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.

Authors

Fasheng Wu, Juzheng Shi, Weishan He, Yang Tang, Guangmei Deng, Xinlan Lin, Long Kou, Yuansheng Huang, Jia Hu, Hui Zhang, Jinzhou Sheng, Wenting Xi, Yajing Lin, Xunxian Zhu, Xiuping Liang, Ying Zhou, Ying Lu, Fangfang Long, Huayan Jiang, Yuanjun Mo, Shuang Luo, Yingchuan Gao, Yanling Cai, Xuemei Li, Yumei Li, Xiaoping Hu, Xuquan Lu, Yong Huang, Wenchuang Huang, Zhilu Tang, Minyang Tang, Youke Xie, Tongze Cai, Wenya Liu, Huiyi Liu, Peipei Yuan, Ye Chen, Hongbin Liang, Jibing Chen

Published in

Advances in radiation oncology. Volume 11. Issue 12. Pages 102147. Epub Jul 23, 2026.

Abstract

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 (BED₁₀), tumor volume, neurologic function, and targeted therapy patterns in stereotactic radiosurgery (SRS) for lung cancer brain metastases (BM), thereby optimizing personalized treatment strategies.
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 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).
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 [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 <2). Continuous targeted therapy combined with high BED₁₀ showed the most favorable synergistic effect on tumor control.
This 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.

PMID:
42644064
Bibliographic data and abstract were imported from PubMed on 26 Aug 2026.

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