Robustness of PET Radiomics Features: Impact of Co-Registration with MRI

The study proposes an analysis of the robustness of Positron Emission Tomography (PET) radiomics features after PET image co-registration with two different Magnetic Resonance Imaging sequences, namely T1 and FLAIR. Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman's correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman's correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction.

Automated summary

The study analyzes the robustness of PET radiomics features after co-registration with different MRI sequences, finding that shape features, first-order features, and some higher-order feature subgroups exhibit good robustness, while others show significant variability. Approximately 40% of the selected features differ between the three groups of images, underscoring the importance of considering registration constraints in radiomics studies to prevent errors in cancer diagnosis and prognosis.