摘要： Radiomics analysis of 18F-FDG-PET/CT images promises for an improved in-vivo disease characterization. To date, several studies reported significant variations in textural features due to differences in patient preparation, imaging protocols, lesion delineation and feature extraction. Our objective was to study variations of features prior to a radiomics analysis of 18F-FDG-PET data and to identify those feature extraction and imaging protocol parameters that minimize radiomic feature variations across PET imaging systems. Methods. A whole-body National Electrical Manufacturers Association image quality phantom was imaged with 13 PET/CT systems at 12 different sites following local protocols. We selected 37 radiomic features related to the four largest spheres (17-37 mm) in the phantom. Based on a combined analysis of voxel size, bin size and lesion volume changes, feature and imaging system ranks were established. A 1-way analysis of variance (ANOVA) was performed over voxel size, bin size and lesion volume subgroups to identify the dependency and the trend change of feature variations across these parameters. Results. Feature ranking revealed that the gray-level co-occurrence matrix (GLCM) and shape features are the least sensitive to PET imaging system variations. Imaging system ranking illustrated that the use of point-spread function (PSF), small voxel sizes and narrow Gaussian post-filtering helped minimize feature variations. ANOVA subgroup analysis indicated that variations of each of the 37 features and for a given voxel size and bin size parameter can be minimized. Conclusions. Our study provides guidance to selecting optimized features from 18F-FDG-PET/CT studies. We were able to demonstrate that feature variations can be minimized for selected image parameters and imaging systems. These results can help imaging specialists and feature engineers in increasing the quality of future radiomic studies involving PET/CT.