Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features
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TL;DR - READII-2-ROQC uses volume-preserving negative controls to test whether radiomics and imaging foundation-model features capture meaningful spatial signals rather than tumour volume or acquisition artifacts. Across three cancer imaging cohorts, it exposed confounding in models that remained predictive after image structure was destroyed.
- Generates voxel-perturbed controls for tumour, background, and whole-image regions while preserving volume.
- Evaluated PyRadiomics and foundation-model features across 3,552 tumour volumes and nine matched controls.
- Reproduced survival and HPV-status signatures, finding both confounded and genuinely perturbation-sensitive models.
- Provides an open-source quality-control framework for more interpretable and reproducible imaging biomarkers.
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Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features
TL;DR - READII-2-ROQC uses volume-preserving negative controls to test whether radiomics and imaging foundation-model features capture meaningful spatial signals rather than tumour volume or acquisition artifacts. Across three cancer imaging cohorts, it exposed confounding in models that remained predictive after image structure was destroyed.
- Generates voxel-perturbed controls for tumour, background, and whole-image regions while preserving volume.
- Evaluated PyRadiomics and foundation-model features across 3,552 tumour volumes and nine matched controls.
- Reproduced survival and HPV-status signatures, finding both confounded and genuinely perturbation-sensitive models.
- Provides an open-source quality-control framework for more interpretable and reproducible imaging biomarkers.