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MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

hal.structure.identifierHôpital Haut-Lévêque [CHU Bordeaux]
dc.contributor.authorGIRAUD, Nicolas
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
hal.structure.identifierHopital Saint-Louis [AP-HP] [AP-HP]
dc.contributor.authorAPARICIO, Thomas
hal.structure.identifierCentre Azuréen de Cancérologie [Mougins, France]
dc.contributor.authorRONCHIN, Philippe
hal.structure.identifierInstitut Curie [Paris]
dc.contributor.authorBAZIRE, Louis-Arnaud
hal.structure.identifierFédération Francophone de Cancérologie Digestive [FFCD]
dc.contributor.authorBARBIER, Emilie
hal.structure.identifierInstitut régional de Cancérologie de Montpellier [ICM]
dc.contributor.authorLEMANSKI, Claire
hal.structure.identifierCentre Régional de Lutte contre le Cancer Oscar Lambret [Lille] [UNICANCER/Lille]
dc.contributor.authorMIRABEL, Xavier
hal.structure.identifierCentre Armoricain de Radiothérapie, d'Imagerie médicale et d'Oncologie [Plérin, Saint-Brieuc] [CARIO]
dc.contributor.authorETIENNE, Pierre-Luc
hal.structure.identifierHôpital Sud [CHU Rennes]
dc.contributor.authorLIÈVRE, Astrid
hal.structure.identifierHôpital privé Pays de Savoie
dc.contributor.authorCACHEUX, Wulfran
hal.structure.identifierInstitut de Cancérologie de Bourgogne
dc.contributor.authorDARUT-JOUVE, Ariane
hal.structure.identifierCentre Léon Bérard [Lyon]
dc.contributor.authorDE LA FOUCHARDIÈRE, Christelle
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorHOCQUELET, Arnaud
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorTRILLAUD, Hervé
hal.structure.identifierHôpital Haut-Lévêque [CHU Bordeaux]
dc.contributor.authorCHARLEUX, Thomas
hal.structure.identifierHôpitaux Civils de Colmar
dc.contributor.authorBREYSACHER, Gilles
hal.structure.identifierGroupe Hospitalier Bretagne Sud [GHBS]
dc.contributor.authorARGO-LEIGNEL, Delphine
hal.structure.identifierCentre Hospitalier Annecy-Genevois [Saint-Julien-en-Genevois]
dc.contributor.authorTESSIER, Alexandre
hal.structure.identifierInstitut de Cancérologie de la Loire Lucien Neuwirth
dc.contributor.authorMAGNÉ, Nicolas
hal.structure.identifierInstitut de Cancérologie de Strasbourg Europe [ICANS]
dc.contributor.authorBEN ABDELGHANI, Meher
hal.structure.identifierCHU Dijon
dc.contributor.authorLEPAGE, Côme
hal.structure.identifierHôpital Haut-Lévêque [CHU Bordeaux]
dc.contributor.authorVENDRELY, Véronique
dc.date.accessioned2024-04-04T02:44:04Z
dc.date.available2024-04-04T02:44:04Z
dc.date.issued2021-01
dc.identifier.issn2072-6694
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191377
dc.description.abstractEnPurpose: Chemo-radiotherapy (CRT) is the standard treatment for non-metastatic anal squamous cell carcinomas (ASCC). Despite excellent results for T1-2 stages, relapses still occur in around 35% of locally advanced tumors. Recent strategies focus on treatment intensification, but could benefit from a better patient selection. Our goal was to assess the prognostic value of pre-therapeutic MRI radiomics on 2-year disease control (DC). Methods: We retrospectively selected patients with non-metastatic ASCC treated at the CHU Bordeaux and in the French FFCD0904 multicentric trial. Radiomic features were extracted from T2-weighted pre-therapeutic MRI delineated sequences. After random division between training and testing sets on a 2:1 ratio, univariate and multivariate analysis were performed on the training cohort to select optimal features. The correlation with 2-year DC was assessed using logistic regression models, with AUC and accuracy as performance gauges, and the prediction of disease-free survival using Cox regression and Kaplan-Meier analysis. Results: A total of 82 patients were randomized in the training (n = 54) and testing sets (n = 28). At 2 years, 24 patients (29%) presented relapse. In the training set, two clinical (tumor size and CRT length) and two radiomic features (FirstOrder_Entropy and GLCM_JointEnergy) were associated with disease control in univariate analysis and included in the model. The clinical model was outperformed by the mixed (clinical and radiomic) model in both the training (AUC 0.758 versus 0.825, accuracy of 75.9% versus 87%) and testing (AUC 0.714 versus 0.898, accuracy of 78.6% versus 85.7%) sets, which led to distinctive high and low risk of disease relapse groups (HR 8.60, p = 0.005). Conclusion: A mixed model with two clinical and two radiomic features was predictive of 2-year disease control after CRT and could contribute to identify high risk patients amenable to treatment intensification with view of personalized medicine.
dc.language.isoen
dc.publisherMDPI
dc.title.enMRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma
dc.typeArticle de revue
dc.identifier.doi10.3390/cancers13020193
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.journalCancers
bordeaux.page193
bordeaux.volume13
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03428535
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03428535v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Cancers&rft.date=2021-01&rft.volume=13&rft.issue=2&rft.spage=193&rft.epage=193&rft.eissn=2072-6694&rft.issn=2072-6694&rft.au=GIRAUD,%20Nicolas&SAUT,%20Olivier&APARICIO,%20Thomas&RONCHIN,%20Philippe&BAZIRE,%20Louis-Arnaud&rft.genre=article


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