Materials and Methods: In this Institutional Review Board (IRB)-approved study, 1.5T and 3.0T MRI scans of 80 patients with surgically verified pNETs were assessed. The images were evaluated for tumor location; size; pattern; predominant signal intensity on precontrast T1- and T2-weighted images, as well as on postcontrast arterial and portal venous phase T1-weighted sequences; presence of pancreatic duct dilatation; pancreatic atrophy; restricted diffusion; vascular involvement by the tumor; extrapancreatic tumor spread; and synchronous liver metastases. Tumors were graded based on the WHO classification and patients were followed-up with computed tomography (CT) or MRI after surgical resection. Data were analyzed with Student's t and chi-square tests, logistic regression, and Kaplan–Meier curves. Results: The MRI features that were associated with aggressive tumors were: size >2.0 cm (odds ratio [OR] = 4.8, P = 0.002), “T2 nonbright lesions” on T2-weighted images (OR = 4.6, P = 0.008), presence of pancreatic ductal dilatation (OR = 4.9, P = 0.024), and restricted diffusion within the lesion (OR = 4.9, P = 0.013). Differences in progression-free survival distribution were found for patients whose pNETs were associated with the following MRI features: size >2.0 cm (χ2 (1) = 6.0, P = 0.014), “nonbright lesions” on T2-weighted images (χ2 (1) = 6.8, P = 0.009), and presence of pancreatic duct dilatation (χ2 (1) = 10.9, P = 0.001). Conclusion: MRI features can be used to assess pNETs aggressiveness and identify patients at risk for early disease progression after surgical resection. Level of Evidence: 3. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2018;47:425–432.