Sensitivity of radiomics features to region volume: A CT phantom study

The extraction of quantitative parameters (features) from medical images to build prediction models for computer-assisted clinical decision making (radiomics) has attracted a lot of research attention in recent years. Radiomics features may be influenced by the size of the region of interest (ROI) and it is recommended to employ volume-independent features to reduce the risk of biased prediction models. Despite being a crucial factor, the influence of ROI volume on radiomics features has been largely overlooked in the literature, with only few studies addressing this aspect. In this work we investigated the sensitivity to ROI volume of 103 conventional radiomics features from seven classes under absolute and relative signal resampling. The analysis was carried out on a set of 90 lesion-agnostic ROIs from a CT phantom. We identified a total of 45 and 54 features significantly correlated with volume under absolute and relative signal resampling, respectively. We also determined that signal resampling had an influence on the direction of the relationship, with relative resampling leading to a significant rise in the number of features negatively correlated with volume.