Abstract
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.
Abstract (translated)
转移性进展仍然是癌症相关死亡的主要原因，但仅凭组织病理学预测原发肿瘤是否会转移以及其扩散部位仍然是一项基本挑战。尽管全玻片图像（WSIs）提供了丰富的形态信息，以往的计算病理方法通常将转移状态或位置预测视为孤立的任务，并未明确模拟临床顺序决策过程中的转移风险评估和随后的位置特异性评估流程。为解决这一研究空白，我们提出了一种决策感知、概念对齐的MIL框架HistoMet，用于从原发肿瘤WSIs中进行预后性转移结局预测。我们的框架采用了一个双模块预测管道，在此框架下首先估计原发肿瘤发生转移的可能性，然后对于高风险病例进行条件位置特异性转移预测。为了引导表示学习并提高临床可解释性，我们通过预先训练的病理学视觉语言模型将语言定义和数据自适应的转移概念整合进我们的框架中。 我们在一个包含6504名有转移随访和位置标注的多机构泛癌症队列上评估了HistoMet。在临床上相关的高敏感度筛查设置（95%的敏感性）下，HistoMet显著减少了后续工作量，同时保持了高水平的转移风险召回率。对于转移病例而言，在条件约束下，HistoMet达到了74.6的宏F1分数和标准差为1.3，以及92.1的宏一对多AUC得分。这些结果表明，明确模拟临床决策结构能够实现从原发肿瘤组织病理学直接进行稳健且可部署的预后性转移进展及部位嗜好的预测。 简言之，该研究通过提出HistoMet框架成功地解决了现有计算病理方法中的局限性，并提高了对癌症转移及其位置的精准预测能力。
URL
https://arxiv.org/abs/2602.07608
