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001043301 005__ 20250724210254.0
001043301 037__ $$aFZJ-2025-02822
001043301 041__ $$aEnglish
001043301 1001_ $$0P:(DE-Juel1)188557$$aBehle, Eric$$b0$$eCorresponding author$$ufzj
001043301 1112_ $$aDPG Spring Meeting 2025$$cRegensburg$$d2025-03-16 - 2025-03-21$$gSKM25$$wGermany
001043301 245__ $$aLeveraging Experimental Vasculature Data for High Resolution Brain Tumor Simulations
001043301 260__ $$c2025
001043301 3367_ $$033$$2EndNote$$aConference Paper
001043301 3367_ $$2BibTeX$$aINPROCEEDINGS
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001043301 520__ $$aCancer remains a leading cause of mortality. Multidisciplinary studies probe its pathology to increase treatment options. Computational modeling of tumors on HPC resources offers insight into its progress and an avenue for advancing our understanding. However, initialization and parameterization of the underlying models require high-resolution data from real tissue structures. Here, we leveraged HPC resources and a massive dataset of a mouse brain's entire vascular network. We processed these image stacks into detailed 3D representations, identified brain regions of interest, and conducted a series of large-scale simulations to investigate how tumor growth is influenced by local vascular network characteristics. By simulating tumor growth with sub-cellular resolution, we can probe to which extent vessel density and network length influence growth. We determined that vessel density is the primary determinant of growth rate. Finally, our results allowed us to extrapolate tumor cell growth predictions for the entire mouse brain, highlighting the critical role of vascular topology in tumor progression. Such increasingly realistic simulations of cancer cells may enable researchers to bridge the gap between basic biology and clinical practice, supporting development of cancer therapies.
001043301 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001043301 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x1
001043301 7001_ $$0P:(DE-HGF)0$$aHerold, J. M.$$b1
001043301 7001_ $$0P:(DE-Juel1)173652$$aSchug, Alexander$$b2$$ufzj
001043301 8564_ $$uhttps://www.dpg-verhandlungen.de/year/2025/conference/regensburg/part/bp/session/14/contribution/9
001043301 909CO $$ooai:juser.fz-juelich.de:1043301$$pVDB
001043301 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188557$$aForschungszentrum Jülich$$b0$$kFZJ
001043301 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173652$$aForschungszentrum Jülich$$b2$$kFZJ
001043301 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001043301 9141_ $$y2025
001043301 920__ $$lyes
001043301 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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001043301 980__ $$aI:(DE-Juel1)JSC-20090406
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