Objectives: The aim of the study was to discriminate hepatic metastases from pancreatic neuroendocrine tumors (pNET) and hepatic metastases from midgut neuroendocrine tumors (mNET) with magnetic resonance imaging (MRI).
Methods: MRI examinations of 24 patients with hepatic metastases from pNET were quantitatively and qualitatively assessed by 2 blinded readers and compared to those obtained in 23 patients with hepatic metastases from mNET. Inter-reader agreement was calculated with kappa and intraclass correlation coefficient (ICC). Sensitivity, specificity, and accuracy of each variable for the diagnosis of hepatic metastasis from pNET were calculated. Associations between variables and primary tumor (i.e., pNET vs. mNET) were assessed by univariate and multivariate analyses. A nomogram was developed and validated using an external cohort of 20 patients with pNET and 20 patients with mNET.
Results: Interobserver agreement was strong to perfect (k = 0.893-1) for qualitative criteria and excellent for quantitative variables (ICC: 0.9817-0.9996). At univariate analysis, homogeneity on T1-weighted images was the most discriminating variable for the diagnosis of pNET (OR: 6.417; p = 0.013) with greatest sensitivity (88%; 21/24; 95% CI: 68-97%). At multivariate analysis, tumor homogeneity on T1-weighted images (p = 0.007; OR: 17.607; 95% CI: 2.179-142.295) and target sign on diffusion-weighted images (p = 0.007; OR: 19.869; 95% CI: 2.305-171.276) were independently associated with pNET. Nomogram yielded a corrected AUC of 0.894 (95% CI: 0.796-0.992) for the diagnosis of pNET in the training cohort and 0.805 (95% CI: 0.662-0.948) in the validation cohort.
Conclusions: MRI provides qualitative features that can help discriminate between hepatic metastases from pNET and those from mNET.
Keywords: Liver neoplasms; Magnetic resonance imaging; Multivariate analysis; Neuroendocrine tumors; Pancreatic neoplasms.
© 2020 S. Karger AG, Basel.