ArticleAdvances in radiation oncology2026
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.
Article in Advances in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: This 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 (BED Methods and Materials: In 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 BED Results: BED-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 [TCD Conclusions: This study confirms volume-dependent heterogeneity in dose response for BM and reveals a dual, baseline-dependent impact of BED
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