Objective To explore the risk factors for very early recurrence (VER) after curative-intent resection for gallbladder cancer (GBC) patients and construct prediction models for VER based on various machine learning (ML) algorithms. Methods A retrospective study was conducted on 329 GBC patients who underwent curative-intent surgery at our hospital between January 2016 and December 2020. Risk factors for VER were identified, and prediction models were constructed, validated and compared with multiple ML algorithms[logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), light gradient boosting machine (LGB), and extreme gradient boosting (XGB)]based on independent associated factors for VER. Results Among the 329 patients who underwent curative-intent resection in patients with GBC, 162 (49.2%) patients experienced recurrence, including 69 (42.6%) with VER（<6 months） and 93 (57.4%) with non-VER（≥6 months）. Survival analysis showed that patients with VER had significantly worse median overall survival compared to those with non-VER (6 months vs. not arrived，χ2=398.2, P<0.001). Univariate analysis showed that carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA-125, tumor differentiation, pathological type, liver involvement, vascular invasion, perineural invasion, TNM stage, T stage and N stage were risk factors of VER (P<0.05), whereas adjuvant chemotherapy was protective factor (P<0.05). Multivariate analysis confirmed CA-125, tumor differentiation, pathological type, vascular invasion and N stage as independent risk factors (P<0.05), whereas adjuvant chemotherapy was independent protective factor (P<0.05). XGB model achieved the best performance with an area under curve (AUC) of 0.841 and an accuracy (ACC) of 83.0% in the validation set. Shapley additive explanations (SHAP) bar plots highlighted tumor differentiation, N stage, pathological type of tumor, and CA-125 the top four features contributing to the model, each positively influencing the predicted probability of VER. Conclusions CA-125, tumor differentiation, pathological type, vascular invasion, N stage and adjuvant chemotherapy are independent factors associated with VER of GBC following curative-intent resection. ML-based prediction models incorporating these factors have the potential to some extent to effectively identify high-risk patients, providing a valuable reference for VER surveillance in GBC.
