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

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 si...

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Autores: Stefano, Alessandro, Leal Plaza, Antonio, Richiusa, Selene, Trang, Phan, Comelli, Albert, Benfante, Viviana
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/139267
Acceso en línea:https://hdl.handle.net/11441/139267
https://doi.org/10.3390/app112110170
Access Level:acceso abierto
Palabra clave:Radiomics feature robustness
Imaging quantification
[11C]-methionine positron emission tomography
PET/MRI co-registration
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spelling Robustness of PET Radiomics Features: Impact of Co-Registration with MRIStefano, AlessandroLeal Plaza, AntonioRichiusa, SeleneTrang, PhanComelli, AlbertBenfante, VivianaRadiomics feature robustnessImaging quantification[11C]-methionine positron emission tomographyPET/MRI co-registrationRadiomics 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.MDPIFisiología Médica y Biofísica2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/139267https://doi.org/10.3390/app112110170reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésApplied Sciences, 11 (21), 10170.https://www.mdpi.com/2076-3417/11/21/10170/htminfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1392672026-06-17T12:51:07Z
dc.title.none.fl_str_mv Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
title Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
spellingShingle Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
Stefano, Alessandro
Radiomics feature robustness
Imaging quantification
[11C]-methionine positron emission tomography
PET/MRI co-registration
title_short Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
title_full Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
title_fullStr Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
title_full_unstemmed Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
title_sort Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
dc.creator.none.fl_str_mv Stefano, Alessandro
Leal Plaza, Antonio
Richiusa, Selene
Trang, Phan
Comelli, Albert
Benfante, Viviana
author Stefano, Alessandro
author_facet Stefano, Alessandro
Leal Plaza, Antonio
Richiusa, Selene
Trang, Phan
Comelli, Albert
Benfante, Viviana
author_role author
author2 Leal Plaza, Antonio
Richiusa, Selene
Trang, Phan
Comelli, Albert
Benfante, Viviana
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Fisiología Médica y Biofísica
dc.subject.none.fl_str_mv Radiomics feature robustness
Imaging quantification
[11C]-methionine positron emission tomography
PET/MRI co-registration
topic Radiomics feature robustness
Imaging quantification
[11C]-methionine positron emission tomography
PET/MRI co-registration
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/139267
https://doi.org/10.3390/app112110170
url https://hdl.handle.net/11441/139267
https://doi.org/10.3390/app112110170
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Applied Sciences, 11 (21), 10170.
https://www.mdpi.com/2076-3417/11/21/10170/htm
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
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