Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools
This work addresses the problem of making pathology foundation models practical for clinical use, specifically for grading cutaneous squamous cell carcinoma to inform patient management, but it is incremental as it builds on existing models with new tools and benchmarks.
The researchers tackled the challenge of adapting computational pathology foundation models to clinical tasks by introducing PathFMTools, a Python package that facilitates efficient execution and analysis, and applied it to histological grading in cutaneous squamous cell carcinoma using 440 whole-slide images, demonstrating trade-offs in adaptation strategies and validating the use of foundation model embeddings for training specialist models.
Despite the promise of computational pathology foundation models, adapting them to specific clinical tasks remains challenging due to the complexity of whole-slide image (WSI) processing, the opacity of learned features, and the wide range of potential adaptation strategies. To address these challenges, we introduce PathFMTools, a lightweight, extensible Python package that enables efficient execution, analysis, and visualization of pathology foundation models. We use this tool to interface with and evaluate two state-of-the-art vision-language foundation models, CONCH and MUSK, on the task of histological grading in cutaneous squamous cell carcinoma (cSCC), a critical criterion that informs cSCC staging and patient management. Using a cohort of 440 cSCC H&E WSIs, we benchmark multiple adaptation strategies, demonstrating trade-offs across prediction approaches and validating the potential of using foundation model embeddings to train small specialist models. These findings underscore the promise of pathology foundation models for real-world clinical applications, with PathFMTools enabling efficient analysis and validation.