## 1 Introduction  
Radiomics techniques, aimed at analyzing a large amount of minable features extracted from medical images, have shown great potential in different clinical areas [1]. Such a large amount of quantitative radiomic features conveys more information than the qualitative patterns observed by the radiologists' naked eye on the images. In particular, radiomic image-based signatures can be associated with clinical outcomes (i.e., biomarkers) [2; 3], thereby improving clinical decision-making tasks [4]. Therefore, the development of robust biomarkers is an essential requirement which could accelerate their usefulness and, consequently, their incorporation into clinical practice [5], providing reliable diagnostic and prognostic biomarkers for precision medicine [6]. It is well known that radiomic features might be affected by several mathematical definitions, and the proliferation of toolboxes does not help this aspect. Thus, standardization initiatives have been carried out by the scientific community in order to deal with the lack of reproducibility and validation of radiomics studies. In particular, the Image Biomarker Standardization Initiative (IBSI) [7] attempts to provide guidelines about the biomarkers' definition. Unfortunately, it is a very complicated pipeline, where every single step (e.g., image acquisition, reconstruction, segmentation, features extraction) must be tackled with caution to ensure reproducibility. Nevertheless, there are still no comprehensive and clear guidelines to obtain radiomic features that are not only reproducible but also robust. For that reason, an accurate and careful analysis of the robustness of the radiomic features is mandatory to define robust and clinically relevant biomarkers. Due to the wider diffusion and use of radiomics, in recent years, the study of methodologies aimed at improving the reproducibility and robustness of these tools has become a research topic faced, by the scientific community, from different points of view and in several clinical application domains. There are many _'sources of variability'_ that should be considered: many literature works have analyzed and assessed the impact on the robustness of radiomic features related to _intrinsic factors_, such as (_i_) imaging protocol [8; 9] and (_ii_) magnetic field strength [10], or due to _extrinsic factors_, such as (_iii_) pre-processing and enhancement techniques [11; 12], (_iv_) perturbation on the region of interest (ROI) due to the inter-operator dependence [13; 14], and segmentation methods [15; 16], but also to (_v_) post-processing techniques, which are certainly modified in original data (e.g., harmonization procedures) [17]. The main goal of this work is to analyze the dependence of robustness on the quantization level used during feature extraction and the reliability of the segmentation of breast masses on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by relying upon two segmentation methods (both automated and manual) obtained by two raters. The proposed analysis exploits hand-crafted features and traditional machine learning approaches, which are still popular in radiomics studies today, aimed at establishing predictive models that are reliable for clinical applications. The main contributions of this study are:  
* an evaluation of the robustness of radiomic features, in terms of ICC, as a function of quantization level in PyRadiomics;
* an assessment of segmentation by comparing three different raters: a automated approach and the manual segmentation of two human operators, to evaluate the features reproducibility;
* a definition of a robustness scale for individual features as a function of ICC and mean standard deviation of the feature differences;
* a definition and quantification of an overall robustness metric for each radiomic category (i.e., GLCM, GLRLM, GLSZM, GLDM, and NGTDM). The remainder of this work is organized as follows. Section 2 describes the conducted study aimed at dealing with the robustness evaluation.