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Variations in patient positioning, image acquisition, and segmentation affect each feature to varying degrees. If radiomic models use features that are not robust against such influences, they will perform poorly when applied to new data. Assessing feature robustness is thus recommended to improve the generalizability of radiomic models and feature analysis.
