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Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer

hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorBILOUS, Maria
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorSERDJEBI, Cindy
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
hal.structure.identifierService d'oncologie multidisciplinaire innovations thérapeutiques [Hôpital Nord - APHM]
dc.contributor.authorBOYER, Arnaud
hal.structure.identifierHôpital Haut-Lévêque [CHU Bordeaux]
dc.contributor.authorPOUYPOUDAT, Claudia
hal.structure.identifierService d'oncologie multidisciplinaire innovations thérapeutiques [Hôpital Nord - APHM]
dc.contributor.authorTOMASINI, Pascale
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorBARBOLOSI, Dominique
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
hal.structure.identifierService d'oncologie multidisciplinaire innovations thérapeutiques [Hôpital Nord - APHM]
hal.structure.identifierCentre de Recherche en Cancérologie de Marseille [CRCM]
dc.contributor.authorBARLESI, Fabrice
hal.structure.identifierInstitut Bergonié [Bordeaux]
dc.contributor.authorCHOMY, François
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBENZEKRY, Sébastien
dc.date.accessioned2024-04-04T02:59:55Z
dc.date.available2024-04-04T02:59:55Z
dc.date.issued2019-09
dc.identifier.issn2045-2322
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192781
dc.description.abstractEnBrain metastases (BMs) are the largest disabling site for non-small cell lung cancers, but are only visible when sizeable. Individualized prediction of the BM risk and extent is a major challenge for therapeutic decision. This study assesses mechanistic models of BM apparition and growth against clinical imaging data.We implemented a quantitative computational method to confront biologicallyinformed mathematical models to clinical data of BMs. Primary tumor growth parameters were estimated from size at diagnosis and histology. Metastatic dissemination and growth parameters were fitted to either population data of BM probability (n=183 patients) or longitudinal measurements of number and size of visible BMs (63 size measurements in two patients). Pre-clinical phases from first cancer cell to detection were estimated to 2.1-5.3 years. A model featuringdormancy was best able to describe the longitudinal data, as well as BM probability as a function of primary tumor size at diagnosis. It predicted first appearance of BMs at 14-19 months pre-diagnosis. Model-informed predictions of invisible cerebral disease burden could be used to inform therapeutic intervention.
dc.language.isoen
dc.publisherNature Publishing Group
dc.title.enQuantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer
dc.typeArticle de revue
dc.identifier.doi10.1038/s41598-019-49407-3
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halMathématiques [math]
bordeaux.journalScientific Reports
bordeaux.volume9
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-01928442
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01928442v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Scientific%20Reports&rft.date=2019-09&rft.volume=9&rft.issue=1&rft.eissn=2045-2322&rft.issn=2045-2322&rft.au=BILOUS,%20Maria&SERDJEBI,%20Cindy&BOYER,%20Arnaud&POUYPOUDAT,%20Claudia&TOMASINI,%20Pascale&rft.genre=article


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