Survival Prediction in Brain Metastasis Patients Treated with Stereotactic Radiosurgery: A Hybrid Machine Learning Approach.

Abstract

Objectives: Accurate survival prediction for brain metastasis patients undergoing stereotactic radiotherapy (SRT) is crucial for personalized treatment planning and improving patient outcomes. This study aimed to develop a machine learning model to estimate survival times, providing clinicians with a reliable tool for making informed decisions based on individual patient characteristics. The goal was to compare the performance of multiple algorithms and identify the most effective model for clinical use.

Methods: We applied a hybrid machine learning approach to predict survival in brain metastasis patients treated with SRT, utilizing real-world data. Four algorithms-XGBoost, CatBoost, Random Forest, and Gradient Boosting-were compared within a meta-model framework to identify the most accurate for survival prediction. Model performance was evaluated using metrics such as MSE, MAE, MAPE, and C index.

Results: XGBoost outperformed all other algorithms, achieving an MSE of 0.14%, MAE of 0.10%, and MAPE of 0.093%, with a high C-index of 100%. CatBoost showed reasonable performance, while Gradient Boosting had higher error rates (MSE of 6.99%, MAE of 21.04%, MAPE of 19.29%). Random Forest performed the weakest, with the highest MSE (14.39%), MAE (30.23%), and MAPE (33.58%).

Conclusion: Inputting relevant clinical variables into the model enables clinicians to obtain highly accurate survival predictions for patients with brain metastasis. This enhances clinical decision making by providing a more precise understanding of expected outcomes. The XGBoost-based hybrid model showed exceptional accuracy in predicting survival for brain metastasis patients after SRT, offering valuable support for clinical decision making. Integrating machine learning into clinical practice can improve treatment planning and personalize care for these patients.