一个多式人工智能生物标志物PATH-ORACLE改善了第一阶段肺腺癌复发的预测
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
一种新的深度学习方法,PATH-ORACLE,使用组织学和转录学准确预测肺腺癌复发. 这个工具可以帮助指导早期肺癌患者的辅助治疗决策.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 病理学 病理学 病理学
背景情况:
- 第一个阶段的肺腺癌通常在手术后复发,存活率很低.
- 目前的预测指标对于风险分层和指导辅助治疗缺乏准确性.
- 结合不同的诊断数据可以提高预后能力.
研究的目的:
- 开发一种多式深度学习生物标志物,用于预测肺腺癌复发.
- 为了提高早期肺癌患者的风险分层.
- 为指导个性化的辅助治疗策略.
主要方法:
- 开发了一种多式联络深度学习模型PATH-ORACLE.
- 集成的转录基因数据 (ORACLE评分) 与常规组织学切口.
- 使用预训练的基础模型进行组织学图像分析.
主要成果:
- 在两个独立的队列中,PATH-ORACLE在预测复发方面取得了超过85%的准确性.
- 与现有方法相比,已经证明了改善风险分层的潜力.
- 确定了引导IB阶段辅助化疗和IA阶段向治疗的潜在潜力.
结论:
- PATH-ORACLE提供了一个高度准确的多式生物标志物,用于肺腺癌复发的预测.
- 这种工具可以使IB阶段的患者在辅助化疗中获得更一致的优先级.
- 结合液体活检,它可以识别高风险阶段IA患者的辅助向治疗.
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