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
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 · 2026
Read 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 by 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 (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13505368.txt.
Description
Fasheng Wu leads a large team that re-examined radiation dosing for lung cancer that had spread to the brain. They reviewed 449 patients treated with focused CyberKnife radiation between 2006 and 2025. Two tools were combined: a dose-response model based on biologically effective dose, and an interpretable machine-learning method that explains which factors drive its forecasts.
Dose needs varied with tumour size, and tumours between two and three centimetres across required the highest dose for a fifty percent chance of control. The learning model predicted early decline in daily functioning well. Its explanations showed dose effects flipping with starting condition: lower doses raised decline risk in patients already weak, while higher doses protected those starting strong. Continuous targeted drug therapy paired with high dose gave the best tumour control. The authors offer the combined approach as a guide for tailoring dose to each patient.
Catalog record
| CREATOR | 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 |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 66567 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |