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整合基因表达和数字组织学来预测乳腺癌的治疗特异性反应

Frederick M Howard, James Dolezal, Hanna Hieromnimon

medRxiv : the preprint server for health sciences

|September 5, 2025

概括

深度学习模型可以从组织学图像中预测基因表达特征,从而预测乳腺癌治疗反应. 这种数字病理学方法为分子检测提供了经济有效的替代方案,改善了精确医学.

科学领域:

癌症学
计算病理学
生物信息学

背景情况:

数字病理学和深度学习为分析病原体图像提供了新的途径.
基因表达特征 (GES) 是预测乳腺癌治疗反应的关键生物标志物.
传统的GES分子测试可能昂贵且耗时.

研究的目的:

开发和验证深度学习模型以推断H&E染色乳腺癌活检的基因表达特征.
评估基因组学衍生GES对新辅助化疗病理完整反应 (pCR) 的预测准确性.
探索基于组织学的GES在患者分层和精确治疗选择中的有用性.

主要方法:

优化基于变压器的深度学习模型以从数字组织学图像中推断GES.
在临床试验和现实世界中的1,940名乳腺癌患者的预治疗H&E染色活检中应用模型.
在多个独立群体中验证了pCR的预测模型,将性能与临床特征和病理学家的评估进行比较.

主要成果:

一个基于变压器的模型预测了HER2阴性患者的pCR,AUC为0. 794,超过了临床模型 (AUC为0. 704).
基因组学推导的GES将患者分为具有显著不同PCR概率的组 (顶部三分之一≥50%).
其他模型预测了化学疗法以外的特定疗法的反应,并将基因表达与组织学结合起来改善了预测.

结论:

通过深度学习获得的基因表达特征是乳腺癌中新辅助化疗反应的强大预测因素.
这种数字病理学方法为分子检测提供了成本效益和可访问的替代方案,提高了精确的治疗策略.
组织学衍生GES为基因表达数据提供了附加值,支持将其纳入各种乳腺癌亚型的临床决策.

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