RT Journal Article
T1 Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
A1 Stefano, Alessandro
A1 Leal, Antonio
A1 Richiusa, Selene
A1 Trang, Phan
A1 Comelli, Albert
A1 Benfante, Viviana
A1 Cosentino, Sebastiano
A1 Sabini, Maria G.
A1 Tuttolomondo, Antonino
A1 Altieri, Roberto
A1 Certo, Francesco
A1 Vincenzo Barbagallo, Giuseppe Maria
A1 Ippolito, Massimo
A1 Russo, Giorgio
K1 Radiomics feature robustness
K1 Imaging quantification
K1 [11C]-methionine positron emission tomography
K1 PET/MRI co-registration
K1 Tomografía de emisión de positrones
K1 Magnetic resonance imaging
K1 Imagen por resonancia magnética
K1 Neoplasias encefálicas
AB 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.
PB MDPI
YR 2021
FD 2021-10-30
LK http://hdl.handle.net/10668/3909
UL http://hdl.handle.net/10668/3909
LA en
NO Stefano A, Leal A, Richiusa S, Trang P, Comelli A, Benfante V, et al. Robustness of PET Radiomics Features: Impact of Co-Registration with MRI.  Applied Sciences. 2021; 11(21):10170
DS RISalud
RD May 13, 2025