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Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

hal.structure.identifierUniversity of Texas at Austin [Austin]
dc.contributor.authorYANKEELOV, Thomas E.
hal.structure.identifierUniversity of Chicago
dc.contributor.authorAN, Gary
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorSAUT, Oliver
hal.structure.identifierWashington University in Saint Louis [WUSTL]
dc.contributor.authorGENIN, Guy M.
hal.structure.identifierFred Hutchinson Cancer Research Center [Seattle] [FHCRC]
dc.contributor.authorLUEBECK, E. Georg
hal.structure.identifierJohns Hopkins University [JHU]
dc.contributor.authorPOPEL, Aleksander S.
hal.structure.identifierRoche Pharma Research and Early Development [Basel] [pRED]
dc.contributor.authorRIBBA, Benjamin
hal.structure.identifierMedImmune
dc.contributor.authorVICINI, Paolo
hal.structure.identifierWake Forest University
dc.contributor.authorZHOU, Xiaobo
hal.structure.identifierVanderbilt University [Nashville]
dc.contributor.authorWEIS, Jared A.
hal.structure.identifierState University of New York [SUNY]
dc.contributor.authorYE, Kaiming
dc.date.accessioned2024-04-04T03:13:06Z
dc.date.available2024-04-04T03:13:06Z
dc.date.issued2016-07-06
dc.identifier.issn0090-6964
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193927
dc.description.abstractEnHierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncol- ogy. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrat- ing their application as well as the current gap between pre- clinical and clinical applications. We conclude with a discus- sion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.
dc.language.isoen
dc.publisherSpringer Verlag
dc.subject.enMathematical modeling
dc.subject.enCancer
dc.subject.enCancer screening
dc.subject.enComputational modeling
dc.subject.enNumerical modeling
dc.subject.enAgent-based modeling
dc.subject.enPredictive oncology
dc.subject.enEpidemiology
dc.title.enMulti-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success
dc.typeArticle de revue
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halMathématiques [math]/Equations aux dérivées partielles [math.AP]
bordeaux.journalAnnals of Biomedical Engineering
bordeaux.volume44
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue9
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01396241
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01396241v1
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