Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features
This study addresses the susceptibility of radiomic and foundation model–derived features to confounding factors such as tumor volume or acquisition artifacts, which often obscure true biological structure. To mitigate this, the authors propose the READII-2-ROQC framework, which introduces—for the first time—a volume-preserving negative control mechanism via controlled voxel perturbations that disrupt spatial organization without altering tumor volume, thereby enabling systematic assessment of feature dependence on genuine image patterns. Integrating PyRadiomics, foundation models, and multi-region perturbation strategies, the method was validated across three public cancer cohorts comprising 3,552 tumors. Results revealed that multiple published models exhibited no significant performance drop after structural disruption, indicating their reliance on volume or contextual confounders rather than biologically meaningful signals. This work substantially enhances the interpretability and reproducibility of imaging biomarkers.