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Population Modeling of Tumor Growth Curves, the Reduced Gompertz Model and Prediction of the Age of a Tumor

hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorVAGHI, Cristina
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorRODALLEC, Anne
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorFANCIULLINO, Raphaelle
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorCICCOLINI, Joseph
hal.structure.identifierIowa State University [ISU]
dc.contributor.authorMOCHEL, Jonathan
hal.structure.identifierRoswell Park Cancer Institute [Buffalo] [RPCI]
dc.contributor.authorMASTRI, Michalis
hal.structure.identifierRoswell Park Cancer Institute [Buffalo] [RPCI]
dc.contributor.authorEBOS, John
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPOIGNARD, Clair
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBENZEKRY, Sébastien
dc.date.accessioned2024-04-04T02:58:44Z
dc.date.available2024-04-04T02:58:44Z
dc.date.issued2019-11-12
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192681
dc.description.abstractEnQuantitative analysis of tumor growth kinetics has been widely carried out using mathematical models. In the majority of cases, individual or average data were fitted. Here, we analyzed three classical models (exponential, logistic and Gom-pertz within the statistical framework of nonlinear mixed-effects modelling , which allowed us to account for inter-animal variability within a population group. We used in vivo data of subcutaneously implanted Lewis Lung carcinoma cells. While the exponential and logistic models failed to accurately fit the data, the Gompertz model provided a superior descriptive power. Moreover, we observed a strong correlation between the Gompertz parameters. Combining this observation with rigorous population parameter estimation motivated a simplification of the standard Gompertz model in a reduced Gompertz model, with only one individual parameter. Using Bayesian inference, we further applied the population methodology to predict the individual initiation times of the tumors from only three measurements. Thanks to its simplicity, the reduced Gompertz model exhibited superior predictive power. The method that we propose here remains to be extended to clinical data, but these results are promising for the personalized estimation of the tumor age given limited data at diagnosis.
dc.language.isoen
dc.source.titleMathematical and Computational Oncology
dc.subject.enTumor growth kinetics
dc.subject.enGompertz model
dc.subject.enMixed-effects modeling
dc.subject.enBayesian estimation
dc.title.enPopulation Modeling of Tumor Growth Curves, the Reduced Gompertz Model and Prediction of the Age of a Tumor
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1007/978-3-030-35210-3_7
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Applications [stat.AP]
dc.subject.halStatistiques [stat]/Calcul [stat.CO]
bordeaux.page87-97
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingMathematical and Computational Oncology
hal.identifierhal-02383995
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02383995v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Mathematical%20and%20Computational%20Oncology&rft.date=2019-11-12&rft.spage=87-97&rft.epage=87-97&rft.au=VAGHI,%20Cristina&RODALLEC,%20Anne&FANCIULLINO,%20Raphaelle&CICCOLINI,%20Joseph&MOCHEL,%20Jonathan&rft.genre=unknown


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