Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19-0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability. Radiomics is a technique that is used to extract large amounts of quantitative information from routine medical images that decode information about a region of interest (ROI). The majority of radiomics articles published concerns its application in the oncological field 1-4. Here, radiomics bears the advantage of non-invasively quantifying the underlying phenotype of the entire tumor for multiple lesions simultaneously, in contrast to tissue biopsy, which samples only a small part of a single (often heterogeneous) tumor 2,5. The ability to characterize the tumor and to establish links to the underlying biology 6 and ultimately clinical outcomes, allows a more patienttailored treatment 7 , enabling 'precision medicine' 8,9. Recently, several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. diagnosis 10,11 , tumor response prediction 12-14 , prediction of molecular tumor subtype 15,16 , and prediction of axillary lymph node metastases 17,18. Although these results are promising, issues regarding features robustness as well as the comparability of results, including inter-observer segmentation variability, need to be addressed 19-24. In order to extract clinically useful information from medical images and to use features as clinical biomarkers, it is important that extracted