135362 20260217205602.0 doi 10.1007/s00366-024-01964-6 sideral 138596 ART-2024-138596 eng Hervas-Raluy, Silvia Universidad de Zaragoza (orcid)0000-0001-8324-5596 Image-based biomarkers for engineering neuroblastoma patient-specific computational models 2024 Access copy available to the general public Unrestricted Childhood cancer is a devastating disease that requires continued research and improved treatment options to increase survival rates and quality of life for those affected. The response to cancer treatment can vary significantly among patients, highlighting the need for a deeper understanding of the underlying mechanisms involved in tumour growth and recovery to improve diagnostic and treatment strategies. Patient-specific models have emerged as a promising alternative to tackle the challenges in tumour mechanics through individualised simulation. In this study, we present a methodology to develop subject-specific tumour models, which incorporate the initial distribution of cell density, tumour vasculature, and tumour geometry obtained from clinical MRI imaging data. Tumour mechanics is simulated through the Finite Element method, coupling the dynamics of tumour growth and remodelling and the mechano-transport of oxygen and chemotherapy. These models enable a new application of tumour mechanics, namely predicting changes in tumour size and shape resulting from chemotherapeutic interventions for individual patients. Although the specific context of application in this work is neuroblastoma, the proposed methodologies can be extended to other solid tumours. Given the difficulty for treating paediatric solid tumours like neuroblastoma, this work includes two patients with different prognosis, who received chemotherapy treatment. The results obtained from the simulation are compared with the actual tumour size and shape from patients. Overall, the simulations provided clinically useful information to evaluate the effectiveness of the chemotherapy treatment in each case. These results suggest that the biomechanical model could be a valuable tool for personalised medicine in solid tumours. info:eu-repo/grantAgreement/ES/DGA/2019-23 info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541 info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/PID2021-122409OB-C21 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 4.9 2024 ENGINEERING, MECHANICAL 30 / 182 = 0.165 2024 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 43 / 177 = 0.243 2024 Q1 T1 1.11 2024 Computer Science Applications 2024 Q1 Software 2024 Q1 Modeling and Simulation 2024 Q1 Engineering (miscellaneous) 2024 Q1 18.6 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sainz-DeMena, Diego Universidad de Zaragoza (orcid)0000-0001-7452-0437 Gómez-Benito, Maria José Universidad de Zaragoza (orcid)0000-0002-1878-8997 García-Aznar, José Manuel Universidad de Zaragoza (orcid)0000-0002-9864-7683 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 40 (2024), 3215–3231 Eng. comput. ENGINEERING WITH COMPUTERS 0177-0667 3563791 http://zaguan.unizar.es/record/135362/files/texto_completo.pdf Versión publicada 2516999 http://zaguan.unizar.es/record/135362/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135362 articulos driver 2026-02-17-20:45:17 ARTICLE