Risk factors, prognostic factors, and nomograms for distant metastases in patients with gastroenteropancreatic neuroendocrine tumors: a population-based study

Abstract

Background: Patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) have a poor prognosis for distant metastasis. Currently, there are no studies on predictive models for the risk of distant metastasis in GEP-NETs.

Methods: In this study, risk factors associated with metastasis in patients with GEP-NETs in the Surveillance, Epidemiology, and End Results (SEER) database were analyzed by univariate and multivariate logistic regression, and a nomogram model for metastasis risk prediction was constructed. Prognostic factors associated with distant metastasis in patients with GEP-NETs were analyzed by univariate and multivariate Cox, and a nomogram model for prognostic prediction was constructed. Finally, the performance of the nomogram model predictions is validated by internal validation set and external validation set.

Results: A total of 9145 patients with GEP-NETs were enrolled in this study. Univariate and multivariate logistic analysis demonstrated that T stage, N stage, tumor size, primary site, and histologic types independent risk factors associated with distant metastasis in GEP-NETs patients (p value < 0.05). Univariate and multivariate Cox analyses demonstrated that age, histologic type, tumor size, N stage, and primary site surgery were independent factors associated with the prognosis of patients with GEP-NETs (p value < 0.05). The nomogram model constructed based on metastasis risk factors and prognostic factors can predict the occurrence of metastasis and patient prognosis of GEP-NETs very effectively in the internal training and validation sets as well as in the external validation set.

Conclusion: In conclusion, we constructed a new distant metastasis risk nomogram model and a new prognostic nomogram model for GEP-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.

Keywords: SEER; distant metastasis; gastroenteropancreatic neuroendocrine tumors; nomogram; prognosis.

Figure 1

Flow chart depicting the patient selection process. GEP-NETs, gastroenteropancreatic neuroendocrine tumors; SEER, Surveillance, Epidemiology, and End Results; TNM, Tumor Node Metastasis.

Figure 2

Construction and validation of a nomogram risk model for distant metastasis in GEP-NETs patients. (A) A nomogram risk prediction model for distant metastasis in GEP-NETs patients constructed on the basis of five independent risk factors (T stage, N stage, tumor size, primary site and histologic type). The model prediction performance was evaluated by ROC curves, calibration curves and DCA curves in the (B-D) training set and (E-G) validation set of the model group. AUC, area under curve. ***, p value<0.001.

Figure 3

In the (A) training and (B) validation sets of the model group, the discrimination between the model and independent risk factors (T stage, N stage, tumor size, primary site and histologic type) for predicting distant metastasis in GEP-NETs patients was compared by ROC curves. AUC, area under curve; ROC, receiver operating characteristic.

Figure 4

External dataset (validation group) to validate the nomogram risk model for distant metastases in GEP-NETs patients. The validation group assessed the model performance through (A) ROC curves, (B) calibration curves and (C) DCA curves. (D) Assessment of the differentiation between the model and the independent risk factors (T stage, N stage, tumor size, primary site and histologic type) through ROC curves. AUC, area under curve; ROC, receiver operating characteristic.

Figure 5

Construction and validation of a nomogram model for distant metastasis survival prediction in GEP-NET patients. (A) Construction of a nomogram survival prediction model for distant metastasis in patients with GEP-NETs based on independent prognostic factors (age, histologic type, tumor size, N stage, and primary site surgery). The accuracy of model survival predictions was assessed by plotting the model’s 1-,2- and 3-year prediction calibration curves in the (B–D) training and (E–G) validation sets of the model group. **, p value<0.01; ***, p value<0.001.

Figure 6

Evaluation of the predictive performance of the nomogram model for predicting survival of distant metastases in GEP-NETs patients. In the (A) training and (D) validation sets of the model group, ROC curves were used to assess the discrimination of 1-,2- and 3-year survival prediction in GEP-NETs patients with distant metastases. ROC curves assessed model and independent prognostic factors (age, histologic type, tumor size, N stage, and primary site surgery) for survival prediction differentiation in the (B) training and (E) validation sets of the model group. Comparison of differential survival of GEP-NETs patients of high- and low-risk groups in the model group according to median model survival prediction scores in the (C) training set and (F) validation sets of the model group (p value < 0.05). AUC, area under curve; ROC, receiver operating characteristic.

Figure 7

External dataset (validation group) to validate the nomogran survival prediction model for distant metastases in GEP-NETs patients (A–C) The calibration curves assessed the accuracy of model 1-2-and 3-year sunival predictions in the validation group. In the validation group, (D) ROC curves were used to assess the discrimination of model 1-2-and 3-yeur survival predichom, (E) and to compare model discrimination with independent prognostic factors (age, histologic type, tuanor size, N stage, and primary site surgery) (F) In the validation group, the differential survival in the high and low-risk groups was compared according to the median value of the model's survival prediction score (p value <0.05) AUC, arca under curve.