Development of a machine learning-based prognostic model for survival prediction in patients with lung cancer brain metastases using multicenter clinical data

Methods: Accurate prognosis prediction for lung cancer brain metastasis (LCBM) patients is critical for clinical decision-making. This study integrates data from the SEER database (n = 2624) and Harbin Medical University Cancer Hospital (n = 362) to develop a machine learning-based prognostic prediction tool. Prognostic factors were selected through Cox regression analysis, and eight prediction models, including XGBoost, Random Forest, and Logistic Regression, were constructed. Performance was evaluated using AUC, learning curves, and PR curves, while the impact of lymph node metastasis was explored through propensity score matching and KaplanMeier survival analysis. Results: Risk factors identified included age >= 60 years, T3 stage, and multiple organ metastases, while protective factors included female gender and household income >=$100,000. The XGBoost model demonstrated superior performance, with mean AUCs of 0.957 (Model 1) and 0.550 (Model 2). The XGBoost-Surv model showed stable performance in both the training set (C-index = 0.653, AUC = 0.731) and the test set (C-index = 0.634, AUC = 0.705). Lymph node metastasis significantly affected prognosis (p < 0.001), though differences in metastatic stages were not statistically significant (p = 0.935). Conclusion: The XGBoost model developed from multicenter data effectively predicts survival outcomes in LCBM patients, with lymph node metastasis serving as an independent prognostic indicator. This model provides a reliable tool for personalized treatment decision-making.