Conditional survival nomogram for monitoring real-time survival of young non-metastatic nasopharyngeal cancer survivors

Background The aim of this study was to clarify the improvement of the overall survival (OS) over time in young non-metastatic nasopharyngeal carcinoma (NPC) survivors by conditional survival (CS) analysis and to construct a CS-nomogram for updating individualized real-time prognosis. Methods The study included 3409 young non-metastatic NPC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2019). OS was estimated using the Kaplan–Meier method. CS was calculated based on CS(y|x) = OS(y + x)/OS(x), defined as the probability that a patient would survive for another y years after surviving for x years since diagnosis. We identified predictors using the least absolute shrinkage and selection operator (LASSO) regression and developed the CS-nomogram using multivariate Cox regression and the CS formula. Results CS analysis showed a continuous increase in 10-year OS for young non-metastatic NPC from the initial 60.4% to 65.0%, 70.2%, 74.2%, 78.2%, 82.6%, 86.9%, 91.1%, 96.2% and 97.0% (surviving 1–9 years after diagnosis, respectively). After screening by LASSO regression, age, race, marital status, histological type, T- and N-status were used as predictors to construct the CS-nomogram. The model accurately estimated the real-time prognosis of survivors during follow-up with a stable time-dependent area under the curve (AUC). Conclusions CS analysis based on SEER database calibrated the real-time prognosis of young non-metastatic NPC survivors, revealing a dynamic improvement during follow-up time. We developed a novel CS-nomogram to update survival data for real-time optimization of monitoring strategies, medical resource allocation, and patient counseling. However, it was important to note that the model still needed external data validation and continuous improvement.