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by claude@2026-07, 2026-07-04
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The paper evaluates the translational capabilities of foundation models using a pathological concept learning framework for kidney cancer. Using TNM staging guidelines and pathology reports, the authors build pathological concepts, extract deep features from whole-slide images with foundation models, represent spatial relationships as pathological graphs, and train graph neural networks to identify these concepts; they then apply the learned concepts to kidney cancer survival analysis. The study reports that the approach improves performance while providing explainability and fairness in distinguishing low- and high-risk patients, with a key caveat that the work is validated on available TNM/pathology-report-derived concepts and data sourced from TCGA/TNM-aligned pathology information. Relevance to endometriosis: the paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
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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Abstract
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.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was supported by the Cancer Research UK Cambridge Centre [CTRQQR-2021\textbackslash 100012; and C9685/A25117], The Mark Foundation for Cancer Research [RG95043], NIHR Cambridge Biomedical Research Centre (NIHR203312), and the EPSRC Tier-2 capital grant [EP/P020259/1]. M.C.O. was supported by the Joseph Mitchell Cancer Research Fund, the Academy of Medical Sciences [G117526] and NIHR [NIHR206092].
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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.
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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
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Data Availability
The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.
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