AI 中文总结
该研究提出Conserved Immune Topology（CIT），一种轻量空间表示，无需标注或目标域数据，可提升病理基础模型跨癌MSI-H预测的泛化能力，在零样本跨癌迁移中使TransMIL的AUC提升0.0534
AI 中文摘要
结合多实例学习的病理基础模型在单癌队列中可达到有竞争力的准确率，但由于器官特异性的组织学和结构差异，跨癌泛化问题仍未解决。本文提出Conserved Immune Topology（CIT，一种用于跨癌MSI-H预测的轻量空间表示），其通过具有生物学动机的免疫描述符增强基础模型嵌入。CIT利用无监督聚类识别免疫相关的图像块，随后从冻结的基础模型嵌入和图像块坐标中编码三级淋巴结构、肿瘤周围免疫反应、多尺度肿瘤浸润淋巴细胞密度以及免疫-肿瘤混合情况，无需标注或目标域数据。该方法在跨站点和跨癌设置下使用CPTAC-COAD和TCGA-STAD队列进行评估，这些队列包含扫描仪变异、分布偏移和器官特异性结构差异。零样本跨癌迁移中，使用CIT将TransMIL的AUC从0.6627提升至0.7161，绝对提升值为0.0534（p=0.003），且在所有三种多实例学习聚合器上均表现出一致提升。这些结果表明，空间免疫拓扑可为MSI-H预测提供潜在的器官不变表示，支持病理基础模型的跨癌泛化。
英文摘要
Pathology foundation models integrated with multiple instance learning achieve competitive accuracy within single-cancer cohorts, yet cross-cancer generalization remains unresolved due to organ-specific histological and architectural differences. In this paper, we propose Conserved Immune Topology (CIT), a lightweight spatial representation for cross-cancer MSI-H prediction that augments foundation-model embeddings with biologically motivated immune descriptors. CIT uses unsupervised clustering to identify immune-associated tiles, then encodes tertiary lymphoid structures, peritumoral immune reactions, multi-scale tumor-infiltrating lymphocyte density, and immune-tumor mixing from frozen foundation-model embeddings and tile coordinates without requiring annotations or target-domain data. The proposed method was evaluated under cross-site and cross-cancer settings using CPTAC-COAD and TCGA-STAD cohorts, which introduce scanner variability, distribution shifts, and organ-specific architectural variations. Zero-shot cross-cancer transfer with CIT increased TransMIL AUC from 0.6627 to 0.7161, an absolute gain of 0.0534 (p=0.003), with consistent improvements across all three MIL aggregators. These results suggest that spatial immune topology provides potentially an organ-invariant representation for MSI-H prediction, supporting cross-cancer generalization of pathology foundation models.
CommentsAccepted at the ECCV 2026 Workshop on Medical Foundation Models and Benchmarks (MedFM-Bench). 15 pages, 2 figures
