用病理概念评估大模型在肾癌中的临床转化能力
Evaluating Foundation Models with Pathological Concept Learning for Kidney Cancer
- 基于TNM分期和病历构建肾癌病理概念体系
- 通过滑动窗特征+图神经网络识别病理概念，准确区分高低风险患者
- 方法兼具可解释性与公平性，适合医学AI研究者使用
为评估大模型的临床转化能力，本文提出一种聚焦肾癌的病理概念学习方法。结合TNM分期标准与病理报告，构建了全面的肾癌病理概念体系。利用大模型从全切片图像中提取深层特征，构建病理图以捕捉空间关联，并训练图神经网络识别这些概念。最终在肾癌生存分析中验证了该方法的有效性，凸显其在识别低风险与高风险患者时的可解释性与公平性。代码已开源：https://github.com/shangqigao/RadioPath。
原文摘要 · Abstract (English)
To evaluate the translational capabilities of foundation models, we develop a pathological concept learning approach focused on kidney cancer. By leveraging TNM staging guidelines and pathology reports, we build comprehensive pathological concepts for kidney cancer. Then, we extract deep features from whole slide images using foundation models, construct pathological graphs to capture spatial correlations, and trained graph neural networks to identify these concepts. Finally, we demonstrate the effectiveness of this approach in kidney cancer survival analysis, highlighting its explainability and fairness in identifying low- and high-risk patients. The source code has been released by https://github.com/shangqigao/RadioPath.
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