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Updated: Jan 10, 2026

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使用放射学和可解释的集体学习来预测放射性肺炎和NSCLC患者在VMAT后的生存率.

Tsair-Fwu Lee1,2,3, Lawrence Tsai1, Po-Shun Tseng1

  • 1Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist, Kaohsiung 80778, Taiwan.

Life (Basel, Switzerland)
|November 27, 2025
PubMed
概括

这项研究开发了一个精确的模型,使用放射学和人工智能来预测辐射肺炎和VMAT后非小细胞肺癌患者的生存率. 该模型实现了高精度,增强了个性化放射治疗.

关键词:
组合学习组合学习可解释的人工智能肺癌是一种肺癌.机器学习是机器学习.辐射性肺炎的发生无线电学 (radiomics) 是一种无线电学.生存分析,生存分析.容量调节弧线疗法是指体积调节的弧线疗法.

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
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