Boosting pathology foundation models via few-shot prompt-tuning for rare cancer subtyping

Rare cancers comprise 20–25% of malignancies (over 70% in pediatric oncology) but face major diagnostic challenges due to limited expert availability. While pathology vision-language models show promising zero-shot capabilities for common cancers, their performance on rare cancers remains limited. Existing multi-instance learning (MIL) methods rely solely on visual features, overlooking cross-modal knowledge and compromising interpretability critical for rare cancer diagnosis. To address this, we propose PathPT, a framework that exploits vision-language foundation models through spatially-aware visual aggregation and task-specific prompt tuning. PathPT converts WSI-level supervision into fine-grained tile-level guidance, preserving tumor localization and enabling cross-modal reasoning. Across eight rare and three common cancer datasets–spanning 56 subtypes and 3958 WSIs, PathPT consistently outperforms state-of-the-art methods under data-scarce settings. It achieves substantial gains in both subtyping accuracy and cancerous region grounding ability, providing a scalable, interpretable AI solution to improve rare cancer subtyping with limited access to specialized expertise. Rare cancers can be challenging to diagnose from imaging due to limited data and previous expert experience. Here, the authors develop the PathPT framework to improve subtyping and tumour localisation.