面向全切片图像的结合对数逻辑生存建模的空间感知专家混合模型
Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images
AI总结：
针对病理全切片图像生存预测的难点，本文提出融合四项创新模块的计算病理学框架，在TCGA多癌种队列上取得最优生存预测性能，兼具更好的校准度与可解释性。
摘要：
由于病理全切片图像（WSIs）具有千兆像素级分辨率、强空间异质性以及复杂的生存分布特征，基于该类图像实现精准生存预测仍存在挑战。本文提出一套综合计算病理学框架，通过四项互补创新解决上述局限：一是分位数门控块选择方法，可动态识别与预后相关的区域；二是图引导聚类方法，依据空间与形态相似性对图像块分组；三是层级上下文注意力机制，可同时建模局部组织交互与全局切片级上下文；四是专家驱动的对数逻辑混合模块，可灵活建模复杂生存分布。在大规模TCGA队列上，该方法取得了当前最优性能，在LUAD、KIRC、BRCA数据集上的时间依赖一致性指数分别达0.644、0.751、0.752，性能一致优于仅组织学基线与多模态基线。该框架还提升了校准度与可解释性，推动了WSIs在个性化癌症预后中的应用。
英文摘要：
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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