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hal.structure.identifierInstitut Bergonié [Bordeaux]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierUniversité de Bordeaux [UB]
dc.contributor.authorCROMBÉ, Amandine
hal.structure.identifierInstitut Bergonié [Bordeaux]
dc.contributor.authorKIND, Michèle
hal.structure.identifierInstitut Bergonié [Bordeaux]
dc.contributor.authorFADLI, David
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierInstitut Bergonié [Bordeaux]
dc.contributor.authorLE LOARER, François
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierInstitut Bergonié [Bordeaux]
dc.contributor.authorITALIANO, Antoine
hal.structure.identifierInstitut Bergonié [Bordeaux]
dc.contributor.authorBUY, Xavier
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierCentre National de la Recherche Scientifique [CNRS]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorSAUT, Olivier
dc.date.accessioned2024-04-04T02:48:34Z
dc.date.available2024-04-04T02:48:34Z
dc.date.issued2020-12
dc.identifier.issn2045-2322
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191751
dc.description.abstractEnIntensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors' radiological phenotype with machine-learning to improve predictive models, such as metastasticrelapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2weighted-imaging of 70 sarcoma patients by using 5 IHTs and extracting 45 radiomics features (RFs), namely: classical standardization (IHTstd), standardization per adipose tissue SIs (IHTfat), histogram-matching with a patient histogram
dc.language.isoen
dc.publisherNature Publishing Group
dc.title.enIntensity Harmonization Techniques Influence Radiomics Features and Radiomics-based Predictions in Sarcoma Patients
dc.typeArticle de revue
dc.identifier.doi10.1038/s41598-020-72535-0
dc.subject.halMathématiques [math]/Equations aux dérivées partielles [math.AP]
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halInformatique [cs]/Modélisation et simulation
bordeaux.journalScientific Reports
bordeaux.volume10
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue1
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03050686
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03050686v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Scientific%20Reports&rft.date=2020-12&rft.volume=10&rft.issue=1&rft.eissn=2045-2322&rft.issn=2045-2322&rft.au=CROMB%C3%89,%20Amandine&KIND,%20Mich%C3%A8le&FADLI,%20David&LE%20LOARER,%20Fran%C3%A7ois&ITALIANO,%20Antoine&rft.genre=article


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