Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it will disseminate directly from histopathology remains a fundamental challenge. Although whole-slide images (WSIs) provide rich morphological information, prior computational pathology approaches typically address metastatic status or site prediction as isolated tasks, and do not explicitly model the clinically sequential decision process of metastatic risk assessment followed by downstream site-specific evaluation. To address this research gap, we present a decision-aware, concept-aligned MIL framework, HistoMet, for prognostic metastatic outcome prediction from primary tumor WSIs. Our proposed framework adopts a two-module prediction pipeline in which the likelihood of metastatic progression from the primary tumor is first estimated, followed by conditional prediction of metastatic site for high-risk cases. To guide representation learning and improve clinical interpretability, our framework integrates linguistically defined and data-adaptive metastatic concepts through a pretrained pathology vision-language model. We evaluate HistoMet on a multi-institutional pan-cancer cohort of 6504 patients with metastasis follow-up and site annotations. Under clinically relevant high-sensitivity screening settings (95 percent sensitivity), HistoMet significantly reduces downstream workload while maintaining high metastatic risk recall. Conditional on metastatic cases, HistoMet achieves a macro F1 of 74.6 with a standard deviation of 1.3 and a macro one-vs-rest AUC of 92.1. These results demonstrate that explicitly modeling clinical decision structure enables robust and deployable prognostic prediction of metastatic progression and site tropism directly from primary tumor histopathology.

翻译：转移进展仍是癌症相关死亡的主要原因，然而直接从组织病理学预测原发肿瘤是否会发生转移及其扩散部位，仍然是一个根本性挑战。尽管全切片图像（WSIs）提供了丰富的形态学信息，但先前的计算病理学方法通常将转移状态或部位预测作为孤立任务处理，并未显式建模临床序贯决策过程——即先进行转移风险评估，再进行下游部位特异性评估。为填补这一研究空白，我们提出一种决策感知、概念对齐的多实例学习框架HistoMet，用于基于原发肿瘤WSIs的转移预后预测。该框架采用双模块预测流程：首先评估原发肿瘤发生转移进展的可能性，随后对高风险病例进行转移部位的条件预测。为引导表征学习并提升临床可解释性，本框架通过预训练的病理视觉语言模型整合了语言定义与数据自适应的转移概念。我们在包含6504例具有转移随访及部位标注患者的多机构全癌种队列中对HistoMet进行评估。在临床相关的高灵敏度筛查设置下（95%灵敏度），HistoMet在保持高转移风险召回率的同时，显著减少了下游工作量。针对转移病例的条件预测，HistoMet取得了宏观F1分数74.6（标准差1.3）和宏观一对多AUC值92.1。这些结果表明，显式建模临床决策结构能够直接从原发肿瘤组织病理学实现稳健且可部署的转移进展及部位倾向性预后预测。