Development and validation of a simple-to-use nomogram to predict liver metastasis in patients with pancreatic neuroendocrine neoplasms: a large cohort study

• Published in: BMC gastroenterology Vol. 21; no. 1; p. 101
• Main Authors: Pan, Maoen, Yang, Yuanyuan, Teng, Tianhong, Lu, Fengchun, Chen, Yanchan, Huang, Heguang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.03.2021; BioMed Central; BMC
• Subjects: Cohort analysis; Cohort Studies; Epidemiology; Gastroenterology; Humans; Liver; Liver cancer; Liver metastasis; Liver Neoplasms; Medical prognosis; Metastases; Metastasis; Neoplasm Staging; Neuroendocrine neoplasms; Neuroendocrine tumors; Nomogram; Nomograms; Pancreas; Pancreatic cancer; Pancreatic tumors; Patients; Prognosis; Quality of life; Regression analysis; Risk factors; SEER databases; Software; Statistical analysis; Statistics; China; Neuroendocrine neoplasms; SEER databases; Pancreas; Nomogram; Liver metastasis
• ISSN: 1471-230X; 1471-230X
• DOI: 10.1186/s12876-021-01685-w

Liver metastasis is an important prognostic factor for pancreatic neuroendocrine neoplasms (pNENs), but the relationship between the clinical features of patients with pNEN and liver metastasis remains undetermined. The aim of this study was to establish and validate an easy-to-use nomogram to predict liver-metastasis in patients with pNEN. We obtained the clinicopathologic data of 2960 patients with pancreatic neuroendocrine neoplasms from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2016. Univariate and multivariate logistic regression were done to screen out independent influencing factors to establish the nomogram. The calibration plots and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of nomogram. Decision curve analysis (DCA) was applied to compare the novel model with the conventional predictive methods. A total of 2960 patients with pancreatic neuroendocrine neoplasms were included in the study. Among these, 1974 patients were assigned to the training group and 986 patients to the validation group. Multivariate logistic regression identified, tumor size, grade, other site metastasis, T stage and N stage as independent risk factors. The calibration plot showed good discriminative ability in the training and validation groups, with C-indexes of 0.850 for the training cohort and 0.846 for the validation cohort. The AUC values were 0.850 (95% CI 0.830-0.869) and 0.839 (95% CI 0.812-0.866), respectively. The nomogram total points (NTP) had the potential to stratify patients into low risk, medium risk and high risk (P < 0.001). Finally, comparing the nomogram with traditional prediction methods, the DCA curve showed that the nomogram had better net benefit. Our nomogram has a good ability to predict liver metastasis of pancreatic neuroendocrine neoplasms, and it can guide clinicians to provide suitable prevention and treatment measures for patients with medium- and high-risk liver metastasis.