Dynamic assessment of long-term survival in survivors with stage III non-small cell lung cancer: a novel conditional survival model with a web-based calculator - Meng, Xiangdi; Wang, Peihe; Liu, Jie; Sun, Daqing; Ju, Zhuojun; Cai, Yuanyuan (Frontiers in Medicine, 2025)
BackgroundConditional survival (CS) analysis can estimate further survival probabilities based on the time already survived, providing dynamic updates for prognostic information. This study aimed to develop a CS-nomogram to promote individualized disease management for stage III non-small cell lung cancer (NSCLC).MethodsThis study included patients diagnosed with stage III NSCLC in the Surveillance, Epidemiology, and End Results database from 2010 to 2017 (N = 3,512). The CS was calculated as CS(y|x) = OS(y + x)/OS(x), where OS(y + x) and OS(x) were the overall survival (OS) in the year (y + x) and year x, respectively, calculated by the Kaplan–Meier method. We used the least absolute shrinkage and selection operator (LASSO) regression to identify predictors and developed the CS-nomogram based on these predictors and the CS formula.ResultsThe CS analysis provided real-time updates on survival, with 5-year OS improving dynamically from 14.4 to 29.9%, 47.9, 66.0, and 80.8% (after 1–4 years of survival). Six independent predictors (age, tumor size, N status, surgery, radiotherapy and chemotherapy) were identified for the development of the CS-nomogram and its web version (https://dynapp.shinyapps.io/NSCLC/). The model performed with an excellent concordance index (C-index) of 0.71 (95% CI: 0.70–0.72), and a median time-dependent AUC of 0.71–0.73 from 200 iterations 5-fold cross-validation.ConclusionThe study demonstrated the improvement in real-time OS over time in stage III NSCLC survivors and developed the novel CS-nomogram to provide patients with updated survival data. It provided novel insights into clinical decisions in follow-up and treatment for survivors, offering a convenient tool for optimize resource allocation.