Prognostic impact of primary tumor resection and nomogram development for survival prediction in colonic neuroendocrine carcinoma patients with liver metastasis: a SEER database-based study.

OBJECTIVE: This study aimed to investigate the prognostic impact of primary tumor resection in colonic neuroendocrine carcinoma (NECs) patients with liver metastasis, compare survival outcomes between left- and right-sided tumors, and develop a nomogram for predicting overall survival (OS) in this population. METHODS: Data from 449 colonic NECs patients with liver metastasis (2010-2021) were extracted from the SEER database. Patients were stratified by primary tumor location (left vs. right colon) and surgical resection status. Propensity score matching (PSM) was applied to balance the groups and subsequently investigate survival differences between them. Survival differences were analyzed using Kaplan-Meier curves and Cox regression. A nomogram integrating independent prognostic factors was developed using LASSO regression and validated for 6-, 12-, and 24-month OS prediction. Model performance was assessed via ROC curves, calibration plots, and decision curve analysis (DCA). RESULTS: Right-sided colonic NECs predominated (85.1%, n = 382) and exhibited better OS than left-sided tumors (before PSM: 7.0 m vs. 6.0 m, p = 0.038; after PSM: 8.0 m vs. 6.0 m, p = 0.031). Primary tumor resection independently improved survival in both left colon (9.0 m vs. 5.0 m for no surgery, p = 0.012) and right colon (9.0 m vs. 6.0 m for no surgery, p < 0.001) groups. The nomogram incorporated age, tumor size, and resection status, demonstrating moderate-to-strong predictive accuracy (AUC: 0.623-0.755 in training cohort; 0.691-0.760 in validation cohort). Calibration curves and DCA confirmed clinical utility. CONCLUSION: Right-sided colonic NECs with liver metastasis have superior survival compared to left-sided tumors. Primary tumor resection is associated with prolonged OS, regardless of tumor location. The validated nomogram provides a practical tool for individualized prognosis prediction, supporting clinical decision-making in this high-risk population.