M3Surv:将多幻灯片和多omics融合为增强记忆的强大生存预测
Mingcheng Qu1, Guang Yang1, Donglin Di2
1Faculty of Computing, Harbin Institute of Technology, Harbin, China.
Medical image analysis
|October 22, 2025
概括
M3Surv集成了多病理幻灯片 (新鲜冷和甲固定的嵌) 与多omics数据,以改善癌症生存预测. 该框架可稳定处理缺失的数据,优于现有方法.
科学领域:
- 计算生物学是一种计算生物学.
- 生物医学信息学是生物医学信息学.
- 癌症研究 癌症研究
背景情况:
- 个性化瘤学依赖于多式模式的生存预测.
- 目前的方法通常只使用FFPE幻灯片和单个omics,忽视FF幻灯片和多omics数据.
- 由于临床限制,缺少数据限制了传统的融合模型.
研究的目的:
- 开发一个框架,M3Surv,用于将多病理幻灯片 (FF和FFPE) 与多病理数据集成.
- 为了应对生存预测模型中缺失的模式的挑战.
- 提高癌症存活率预测的准确性和适用性.
主要方法:
- 采用了分裂与征服的超图学习来实现多幻灯片的融合,捕捉幻灯片内和幻灯片间的关系.
- 使用交互式交叉注意力的病理特征集成的多omics数据 (蛋白质组学,转录组学).
- 引入了基于原型的内存库,用于在推理过程中归因缺失的模式.
主要成果:
- 在5个TCGA癌症数据集和一个内部数据集中,M3Surv在C指数中平均提高了2.2%.
- 与最先进的生存预测方法相比,表现优越.
- 在缺少数据模式的场景中表现出强大的稳定性和稳定性.
结论:
- M3Surv有效地整合了各种数据类型,用于多式联络生存预测.
- 该框架处理缺失数据的能力提高了其临床适用性.
- M3Surv为数据不完整的癌症生存预测提供了一个有希望的方法.
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