用工具包验证大模型在皮肤癌分级中的应用效果
Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools
- 开发轻量工具包，统一管理病理大模型的推理与分析
- 在440例皮肤鳞癌切片上验证模型，证明小模型可借大模型特征提升性能
- 适合临床研究者快速测试大模型在病理诊断中的可行性
尽管计算病理学基础模型前景广阔，但将其适配到具体临床任务仍面临全切片图像处理复杂、学习特征不透明及适配策略多样等挑战。为此，我们提出PathFMTools，一个轻量级、可扩展的Python工具包，支持病理基础模型的高效执行、分析与可视化。利用该工具，我们对接并评估了两种先进视觉-语言基础模型（CONCH和MUSK）在皮肤鳞状细胞癌（cSCC）组织学分级任务中的表现。基于440例cSCC H&E全切片图像，我们对比多种适配策略，揭示了不同预测方法间的权衡，并验证了使用基础模型嵌入训练小型专用模型的可行性。结果表明，病理基础模型在真实临床场景中具备应用潜力，而PathFMTools则有效支持了分析与验证流程。
原文摘要 · Abstract (English)
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.
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