Stereotactic Radiation Therapy for Lung Cancer Brain Metastases: Dose Optimization and Prognostic Prediction via Biologically Effective Dose-Based Empirical Dose-Response Modeling and Extreme Gradient Boosting-SHapley Additive Explanations Machine Learning.

Abstract

PurposeThis study aimed to integrate BED-based empirical dose-response modeling (logistic regression-derived tumor control probability) with interpretable machine learning (ML) to investigate the complex interactions among biologically effective dose (BED₁₀), tumor volume, neurologic function, and targeted therapy patterns in stereotactic radiosurgery (SRS) for lung cancer brain metastases (BM), thereby optimizing personalized treatment strategies.Methods and materialsIn this retrospective study, 449 lung cancer patients with BM treated with CyberKnife between June 2006 and March 2025 were enrolled. Stratified BED-based empirical dose-response models based on maximum tumor diameter (≤2 cm, 2-3 cm, >3 cm) were established to analyze BED₁₀ and local control relationship. The extreme gradient boosting (XGBoost) algorithm was employed to build predictive models for early Karnofsky performance status (KPS) decline and tumor control, incorporating BED₁₀, baseline neurologic status, and targeted therapy patterns. Model interpretability was achieved using SHapley Additive exPlanations (SHAP).ResultsBED-based empirical dose-response modeling revealed significant volume dependence in dose response. Tumors of 2 to 3 cm required the highest dose for 50% control (dose required for 50% tumor control [TCD₅₀]: 62.52 Gy for 1-year; 62.48 Gy for 2-year). The ML model excellently predicted early KPS decline (area under the curve, 0.874). SHAP analysis demonstrated that BED₁₀ effect on neurologic outcomes was modulated by baseline function: lower BED₁₀ (30-60 Gy) increased KPS decline risk in patients with poor baseline neurologic function (grade ≥2), whereas higher BED₁₀ (60-90 Gy) was protective in those with good function (grade ConclusionsThis study confirms volume-dependent heterogeneity in dose response for BM and reveals a dual, baseline-dependent impact of BED₁₀ on functional outcomes. The integration of ML with BED-based empirical dose-response modeling provides valuable insights for individualized dose prescription and combined modality strategies, supporting continuous targeted therapy with high BED₁₀ as a key approach for optimizing local control.