Impact of tissue staining and scanner variation on the performance of pathology foundation models: a study of sarcomas and their mimics.

ABSTRACT: Histopathological analysis is considered the gold standard for the diagnosis and prognostication of cancer. Recent advances in AI, driven by large-scale digitisation and pan-cancer foundation models, are opening new opportunities for clinical integration. However, it remains unclear how robust these foundation models are to real-world sources of variability, particularly in H&E staining and scanners produced by different manufacturers. In this study, we use soft tissue tumours, a rare and morphologically diverse tumour type, as a challenging test case to systematically investigate the colour-related robustness and generalisability of seven AI models. Controlled staining and scanning experiments were utilised to assess model performance across diverse real-world data sources. Foundation mod

SUBMITTER: Chai B 

PROVIDER: S-EPMC12932120 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature