Segmentation accuracy and the reliability of radiomics features

In this paper we introduce a novel computational procedure to quantitatively investigate how image segmentation affects radiomics feature computation. Specifically, this study introduces four correlation coefficients that quantitatively assess the features' reliability in terms of quality, consistency, robustness, and instability of the features themselves. We validate our analysis in the case of an MRI-based study involving meningioma patients. The proposed approach has been intrinsically conceived for automated radiomics analysis and it is of potential interest for other imaging-driven applications in oncology.