基于病理学和空间转录组的原型驱动融合用于可解释的生存预测
Prototype-driven fusion of pathology and spatial transcriptomics for interpretable survival prediction
AI总结：
PathoSpatial通过整合病理学和空间转录组数据，实现可解释的生存预测，提升多模态学习的可解释性和有效性。
摘要：
全切片图像（WSI）通过多实例学习（MIL）实现弱监督的预后建模。空间转录组学（ST）保留了原位基因表达，提供了补充形态学的时空分子背景。随着配对的WSI-ST队列扩展到人口层面，利用其互补的空间信号进行预后预测变得至关重要；然而，这种范式下的原理性跨模态融合策略仍然有限。为此，我们引入了PathoSpatial，一个可解释的端到端框架，整合共注册的WSI和ST以学习具有空间信息的预后表示。PathoSpatial在多级专家架构中使用任务引导的原型学习，自适应地协调无监督的单模态发现与监督的跨模态聚合。通过设计，PathoSpatial在保持判别能力的同时显著增强了可解释性。我们在一个三阴性乳腺癌队列中评估了PathoSpatial，该队列具有配对的ST和WSI。PathoSpatial在五个生存终点上表现出强大且一致的性能，实现了优于或与领先单模态和多模态方法相当的性能。PathoSpatial本质上能够实现事后原型解释和分子风险分解，提供定量且基于生物学的解释，突出候选预后因素。我们提出PathoSpatial作为可扩展且可解释的多模态学习的证明概念，用于空间组学-病理学融合。
英文摘要：
Whole slide images (WSIs) enable weakly supervised prognostic modeling via multiple instance learning (MIL). Spatial transcriptomics (ST) preserves in situ gene expression, providing a spatial molecular context that complements morphology. As paired WSI-ST cohorts scale to population level, leveraging their complementary spatial signals for prognosis becomes crucial; however, principled cross-modal fusion strategies remain limited for this paradigm. To this end, we introduce PathoSpatial, an interpretable end-to-end framework integrating co-registered WSIs and ST to learn spatially informed prognostic representations. PathoSpatial uses task-guided prototype learning within a multi-level experts architecture, adaptively orchestrating unsupervised within-modality discovery with supervised cross-modal aggregation. By design, PathoSpatial substantially strengthens interpretability while maintaining discriminative ability. We evaluate PathoSpatial on a triple-negative breast cancer cohort with paired ST and WSIs. PathoSpatial delivers strong and consistent performance across five survival endpoints, achieving superior or comparable performance to leading unimodal and multimodal methods. PathoSpatial inherently enables post-hoc prototype interpretation and molecular risk decomposition, providing quantitative, biologically grounded explanations, highlighting candidate prognostic factors. We present PathoSpatial as a proof-of-concept for scalable and interpretable multimodal learning for spatial omics-pathology fusion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
