Machine Learning-Based Survival Prediction in Early-Stage Non-Small Cell Lung Cancer: Development and Cross-National...

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. However, prognostic models developed within a specific population may not be accurate when applied to another population due to differences in demographics and clinical practices. In the present study, we investigated the cross-national applicability of machine learning (ML)-based survival prediction models trained on population data from the United States and validated on an independent Chinese clinical cohort. Methods: Cox proportional hazards, Random Survival Forest (RSF), and XGBoost-Cox models were developed and externally validated. Model discrimination was evaluated using the concordance index (C-index) and time-dependent AUC at 1, 3, and 5 years, along with calibration and decision curve analysis. Hyperparameter tuning was performed using cross-validation to reduce overfitting and improve model generalizability. Results: Three survival prediction models were developed using the U.S. SEER database (n = 13,260) and externally validated in an independent Chinese cohort (n = 505). Baseline characteristics differed between the cohorts, with the Chinese cohort being younger and having a higher proportion of stage IA disease. Despite these differences, all models demonstrated acceptable discrimination. The RSF model was the most stable across cohorts and time horizons, with a C-index of 0.740 (95% CI: 0.735-0.746) in SEER and 0.782 (95% CI: 0.720-0.844) in the Chinese cohort. RSF showed good calibration at 1 and 3 years but slightly overestimated 5-year mortality risk in the Chinese cohort. Conclusions: Machine learning-based survival prediction models, such as the Random Survival Forest model, are promising and robust tools for predicting cross-population survival in early-stage non-small cell lung cancer (NSCLC). However, differences in patient characteristics and treatment patterns may influence long-term model performance. These findings highlight the potential of flexible machine learning models in oncology and the essential role of rigorous external validation.