## 1 Introduction  
Cancer is one of the leading causes of mortality, and anatomical and functional imaging is of vital importance for diagnosis, treatment planning, and treatment response, which has become standard in clinical protocols for many different oncological disease types. However, qualitative analyses are not always sufficient to reveal disease characteristics and to make a treatment decision or final diagnosis with the utmost confidence. To date, interest has emerged in characterizing tumor heterogeneity and phenotypes based on innovative image-based biomarkers related to the pathological, genomic, proteomic, and clinical data. Recent advances in computational power and the use of automated algorithms have generated a new area of research termed radiomics [1; 2] that can be applied on imaging data sets such as computed tomography (CT) [3; 4], positron emission tomography (PET) [5; 6], and magnetic resonance imaging (MRI) [7; 8]. It is based on the extraction of a large variety of biomarkers from images in order to improve diagnosis and treatment response prediction, and thus potentially allow for the personalization of cancer treatments. The fundamental hypothesis of radiomics is that much more information is presented in medical images than what visual assessment can understand, and therefore, the pathophysiological information of tumors can be captured using image biomarkers. In computer vision and image processing, a biomarker is an information about the content of an image and can be renamed as feature. Specifically, these features express properties regarding the shape, histogram, and texture of the images. Shape features are based on the surface reconstruction whereas first-order metrics are obtained from the histogram that describes the distribution of voxel intensities in the image. Information about inter-voxel relationships within the image can be interpreted using higher-order statistics based on texture analysis. As a result, quantitative analysis based on these features is considered one of the key findings in clinical studies for cancer detection, diagnosis and therapy assessment, resulting in improved decision support systems. Nevertheless, their clinical application may be challenging. A major obstacle is that the \"robustness\" of the extracted radiomics features is unclear. Robustness is understood as the level of variability of features as a result of perturbations, such as image co-registration. In other words, an essential ingredient to establish novel quantitative imaging biomarkers in clinical practice is to quantify and ascertain the consistency of radiomics features. Recently, many researchers have focused on gaining a deeper understanding of feature robustness. However, most studies used phantoms, and consequently, it is difficult to ensure that their results could be applied to imaging studies of real patients [9]. Furthermore, standardizing the parameters during image acquisition in clinical settings is a challenge. To date, there are various studies on the robustness of radiomics features due to various factors, such as the impact of voxel size resampling and interpolation, image perturbation, different slice thickness, etc. [10; 11; 12], and many works have discussed the potential uncertainty of feature extraction, i.e., [13]. In PET imaging, some standardized semi-quantitative measurements are usually extracted and used in clinical practice, such as the standardized uptake value (SUV), and the metabolically active tumor volume (MTV) [14]. [PERSON] et al. [15] investigated the reproducibility of SUV, intensity histogram features, intensity-size zone features, and co-occurrence matrices features. The results showed that these features were insensitive to the discretization range. [PERSON] et al. [16] investigated the robustness of PET based heterogeneity textural features with respect to the delineation of volumes and partial volume effects correction. These features were significantly affected by the differences in the volume delineation.