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基于下一代测序的测试在美国的高级或转移性非状非小细胞肺癌患者中:使用机器学习方法进行预测建模.

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概括
此摘要是机器生成的。

机器学习确定了在高级非小细胞肺癌 (NSCLC) 中预测下一代测序 (NGS) 测试的因素. 公平获得NGS测试对于所有患者至关重要,无论其人口统计数据或保险状况如何.

关键词:
进行NGS测试.人工智能的人工智能是人工智能.生物标志物 生物标志物肺癌是一种肺癌.机器学习是机器学习.这是下一代测序.瘤学 在瘤学方面.预测建模预测建模现实世界的数据数据.治疗指南 治疗指南瘤生物标志物的生物标志物

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科学领域:

  • 在瘤学瘤学.
  • 基因组学就是基因组学.
  • 机器学习 机器学习

背景情况:

  • 下一代测序 (NGS) 对于晚期肺癌治疗至关重要.
  • 目前的指导方针建议NGS用于晚期或转移性非小细胞肺癌 (NSCLC).

研究的目的:

  • 确定在晚期NSCLC患者中进行NGS测试的人口和临床预测因素.
  • 确定影响NGS测试的可能性和时间 (早期或晚期) 的因素.

主要方法:

  • 在现实世界NSCLC患者数据上利用机器学习 (逻辑回归,LASSO,XGBoost).
  • 分析了永远与永远以及早期与晚期NGS测试的预测因素.
  • 使用接收器操作曲线下的面积来评估模型性能.

主要成果:

  • 确定了NGS测试的预测因素,包括诊断年份,吸烟史和PD-L1测试.
  • 诸如年龄较大,表现较低,黑人种族和公共保险等因素与NGS测试较少有关.
  • 接受NGS测试的患者中,有84%的患者进行了早期测试.

结论:

  • 机器学习模型始终确定了先进NSCLC中NGS测试的预测因素.
  • 确保公平获得NGS测试至关重要,解决与年龄,种族,保险和地理位置相关的差异.