预后和CA19-9拦截在胰腺管道腺癌的病理学签名:一个现实生活,多中心研究
Qiangda Chen1,2, Zhihang Xu1,3, Yiping Zou4
1Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 20, 2026
概括
一个人工智能模型分析了常规的H&E幻灯片,以预测胰腺癌存活率. 它确定了从化疗中受益的高风险患者和CA19-9监测的低风险患者,个性化治疗.
科学领域:
- 在瘤学瘤学.
- 数字病理学数字病理学
- 人工智能的人工智能
背景情况:
- 组织病理性血素和素 (H&E) 幻灯片为胰腺管道腺癌 (PDAC) 提供了预后见解.
- 从H&E幻灯片中系统地提取PDAC预后的特征是具有挑战性的.
- 在数字化全幻灯片图像上的深度学习为自动预后建模提供了机会.
研究的目的:
- 开发和验证用于PDAC预后的自动化深度学习模型,使用数字化H&E幻灯片.
- 为了确定预测患者生存的关键组织病理学特征.
- 评估该模型对患者进行分层,以定制治疗策略的能力.
主要方法:
- 一项多中心研究涉及873名PDAC患者进行手术切除.
- 在全幻灯片图像上使用CrossFormer深度学习架构开发和验证预测模型.
- 使用梯度加权类激活映射 (Grad-CAM) 进行特征解释.
主要成果:
- 与其他架构相比,CrossFormer模型实现了优越的外部验证性能 (AUC = 0.774).
- 确定的关键预后特征包括脱质性肌瘤,高核-细胞质比率,瘤缩和免疫细胞透.
- 病理学特征将患者分为不同的低风险和高风险组,具有显著的生存差异 (p < 0.001).
- 碳水化合物抗原19-9 (CA19-9) 仅在低风险患者中保留了预后价值.
- 高风险患者从辅助化疗中显著受益,而低风险患者没有.
结论:
- 经过验证,可解释的深度学习模型将H&E幻灯片转化为PDAC的定量预测工具.
- 可行的临床见解包括高风险患者的治疗强化和低风险患者的CA19-9引导监测.
- 这种方法可以实现个性化治疗策略,而无需额外的测试成本,从而改善患者的治疗结果.
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