从单一癌症到全癌症的预后:一种多模式的深度学习框架,用于生存分析,具有强大的概括能力
Binyu Zhang1, Shichao Li1, Junpeng Jian1
1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
The American journal of pathology
|July 12, 2025
概括
准确的癌症预后对于个性化医学至关重要. 统一的多模式泛癌存活网络 (UMPSNet) 整合了各种数据,用于优越的泛癌存活预测,即使是在新的癌症类型中.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 准确的癌症预后对于个性化治疗和资源分配至关重要.
- 现有的预后模型受限于模式特定的设计和癌症类型特定的培训,阻碍了概括.
- 开发能够整合多样化的数据类型并跨癌症进行概括的模型是一个重大挑战.
研究的目的:
- 引入统一的多模式泛癌生存网络 (UMPSNet) 以提高预后预测.
- 通过整合组织病理学图像,基因组数据和元数据来克服现有模型的局限性.
- 证明UMPSNet能够在不同癌症类型和数据源中进行概括.
主要方法:
- UMPSNet使用结构化文本模板集成了基因组学图像,基因组表达形状和元数据.
- 基于运输的最佳注意力用于多式联运特征对齐.
- 一个指导性的专家混合机制解决了癌症类型分布的变化.
主要成果:
- 在5个癌症基因组图谱队列中,UMPSNet实现了0.725的平均一致性指数,超过了单一癌症模型.
- 在对胰腺癌的零射击转移评估中,UMPSNet在没有微调的情况下实现了0.652的一致性指数.
- 通过UMPSNet识别的预后基因特征显示与已知的突变重叠,并揭示了新的候选者.
结论:
- UMPSNet为瘤学中的多式生存分析建立了一个新的范式.
- 该框架有效地克服了数据异质性和领域转移的挑战.
- UMPSNet提供了一个临床上适应的工具,用于泛癌预后预测和精确瘤学.
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