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

作者：Nuerailaguli Jumai, Luohai Chen, Xiaoxuan Lin, Qiao He, Man Liu, Yuan Lin, Yanji Luo, Yu Wang, Minhu Chen, Xiangsong Zhang, Zhirong Zeng, Ning Zhang · 发表于：BMC Cancer · 年份：2025 · DOI：10.1186/s12885-025-14535-9 · 被引用次数：1 · 研究领域：Neuroendocrine Tumor Research Advances、Lung Cancer Research Studies、Neuroblastoma Research and Treatments

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 68Ga-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 (gamma-glutamyltransferase), 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, pa...