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

## TL;DR

This study examines how variations in tissue staining and scanners affect AI models in diagnosing soft tissue tumors, finding that some models are robust and adaptable.

## Contribution

The study introduces a systematic evaluation of AI model robustness to staining and scanner variation in rare, morphologically diverse tumors.

## Key findings

- Foundation models like UNI-v2, Virchow, and TITAN showed robustness to staining and scanner variation.
- Including a small number of stain-varied slides improved model adaptability and data efficiency.
- Soft tissue tumors served as a challenging test case for evaluating model generalizability.

## 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 models, particularly UNI‐v2, Virchow and TITAN, demonstrated encouraging robustness to staining and scanning variation, particularly when a small number of stain‐varied slides were included in the training loop, highlighting their potential as adaptable and data‐efficient tools for real‐world digital pathology workflows.