用空间感知专家混合模型预测癌症生存期，精度超现有方法。
Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images
- 动态选关键组织区域，结合空间与形态聚类
- 在LUAD、KIRC、BRCA上时间依赖一致性达0.644~0.752
- 适合需要精准生存预测的病理分析场景
从病理全切片图像（WSIs）中准确预测生存期仍具挑战，原因在于其吉字节级分辨率、强烈的空间异质性及复杂的生存分布。本文提出一个综合计算病理框架，通过四项互补创新解决上述问题：(1) 分位数门控的块选择，动态识别预后相关区域；(2) 图引导聚类，按空间与形态相似性分组；(3) 层次化上下文注意力，建模局部组织交互与全局切片上下文；(4) 专家驱动的对数-对数逻辑分布模块，灵活拟合复杂生存分布。在大型TCGA队列中，本方法达到领先性能，时间依赖一致性指数分别为LUAD 0.644、KIRC 0.751、BRCA 0.752，持续优于仅基于组织学或多模态基线模型。该框架还提升了校准度与可解释性，推动了全切片图像在个性化癌症预后中的应用。
原文摘要 · Abstract (English)
Accurate survival prediction from histopathology whole-slide images (WSIs) remains challenging due to their gigapixel resolution, strong spatial heterogeneity, and complex survival distributions. We introduce a comprehensive computational pathology framework that addresses these limitations through four complementary innovations: (1) Quantile-Gated Patch Selection for dynamically identifying prognostically relevant regions, (2) Graph-Guided Clustering to group patches by spatial and morphological similarity, (3) Hierarchical Context Attention to model both local tissue interactions and global slide-level context, and (4) an Expert-Driven Mixture of Log-Logistics module that flexibly models complex survival distributions. Across large TCGA cohorts, our method achieves state-of-the-art performance, yielding time-dependent concordance indices of 0.644 on LUAD, 0.751 on KIRC, and 0.752 on BRCA, consistently outperforming both histology-only and multimodal baselines. The framework further provides improved calibration and interpretability, advancing the use of WSIs for personalized cancer prognosis.
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