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Comparison of MRI-based response criteria and radiomics for the prediction of early response to transarterial radioembolization in patients with hepatocellular carcinoma

hal.structure.identifierCHU Bordeaux
dc.contributor.authorAUJAY, Godefroy
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorETCHEGARAY, Christèle
hal.structure.identifierCHU Bordeaux
dc.contributor.authorBLANC, Jean-Frederic
hal.structure.identifierCHU Bordeaux
dc.contributor.authorLAPUYADE, Bruno
hal.structure.identifierCHU Bordeaux
dc.contributor.authorPAPADOPOULOS, Panteleimon
hal.structure.identifierCHU Bordeaux
dc.contributor.authorPEY, Marie-Anaïg
hal.structure.identifierCHU Bordeaux
dc.contributor.authorBORDENAVE, Laurence
hal.structure.identifierCHU Bordeaux
dc.contributor.authorTRILLAUD, Hervé
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorSAUT, Olivier
hal.structure.identifierCHU Bordeaux
dc.contributor.authorPINAQUY, Jean-Baptiste
dc.date.accessioned2024-04-04T02:36:34Z
dc.date.available2024-04-04T02:36:34Z
dc.date.issued2022-07-01
dc.identifier.issn2211-5684
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190730
dc.description.abstractEnPurposeThe purpose of this study was to evaluate the capabilities of radiomics using magnetic resonance imaging (MRI) data in the assessment of treatment response to 90yttrium transarterial radioembolization (TARE) in patients with locally advanced hepatocellular carcinoma (HCC) by comparison with predictions based on European Association for the Study of the Liver (EASL) criteria.Patients and methodsTwenty-two patients with HCC (19 men, 3 women; mean age: 66.7 ± 9.8 [SD]; age range: 37–82 years) who underwent contrast-enhanced MRI 4 ± 1 weeks before and 4 ± 4 weeks after TARE, were enrolled in this retrospective study. Regions of interest were placed manually along the contours of the treated tumor on each axial slice of arterial and portal phase images using the ITK-SNAP post-processing software. For each MRI, the Pyradiomics Python package was used to extract 107 radiomics features on both arterial and portal phases, and resulting delta-features were computed. The Mann-Whitney U test with Bonferroni correction was used to select statistically different features between responders and non-responders (i.e., those with progressive or stable disease) at 6-month follow-up, according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Finally, for each selected feature, univariable logistic regression with leave-one-out cross validation procedure was used to perform receiver operating characteristic (ROC) curve analysis and compare radiomics parameters with MRI variables.ResultsAccording to mRECIST, 14 patients (14/22; 64%) were non-responders and 8 (8/22; 36%) were responders. Four radiomics parameters (long run emphasis, minor axis length, surface area, and gray level non-uniformity on arterial phase images) were the only predictors of early response. ROC curve analysis showed that long run emphasis was the best parameter for predicting early response, with 100% sensitivity (95% CI: 68–100) and 100% specificity (95% CI: 78–100). EASL morphologic criteria yielded 75% sensitivity (95% CI: 41–96%) and 93% specificity (95% CI: 69–100%).ConclusionRadiomics allows identify marked differences between responders and non-responders, and could aid in the prediction of early treatment response following TARE in patients with HCC.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enRadiomics analysis
dc.subject.enHepatocellular carcinoma
dc.subject.enSelective internal radiation therapy (SIRT)
dc.subject.enTreatment response
dc.subject.enMagnetic resonance imaging
dc.title.enComparison of MRI-based response criteria and radiomics for the prediction of early response to transarterial radioembolization in patients with hepatocellular carcinoma
dc.typeArticle de revue
dc.identifier.doi10.1016/j.diii.2022.01.009
dc.subject.halInformatique [cs]/Imagerie médicale
bordeaux.journalDiagnostic and Interventional Imaging
bordeaux.page360-366
bordeaux.volume103
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue7-8
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03930592
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03930592v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Diagnostic%20and%20Interventional%20Imaging&rft.date=2022-07-01&rft.volume=103&rft.issue=7-8&rft.spage=360-366&rft.epage=360-366&rft.eissn=2211-5684&rft.issn=2211-5684&rft.au=AUJAY,%20Godefroy&ETCHEGARAY,%20Christ%C3%A8le&BLANC,%20Jean-Frederic&LAPUYADE,%20Bruno&PAPADOPOULOS,%20Panteleimon&rft.genre=article


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