Foundation Models for Hepatocellular Carcinoma: Challenges in Generalization under Data Scarcity

Hepatocellular carcinoma (HCC) is a complex and heterogeneous disease that poses significant challenges for diagnosis and treatment. Recent advancements in medical imaging and foundation models have provided new opportunities to enhance our understanding of HCC. However, these foundation models appear to lack the generalization capabilities when applied to diverse HCC cohorts and prediction tasks. This paper provides a critical review of the current progress and limitations in the generalization of foundation models in HCC pathology. It leverages numerous foundation models and multiple instance learning methods on publicly available TCGA data and a private cohort to evaluate tumor grade and overall survival estimation. To simulate real-world clinical scenarios, we assess the adaptation of foundation models under a data scarcity regime, analyzing their robustness to domain shifts. Results indicate that while foundation models exhibit stable but suboptimal performance across both tasks, their representation power for less common applications such as HCC remains limited, likely due to under-representation in the pretraining datasets. Furthermore, a substantial performance instability in zero-shot analysis highlights the necessity of fine-tuning on the target domain for reliable deployment in clinical settings. These findings underscore the challenges of applying foundation models in specialized medical domains and emphasize the need for improved adaptation strategies for histopathology applications.