Interpretation of radiomics features–A pictorial review

•Commonly used radiomics features with practical examples are illustrated.•First-order features evaluate the distribution of individual pixel/voxel in images.•Second- and higher-order features consider relationships between pixels/voxels, respectively.•Transform-based features describe textures in a different space after image transformation or image filtering.•Physicians should get involved in all phases to build a practical model. Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a “digital biopsy”. Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.