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dc.contributor.authorAngeli, Steliosen
dc.contributor.authorEmblem, Kyrre E.en
dc.contributor.authorDue-Tonnessen, Paulinaen
dc.contributor.authorStylianopoulos, Triantafyllosen
dc.creatorAngeli, Steliosen
dc.creatorEmblem, Kyrre E.en
dc.creatorDue-Tonnessen, Paulinaen
dc.creatorStylianopoulos, Triantafyllosen
dc.date.accessioned2021-01-27T10:17:51Z
dc.date.available2021-01-27T10:17:51Z
dc.date.issued2018
dc.identifier.issn2213-1582
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/63816
dc.description.abstractPrevious studies to simulate brain tumor progression, often investigate either temporal changes in cancer cell density or the overall tissue-level growth of the tumor mass. Here, we developed a computational model to bridge these two approaches. The model incorporates the tumor biomechanical response at the tissue level and accounts for cellular events by modeling cancer cell proliferation, infiltration to surrounding tissues, and invasion to distant locations. Moreover, acquisition of high resolution human data from anatomical magnetic resonance imaging, diffusion tensor imaging and perfusion imaging was employed within the simulations towards a realistic and patient specific model. The model predicted the intratumoral mechanical stresses to range from 20 to 34 kPa, which caused an up to 4.5 mm displacement to the adjacent healthy tissue. Furthermore, the model predicted plausible cancer cell invasion patterns within the brain along the white matter fiber tracts. Finally, by varying the tumor vascular density and its invasive outer ring thickness, our model showed the potential of these parameters for guiding the timing (83–90 days) of cancer cell distant invasion as well as the number (0–2 sites) and location (temportal and/or parietal lobe) of the invasion sites.en
dc.language.isoenen
dc.sourceNeuroImage: Clinicalen
dc.source.urihttp://www.sciencedirect.com/science/article/pii/S2213158218302754
dc.titleTowards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRIen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.nicl.2018.08.032
dc.description.volume20
dc.description.startingpage664
dc.description.endingpage673
dc.author.facultyΠολυτεχνική Σχολή / Faculty of Engineering
dc.author.departmentΤμήμα Μηχανικών Μηχανολογίας και Κατασκευαστικής / Department of Mechanical and Manufacturing Engineering
dc.type.uhtypeArticleen
dc.source.abbreviationNeuroImage: Clinicalen
dc.contributor.orcidStylianopoulos, Triantafyllos [0000-0002-3093-1696]
dc.gnosis.orcid0000-0002-3093-1696


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