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Kidney tumors can be highly heterogeneous from the microscopic to the macroscopic scale. To address this, we propose a sparsity-informed probabilistic integration of radiomics and pathomics for kidney cancer analysis. We construct radiology and pathology graphs to model spatial correlations, then use a probabilistic method and graph neural networks to identify biomarkers and aggregate spatial features. Our validation shows that this integrated approach significantly outperforms traditional methods in kidney survival analysis, achieving a notable improvement of 0.084 in the concordance index, enabling better prognostic assessments for kidney cancer patients. The source code has been released by https://github.com/shangqigao/RadioPath .

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Probabilistic Integration of Renal Cancer Radiology and Pathology Using Graph Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Shangqi Gao,
  • Shangde Gao,
  • Ines Machado,
  • Mireia Crispin-Ortuzar

摘要

Kidney tumors can be highly heterogeneous from the microscopic to the macroscopic scale. To address this, we propose a sparsity-informed probabilistic integration of radiomics and pathomics for kidney cancer analysis. We construct radiology and pathology graphs to model spatial correlations, then use a probabilistic method and graph neural networks to identify biomarkers and aggregate spatial features. Our validation shows that this integrated approach significantly outperforms traditional methods in kidney survival analysis, achieving a notable improvement of 0.084 in the concordance index, enabling better prognostic assessments for kidney cancer patients. The source code has been released by https://github.com/shangqigao/RadioPath .