Dynamic conditional survival nomogram for primary hepatocellular carcinoma: a population-based analysis.

Background And Purpose: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide. While 5-year overall survival (OS) is a common prognostic metric, it does not reflect the evolving prognosis of long-term survivors. The study aimed to evaluate dynamic changes in real-time survival among HCC patients over time using conditional survival (CS) analysis and to develop an individualized, time-updated prognostic model.

Methods: A total of 11,926 patients with primary HCC were included and randomly assigned to training (70%) and validation (30%) cohorts. CS was defined as the probability of surviving additional years given time already survived [CS(t|t) = OS(t + t)/OS(t), where OS(t) represents the survival probability at t-years from diagnosis, and OS(t + t) represents survival at (t + t)-years]. Univariable and multivariable Cox regressions were used to identify independent prognostic factors and construct a CS-nomogram. Model performance was assessed using area under the curve (AUC), calibration curves, and decision curve analysis.

Results: CS analysis showed that real-time survival rate increased significantly with each additional year survived, with 5-year CS rising from 35.1% at diagnosis to 52.9%, 66.7%, 79.2%, and 90.2% after 1-4 years of survival. Eleven prognostic factors were included in the final model (all p < 0.05). The CS-nomogram demonstrated strong discrimination (5-year AUC > 0.84 in both cohorts) and good calibration. An interactive web-based tool was developed to facilitate clinical application.

Conclusion: CS analysis offers more accurate and dynamic prognostic information for HCC patients. The CS-nomogram provides personalized, time-adjusted survival estimates, supporting more informed decision-making and survivorship care.