Advances in radiation oncology · 2026
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.
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Wu Fasheng, Shi Juzheng, He Weishan, Tang Yang, Deng Guangmei, Lin Xinlan, Kou Long, Huang Yuansheng, Hu Jia, Zhang Hui, Sheng Jinzhou, Xi Wenting, Lin Yajing, Zhu Xunxian, Liang Xiuping, Zhou Ying, Lu Ying, Long Fangfang, Jiang Huayan, Mo Yuanjun, Luo Shuang, Gao Yingchuan, Cai Yanling, Li Xuemei, Li Yumei, Hu Xiaoping, Lu Xuquan, Huang Yong, Huang Wenchuang, Tang Zhilu, Tang Minyang, Xie Youke, Cai Tongze, Liu Wenya, Liu Huiyi, Yuan Peipei, Chen Ye, Liang Hongbin, Chen Jibing
Аннотация
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 (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 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 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).
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 [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.
CONCLUSIONS: 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.
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 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).
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 [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.
CONCLUSIONS: 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.