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在肺癌中使用基因组和生存数据对人工智能引导的化疗进行优化

Hojin Moon¹, Phan N Nguyen¹, Jaehee Park²

¹Department of Mathematics and Statistics, California State University, Long Beach 1250 Bellflower Blvd., Long Beach, CA 90840, USA.

Journal of personalized medicine

|June 25, 2025

概括

机器学习模型,特别是随机生存森林 (RSF),可以识别早期非小细胞肺癌 (NSCLC) 患者,这些患者可以从辅助化疗 (ACT) 中受益. 这种精确的瘤学方法有助于个性化治疗决策,以改善生存结果.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

辅助化疗 (ACT) 改善了早期非小细胞肺癌 (NSCLC) 的存活率,但患者的益处是高度可变的.
在精密瘤学中,确定适合ACT的候选者是一个重大挑战.

研究的目的:

开发和比较机器学习模型来预测存活率,并推早期NSCLC患者的ACT与观察 (OBS).
识别与治疗反应相关的基因组特征.

主要方法:

从两个公开的NSCLC基因表达数据集 (GSE37745,GSE29013) 构建了一个元数据库.
通过使用基于Cox的单变量选进行了特征选择,并进行了交叉验证.
开发并比较了三种生存模型:用弹性网进行包装惩罚了考克斯回归,随机生存森林 (RSF) 和DeepSurv神经生存网络.

主要成果:

RSF获得了最高的预测性能 (测试C指数=0.885).
基于模型的建议在训练和测试数据集中改善了生存率 (Kaplan-Meier分析).
确定了用于治疗反应分层的关键基因组特征 (TTR,MTURN,ETV3).

结论:

机器学习生存模型,特别是RSF,有效地识别了从ACT中受益的NSCLC患者.
这种数据驱动的方法支持早期NSCLC的个性化化疗决策.
有助于推进NSCLC的个性化治疗策略.

关键词:

人工智能在瘤学中的应用化疗的优化化疗法的优化.基因组生物标志物基因组生物标志物非小细胞肺癌 (NSCLC) 是一种非小细胞肺癌.生存分析,生存分析.

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