基因特征在血素和色染色图像中的空间映射:使用附加多实例学习进行可解释预测的概念验证
Miles Markey1, Juhyun Kim1, Zvi Goldstein1
1PathAI, Boston, Massachusetts.
概括
机器学习可以从组织学图像中预测癌症相关纤维细胞 (CAF) 亚型. 这种方法将组织特征与基因表达联系起来,为癌症免疫治疗提供可解释的生物标志物.
科学领域:
- 计算病理学计算病理学
- 癌症生物学 癌症生物学
- 机器学习在瘤学中
背景情况:
- 癌症相关纤维细胞 (CAF) 亚型显著影响瘤治疗反应,特别是免疫疗法.
- 目前CAF亚型的基因表达特征作为临床生物标志物未得到充分利用.
研究的目的:
- 开发一个可解释的机器学习模型,从整个幻灯片图像中预测CAF亚型.
- 专注于预测免疫抑制性LRRC15+CAF丰富的TGFβ-CAF特征.
主要方法:
- 利用添加式多重实例学习 (aMIL) 方法来预测来自H&E染色全幻灯片图像的基因表达特征.
- 采用机器学习模型来分析细胞类型的空间分布,结构亚型和与TGFβ-CAF预测相关的核形态.
主要成果:
- aMIL模型准确地预测了各种癌症类型的TGFβ-CAF特征.
- 在"激发性" (TGFβ-CAF高) 和"抑制性" (TGFβ-CAF低) 区域确定了不同的组织学特征,特别是在乳腺癌中.
- 在激发性和抑制性区域之间观察到纤维细胞和淋巴细胞核形态的差异.
结论:
- aMIL方法提供了基于数据的组织学特征和基因表达之间的联系,使生物解释性成为可能.
- 这种方法为开发用于癌症免疫治疗的新型,可解释的生物标志物提供了潜在的途径.
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