Interpretable machine learning prognostication of gastroenteropancreatic neuroendocrine tumors across Chinese and United States cohorts.

BACKGROUND: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) display marked clinical heterogeneity, and conventional prognostic indicators such as TNM stage and Ki-67 index provide limited individualized risk discrimination. We aimed to develop and externally validate interpretable machine learning models for survival prediction. METHODS: This retrospective study enrolled 337 patients with histologically confirmed GEP-NETs, randomly partitioned into a training set (n = 236) and an independent test set (n = 101) with stratification by mortality. Six survival models, including unpenalized Cox regression as a baseline, LASSO Cox, random survival forest (RSF), gradient-boosted survival analysis (GBSA), Extra Trees, and DeepSurv, were trained with 5×5 nested cross-validation. Discrimination, probabilistic accuracy, calibration, and clinical utility were comprehensively evaluated. SHAP analysis identified key predictors and informed nomogram construction. External validation was performed in 51,225 SEER patients. RESULTS: On the test set, Extra Trees achieved the highest discrimination (C-index 0.843, 95% CI 0.762-0.914) and balanced calibration (1-year ICI 0.031). Time-dependent AUC values were 0.871, 0.847 and 0.935 at 1, 3 and 5 years. The Extra Trees-based stratification identified high-risk patients with substantially worse survival (HR 8.93, 95% CI 3.35-23.84, P<0.001). The model outperformed TNM stage (C-index 0.772), Ki-67 (0.705) and tumor grade (0.699). Decision curve analysis demonstrated greater net benefit across threshold probabilities of 5%-50%. SHAP analysis identified T stage, tumor grade, M1 status, TNM stage and Ki-67 as the top predictors. External validation in SEER preserved discriminative ability (C-index 0.719) and excellent calibration (slopes 0.901-1.058). CONCLUSION: Interpretable machine learning models outperform conventional approaches in predicting GEP-NET survival. Extra Trees showed the best internal discrimination and calibration, whereas DeepSurv achieved the highest external C-index. The simplified SHAP-derived nomogram provides a practical and well-calibrated but exploratory tool for individualized prognosis that requires prospective validation before clinical use.