000135362 001__ 135362
000135362 005__ 20241220120718.0
000135362 0247_ $$2doi$$a10.1007/s00366-024-01964-6
000135362 0248_ $$2sideral$$a138596
000135362 037__ $$aART-2024-138596
000135362 041__ $$aeng
000135362 100__ $$0(orcid)0000-0001-8324-5596$$aHervas-Raluy, Silvia$$uUniversidad de Zaragoza
000135362 245__ $$aImage-based biomarkers for engineering neuroblastoma patient-specific computational models
000135362 260__ $$c2024
000135362 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135362 5203_ $$aChildhood 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.
000135362 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$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE$$9info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541$$9info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/PID2021-122409OB-C21
000135362 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135362 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135362 700__ $$0(orcid)0000-0001-7452-0437$$aSainz-DeMena, Diego$$uUniversidad de Zaragoza
000135362 700__ $$0(orcid)0000-0002-1878-8997$$aGómez-Benito, Maria José$$uUniversidad de Zaragoza
000135362 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, José Manuel$$uUniversidad de Zaragoza
000135362 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000135362 773__ $$g40 (2024), 3215–3231$$pEng. comput.$$tENGINEERING WITH COMPUTERS$$x0177-0667
000135362 8564_ $$s3563791$$uhttps://zaguan.unizar.es/record/135362/files/texto_completo.pdf$$yVersión publicada
000135362 8564_ $$s2516999$$uhttps://zaguan.unizar.es/record/135362/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135362 909CO $$ooai:zaguan.unizar.es:135362$$particulos$$pdriver
000135362 951__ $$a2024-12-20-12:05:37
000135362 980__ $$aARTICLE