A novel nomogram based on machine learning predicting overall survival for hepatocellular carcinoma patients with...

The principal aim of the present study was to develop and validate a nomogram predicting overall survival (OS) in patients with α-fetoprotein (AFP)-negative hepatocellular carcinoma (AFP-NHCC) who experience dynamic changes in AFP level after hepatectomy. A cohort of 870 patients were enrolled and randomly assigned into a training cohort (n=600) and a validation cohort (n=270) at a 7:3 ratio. The key variables contributing to the nomogram were determined through random survival forest analysis and multivariate Cox regression. The discriminative ability of the nomogram was evaluated using time-dependent receiver operating characteristic curves and the area under the curves. Furthermore, the nomogram was comprehensively assessed using the concordance index (C-index), calibration curves and clinical decision curve analysis (DCA). Kaplan-Meier (KM) curves analysis was employed to discern survival rates across diverse risk strata of patients. Ultimately, the nomogram incorporated critical factors including sex, tumor size, globulin levels, gamma-glutamyl transferase and fibrinogen levels. In the training and validation cohorts, the C-indexes were 0.72 [95% confidence interval (CI): 0.685-0.755) and 0.664 (95% CI: 0.611-0.717], respectively, attesting to its predictive validity. The nomogram demonstrated excellent calibration and DCA further confirmed its clinical usefulness. Additionally, KM curve analysis unveiled statistically significant differences in OS among three distinct risk groups. In conclusion, the present study successfully formulated a nomogram predicting 3-, 5- and 8-year OS in patients with AFP-NHCC with dynamic changes in AFP level post-local resection. This model serves as a valuable tool for clinicians to promptly identify high-risk patients, thereby facilitating timely interventions and potentially enhancing patient survival outcomes.