Repeatability of F-FDG PET Radiomic Features in Cervical Cancer.

Knowledge of the intrinsic variability of radiomic features is essential to the proper interpretation of changes in these features over time. The primary aim of this study was to assess the test-retest repeatability of radiomic features extracted from F-FDG PET images of cervical tumors. The impact of different image preprocessing methods was also explored. Patients with cervical cancer underwent baseline and repeat F-FDG PET/CT imaging within 7 d. PET images were reconstructed using 2 methods: ordered-subset expectation maximization (PET) or ordered-subset expectation maximization with point-spread function (PET). Tumors were segmented to produce whole-tumor volumes of interest (VOI) and 40% isocontours (VOI). Voxels were either left at the default size or resampled to 3-mm isotropic voxels. SUV was discretized to a fixed number of bins (32, 64, or 128). Radiomic features were extracted from both VOIs, and repeatability was then assessed using the Lin concordance correlation coefficient (CCC). Eleven patients were enrolled and completed the test-retest PET/CT imaging protocol. Shape, neighborhood gray-level difference matrix, and gray-level cooccurrence matrix features were repeatable, with a mean CCC value of 0.81. Radiomic features extracted from PET images showed significantly better repeatability than features extracted from PET images ( < 0.001). Radiomic features extracted from VOI were more repeatable than features extracted from VOI ( < 0.001). For most features (78.4%), a change in bin number or voxel size resulted in less than a 10% change in feature value. All gray-level emphasis and gray-level run emphasis features showed poor repeatability (CCC values < 0.52) when extracted from VOI but were highly repeatable (mean CCC values > 0.96) when extracted from VOI Shape, gray-level cooccurrence matrix, and neighborhood gray-level difference matrix radiomic features were consistently repeatable, whereas gray-level run length matrix and gray-level zone length matrix features were highly variable. Radiomic features extracted from VOI were more repeatable than features extracted from VOI Changes in voxel size or SUV discretization parameters typically resulted in relatively small differences in feature value, though several features were highly sensitive to these changes.