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This paper introduces ConRad, an efficient conformal prediction framework designed for scalar radiomic targets that leverages covariates from predicted masks and input images to construct adaptive prediction intervals. By addressing the inefficiencies of traditional black-box conformal prediction methods, ConRad achieves improved feature-level efficiency while ensuring near-nominal empirical coverage across five 2D medical imaging datasets. The findings highlight that incorporating segmentation boundary uncertainty significantly enhances the efficiency of the prediction intervals, making them more reliable for clinical decision-making.
ConRad boosts the efficiency of radiomic feature predictions by integrating segmentation boundary uncertainty, offering a game-changing approach to clinical imaging reliability.
Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models can be overconfident or poorly calibrated, making derived measurements appear more reliable than they are. Conformal prediction (CP) provides distribution-free prediction intervals with finite-sample marginal coverage guarantees, but black-box intervals for segmentation-derived radiomics can be inefficient because they ignore test-time information about image appearance, mask geometry, and segmentation uncertainty. We propose ConRad, a conformal framework for scalar radiomic targets that uses covariates derived from the predicted mask, input image, predicted radiomics, and boundary uncertainty to construct adaptive intervals while maintaining coverage. Across five 2D medical imaging datasets and 171 retained radiomic targets, we show that ConRad improves feature-level efficiency compared to baselines while maintaining near-nominal empirical coverage. Ablation results further indicate that segmentation boundary uncertainty features are the largest contributors to interval efficiency.
