Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools

TL;DR

This paper introduces PathFMTools, a Python package for analyzing pathology foundation models, and demonstrates its use in benchmarking models for histological grading of cutaneous squamous cell carcinoma using a large WSI dataset.

Contribution

The paper presents PathFMTools, a new tool for efficient analysis of pathology foundation models, and evaluates two state-of-the-art models on cSCC grading, highlighting adaptation strategies and embedding utility.

Findings

PathFMTools enables efficient model analysis and visualization.

Benchmarking shows trade-offs in adaptation strategies.

Foundation model embeddings can train effective small specialist models.

Abstract

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.