Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features

Background: Metastatic liver tumor burden (LTB) is a prognostic factor affecting the survival of gastroenteropancreatic neuroendocrine tumors (GEP-NETs), but evaluation of the LTB usually depends on radiologic and functional imaging. This study aimed to develop a clinical model based on easily accessible clinicopathological markers to predict LTB level in GEP-NET patients. Methods: LTB was quantified based on Ga-68-DOTANOC PET/CT scan. The optimal cut-off value for high and low-LTB was determined based on our previous study. Serum levels of liver enzymes and tumor biomarkers were obtained within one week before PET/CT scan. The whole dataset was divided into training set and validation set. LASSO regression method was used to select predictors, and multivariate logistic regression was used to develop a clinical model which was further visualized by constructing a nomogram. Area under the curve (AUC) was applied to assess the accuracy of the constructed model. Results: We retrospectively enrolled 200 patients with well-differentiated GEP-NETs. Ki-67 index, GGT (gammaglutamyltransferase), LDH (lactate dehydrogenase), and NSE (neuron-specific enolase) were selected through the LASSO regression method, and a nomogram was built based on these variables. The predictive model yielded an AUC of 0.785 (95% CI, [0.708-0.862]) in the training set, and 0.783 (95% CI, [0.644-0.923]) in the validation set. Additionally, with the optimal cut-off values based on the nomogram total points, patients were categorized as LTBhigh (total points >= 26.2) and LTBlow (total points < 26.2) groups presenting significantly different OS. Conclusion: A clinically applicable nomogram incorporating four clinicopathological characteristics was constructed to predict GEP-NET patients with high-LTB. The nomogram may help clinicians identify patients with high-LTB and take optimal treatment measures to improve prognosis.