Abstract: Primary liver cancer is a highly prevalent digestive system malignancy worldwide, predominantly comprising intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC). Clinical practice demonstrates that precise histological subtyping and clinical staging of these subtypes are critical for guiding personalized treatment strategies and prognosis evaluation. However, effectively exploiting cross-scale features for multi-task pathological analysis remains challenging due to the high heterogeneity of liver cancer and the complex coexistence of macroscopic tissue structures and microscopic nuclei in whole slide images (WSIs). To address this problem, this study proposes a weakly supervised Dual-Branch Multi-Source Feature Fusion (DBMSF) model. This model integrates multi-scale deep features extracted by the CHIEF foundation model and handcrafted features derived from HoVer-NeXt nuclei segmentation. Specifically, the deep branch employs a multi-scale alignment module for feature interaction, while the handcrafted branch utilizes a graph convolutional network (GCN) to dynamically aggregate nuclei information, capturing a comprehensive representation of the tumor microenvironment. Finally, a multi-source fusion module dynamically integrates these features. Multi-task evaluations on a private ICC cohort and the public TCGA-LIHC cohort demonstrated that DBMSF achieved an AUC of 88.5% and accuracy of 75.6% for ICC subtyping, and an AUC of 82.4% and accuracy of 71.5% for HCC T-stage prediction. These experimental results indicate that DBMSF significantly outperforms state-of-the-art methods, demonstrating robust effectiveness and promising clinical application potential for multi-task pathology analysis.

摘要： 原发性肝癌是全球范围内高发的消化系统恶性肿瘤，主要包括肝内胆管癌（ICC）与肝细胞癌（HCC）两种亚型。临床实践表明，针对上述亚型进行精准的组织学分型与临床分期，对于个体化治疗与预后评估至关重要。然而，由于肝癌组织结构的高度异质性，且全景切片图像（WSIs）中同时蕴含宏观组织结构与微观多源细胞核的互补信息，如何充分利用这些跨尺度特征实现病理图像多任务分析仍是一个重大挑战。为解决这一问题，该工作提出了一种基于弱监督的双分支多源特征融合（DBMSF）模型。模型整合了由CHIEF病理基础模型提取的多尺度深度特征，以及由HoVer-NeXt分割得到的细胞核构建的手工特征。前者通过多尺度特征对齐模块实现跨尺度特征交互与对齐，后者通过图卷积网络（GCN）对不同类型细胞核特征进行动态聚合，从而捕获肿瘤微环境的全面表征。最终，通过多源特征融合模块实现深度与手工特征的动态融合。在南京鼓楼医院ICC私有队列与TCGA-LIHC公开队列上的多任务评估结果显示，模型在ICC分型任务中AUC与ACC分别达到88.5%与75.6%，在HCC T分期任务中分别达到82.4%与71.5%。实验结果表明，DBMSF模型性能显著优于现有先进方法，在肝癌病理图像多任务分析中展现出良好的有效性与临床应用潜力。