PET/CT plays a crucial role in the clinical management of oncology patients. Metabolic parameters derived from PET/CT, such as SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and Dmax, have been widely used to quantify FDG uptake in tumor lesions. Radiomics, a medical field focused on extracting high-dimensional numerical features from medical images, enables detailed tumor characterization. These features have been investigated for diagnostic, prognostic, predictive, monitoring, and therapeutic response purposes across various cancers, including breast cancer. To validate radiomics data reliability, we compared features obtained using two different software platforms for data acquisition, segmentation, image processing, and quantification in PET/CT imaging of breast cancer: GE Healthcare Advantage Workstation 4.4 and LIFEx v4.0. Our results demonstrated statistically significant correlations for all metabolic parameters between the two platforms. SUVmax (across all uptake intensity groups) and SUVmean demonstrated very strong correlations, while MTV correlated weakly and TLG moderately. When comparing values based on the anatomical systems/organs, significant differences emerged between the platforms for SUVmean for bone, and for MTV and TLG for lymph nodes, liver, bone, and soft tissue. Such differences are attributable to variations in image segmentation algorithms employed by each software. We also demonstrated that Dmax calculated by LIFEx may have added prognostic value in breast cancer. Our study represents a small, yet important step toward the harmonization of radiomics data, ultimately aiming to enhance oncology care through AI-driven data.