Development and validation of a prognostic model for esophageal cancer patients with liver metastasis: a cohort study based on surveillance, epidemiology, and end results database

PurposeOur objective is to examine the independent prognostic risk factors for patients with Esophageal Cancer with Liver Metastasis (ECLM) and to develop a predictive model.MethodsIn this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was employed to identify independent prognostic factors and construct nomograms based on the results of multivariate regression. The predictive performance of the nomograms was assessed using several methods, including the consistency index (C-index), calibration curve, time-dependent receiver-operating characteristic curve (ROC), and decision curve analysis (DCA). Additionally, Kaplan-Meier survival curves were generated to demonstrate the variation in overall survival between groups.ResultsA total of 1163 ECLM patients were included in the study. Multivariate Cox analysis revealed that age, tumor differentiation grade, bone metastasis, therapy, and income were independently associated with overall survival (OS) in the training set. Subsequently, a prognostic nomogram was constructed based on these independent predictors. The C-index values were 0.739 and 0.715 in the training and validation sets, respectively. The area under the curve (AUC) values at 0.5, 1, and 2 years were all higher than 0.700. Calibration curves indicated that the nomogram accurately predicted OS. Decision curve analysis (DCA) showed moderately positive net benefits. Kaplan-Meier survival curves demonstrated significant differences in survival between high- and low-risk groups, which were divided based on the nomogram risk score.ConclusionsThe nomogram we developed for ECLM patients has demonstrated good predictive capability, allowing clinicians to accurately evaluate patient prognosis and identify those at high risk, thereby facilitating the development of personalized treatment plans.