000127779 001__ 127779
000127779 005__ 20241125101144.0
000127779 0247_ $$2doi$$a10.1016/j.cmpb.2023.107742
000127779 0248_ $$2sideral$$a134899
000127779 037__ $$aART-2023-134899
000127779 041__ $$aeng
000127779 100__ $$0(orcid)0000-0002-3784-1140$$aBorau, C.$$uUniversidad de Zaragoza
000127779 245__ $$aA multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma
000127779 260__ $$c2023
000127779 5060_ $$aAccess copy available to the general public$$fUnrestricted
000127779 5203_ $$aNeuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.
000127779 536__ $$9info:eu-repo/grantAgreement/ES/DGA/2019-23$$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE
000127779 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000127779 590__ $$a4.9$$b2023
000127779 592__ $$a1.189$$b2023
000127779 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b32 / 170 = 0.188$$c2023$$dQ1$$eT1
000127779 593__ $$aComputer Science Applications$$c2023$$dQ1
000127779 591__ $$aMEDICAL INFORMATICS$$b9 / 44 = 0.205$$c2023$$dQ1$$eT1
000127779 593__ $$aSoftware$$c2023$$dQ1
000127779 591__ $$aENGINEERING, BIOMEDICAL$$b30 / 123 = 0.244$$c2023$$dQ1$$eT1
000127779 593__ $$aHealth Informatics$$c2023$$dQ1
000127779 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b20 / 144 = 0.139$$c2023$$dQ1$$eT1
000127779 594__ $$a12.3$$b2023
000127779 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000127779 700__ $$aWertheim, K.Y.
000127779 700__ $$0(orcid)0000-0001-8324-5596$$aHervas-Raluy, S.$$uUniversidad de Zaragoza
000127779 700__ $$0(orcid)0000-0001-7452-0437$$aSainz-DeMena, D.$$uUniversidad de Zaragoza
000127779 700__ $$aWalker, D.
000127779 700__ $$aChisholm, R.
000127779 700__ $$aRichmond, P.
000127779 700__ $$aVarella, V.
000127779 700__ $$aViceconti, M.
000127779 700__ $$aMontero, A.
000127779 700__ $$aGregori-Puigjané, E.
000127779 700__ $$aMestres, J.
000127779 700__ $$aKasztelnik, M.
000127779 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, J.M.$$uUniversidad de Zaragoza
000127779 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000127779 773__ $$g241 (2023), 107742 [14 pp.]$$pComput. methods programs biomed.$$tComputer Methods and Programs in Biomedicine$$x0169-2607
000127779 8564_ $$s3499193$$uhttps://zaguan.unizar.es/record/127779/files/texto_completo.pdf$$yVersión publicada
000127779 8564_ $$s2455801$$uhttps://zaguan.unizar.es/record/127779/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000127779 909CO $$ooai:zaguan.unizar.es:127779$$particulos$$pdriver
000127779 951__ $$a2024-11-22-12:03:38
000127779 980__ $$aARTICLE