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Spatial mechanistic modeling for prediction of the growth of asymptomatic meningioma

hal.structure.identifierInstitut Polytechnique de Bordeaux [Bordeaux INP]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorCOLLIN, Annabelle
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCOPOL, Cédrick
hal.structure.identifierSOPHiA GENETICS [Pessac]
dc.contributor.authorPIANET, Vivien
hal.structure.identifierSOPHiA GENETICS [Pessac]
dc.contributor.authorCOLIN, Thierry
hal.structure.identifierGroupe hospitalier Pellegrin
dc.contributor.authorENGELHARDT, Julien
hal.structure.identifierInstitut Bergonié [Bordeaux]
dc.contributor.authorKANTOR, Guy
hal.structure.identifierGroupe hospitalier Pellegrin
hal.structure.identifierImagerie moléculaire et thérapies innovantes en oncologie [IMOTION]
dc.contributor.authorLOISEAU, Hugues
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorSAUT, Olivier
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierGroupe hospitalier Pellegrin
dc.contributor.authorTATON, Benjamin
dc.date2020
dc.date.accessioned2024-04-04T02:48:16Z
dc.date.available2024-04-04T02:48:16Z
dc.date.issued2020
dc.identifier.issn0169-2607
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191723
dc.description.abstractEnMathematical modeling of tumor growth draws interest from the medical community as they have the potential to improve patients' care and the use of public health resources. The main objectives of this work are to model the growth of meningiomas-slow-growing benign tumors requiring extended imaging follow-up-and to predict tumor volume and shape at a later desired time using only two time examinations. We propose two variants of a 3D partial differential system of equations (PDE) which yield after a spatial integration systems of ordinary differential equations (ODE) that relate tumor volume with time. Estimation of models parameters is a crucial step for obtaining a personalized model for a patient that can be used for descriptive or predictive purposes. As PDE and ODE systems share the same parameters, they are both estimated by fitting the ODE systems to the tumor volumes obtained from MRI examinations acquired at different times. A population approach allows to compensate for sparse sampling times and measurement uncertainties by constraining the variability of the parameters in the population. Description capabilities of the models are investigated in 40 patients with benign asymptomatic meningiomas who had had at least 3 surveillance MRI examinations. The two models can fit to the data accurately and more realistically than a naive linear regression. Prediction performances are validated for 33 patients using a population approach. Mean relative errors in volume predictions are less than 10% with ODE systems versus 12.5% with the naive linear model using only two time examinations. Concerning the shape, the mean Sørensen-Dice coefficients are 85% with the PDE systems in a subset of 10 representative patients.
dc.language.isoen
dc.publisherElsevier
dc.subject.enInverse problem
dc.subject.enPDE Modeling
dc.subject.enMeningiomas
dc.subject.enTumor Growth
dc.title.enSpatial mechanistic modeling for prediction of the growth of asymptomatic meningioma
dc.typeArticle de revue
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halMathématiques [math]/Equations aux dérivées partielles [math.AP]
dc.subject.halInformatique [cs]/Analyse numérique [cs.NA]
dc.subject.halInformatique [cs]/Imagerie médicale
bordeaux.journalComputer Methods and Programs in Biomedicine
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-02397720
hal.version3
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02397720v3
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computer%20Methods%20and%20Programs%20in%20Biomedicine&rft.date=2020&rft.eissn=0169-2607&rft.issn=0169-2607&rft.au=COLLIN,%20Annabelle&COPOL,%20C%C3%A9drick&PIANET,%20Vivien&COLIN,%20Thierry&ENGELHARDT,%20Julien&rft.genre=article


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