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科学领域:

  • 计算病理学计算病理学
  • 基因组分析 基因组分析
  • 人工智能在瘤学中的应用

背景情况:

  • 人工智能 (AI) 模型可以从病理幻灯片中快速,低成本地预测基因组变化,从而有可能加速肺癌治疗决策.
  • 人工智能模型在不同患者群体和组织类型中的通用性在很大程度上是未知的.

研究的目的:

  • 评估两个开源AI病理学模型的性能和通用性,以预测肺腺癌 (LUAD) 中的EGFR突变状态.
  • 评估跨独立队列和多样化的祖先子组的模型性能.

主要方法:

  • 一项队列研究包括来自两个独立队列 (Dana-Farber癌症研究所和欧洲一项试验) 的LUAD患者,并配对下一代测序和全幻灯片成像数据.
  • 用DFCI队列中的生殖系基因型数据推断了遗传祖先.
  • 模型性能以接收器运行特征曲线 (AUC) 下的面积进行测量,总体评估,按祖先子组和样本类型进行评估.

主要成果:

  • 一个AI模型在DFCI队列中实现了较高的整体AUC (0.83) 与另一种 (0.68) 相比.
  • 在祖先子组中,表现各不相同,表现较高的模型显示AUC为0.84 (欧洲),0.85 (非洲) 和0.68 (亚洲).
  • 与肺样本 (AUC,0.86) 相比,体样本 (AUC,0.66) 的模型性能下降. 人工智能引导的分组表明,快速EGFR测试的潜在减少率为57%,具有高灵敏度 (0.84) 和特异性 (0.99).

结论:

  • 基于AI的病理学工具显示出在肺癌中预测EGFR的初步辅助工具的潜力.
  • 祖先子组之间的性能差异需要仔细解释和进一步验证.
  • 人工智能工具可以简化EGFR测试工作流程,提高肺癌管理的效率.