Incidence, survival, and prognostic factors for patients with gastrointestinal mixed neuroendocrine non-neuroendocrine neoplasms: a SEER population-based study

Abstract

Background: Mixed neuroendocrine non-neuroendocrine neoplasms (MiNENs) are a group of rare tumors with limited research currently available. This study aimed to analyze the incidence, survival, and prognostic factors of gastrointestinal MiNENs.

Methods: We included data from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019. We compared the clinicopathologic characteristics and survival rates between MiNENs and neuroendocrine tumors (NETs), and calculated the incidence of MiNENs. We utilized univariate and multivariate Cox analysis to assess independent factors of prognosis and established a nomogram to predict 1-, 2-, and 3-year cancer-specific survival (CSS). Calibration and receiver operating characteristic (ROC) curves were drawn to validate the accuracy and reliability of the model. Decision curve analysis (DCA) was used to assess the clinical utility of the model.

Results: Patients with gastrointestinal MiNENs had a poorer prognosis than those with NETs. The overall incidence of gastrointestinal MiNENs has been increasing annually. Multivariate Cox regression analysis revealed that tumor size, lymph node metastasis, distant metastasis, and surgery were independent risk factors for CSS in MiNENs patients. Based on these risk factors, the 1-, 2-, and 3-year CSS nomogram model for MiNENs patients was established. Calibration, ROC, and DCA curves of the training and validation sets demonstrated that this model had good accuracy and clinical utility.

Conclusion: Gastrointestinal MiNENs are rare tumors with an increasing incidence rate. The nomogram model is expected to be an effective tool for personalized prognosis prediction in MiNENs patients, which may benefit clinical decision-making.

Keywords: Incidence; Mixed neuroendocrine non-neuroendocrine neoplasms; Nomogram; Prognostic factors; Survival.

Fig. 1

Inclusion and exclusion procedures for MiNENs patients from SEER database. SEER Surveillance, Epidemiology, and End Results, MiNENs mixed neuroendocrine non-neuroendocrine neoplasms, NETs neuroendocrine tumors

Fig. 2

Incidence and trend of MiNENs between 2000 and 2019 (per 1,000,000 person-years). MiNENs mixed neuroendocrine non-neuroendocrine neoplasms

Fig. 3

Incidence and trends of different subgroups of MiNENs patients. a Incidence and trends of MiNENs patients of different ages; b incidence and trends of MiNENs patients of different genders; c incidence and trends of MiNENs in different sites. MiNENs mixed neuroendocrine non-neuroendocrine neoplasms

Fig. 4

KM survival curves comparing the OS and CSS of patients in different pathological subgroups. a OS of patients with MiNENs and NETs; b CSS of patients with MiNENs and NETs. KM Kaplan–Meier, OS overall survival, CSS cancer-specific survival, MiNENs mixed neuroendocrine non-neuroendocrine neoplasms, NETs neuroendocrine tumors

Fig. 5

A nomogram for predicting the 1-, 2-, and 3- year CSS in patients with MiNENs. CSS cancer-specific survival, MiNENs mixed neuroendocrine non-neuroendocrine neoplasms

Fig. 6

Calibration curves for evaluating the accuracy of the nomogram. a–c The calibration curves for predicting the 1-, 2-, 3-year CSS in the training set; d–f the calibration curves for predicting the 1-, 2-, 3-year CSS in the validation set. CSS cancer-specific survival

Fig. 7

ROC curves for evaluating the discriminability of the nomogram. a ROC curves for the 1-, 2-, and 3- year CSS in the training set; b ROC curves for the 1-, 2-, and 3- year CSS in the validation set. ROC receiver operating characteristic, AUC area under the curve, CSS: cancer-specific survival

Fig. 8

DCA curves for evaluating the potential clinical value of the nomogram. a–c DCA curves of the nomogram and AJCC TNM staging system for predicting the 1-, 2-, and 3-year CSS in the training set; d–f DCA curves of the nomogram and AJCC TNM staging system for predicting the 1-, 2-, and 3-year CSS in the validation set. DCA decision curve analysis, CSS cancer-specific survival