000147221 001__ 147221
000147221 005__ 20241216115435.0
000147221 0247_ $$2doi$$a10.3389/fphys.2024.1421591
000147221 0248_ $$2sideral$$a141064
000147221 037__ $$aART-2024-141064
000147221 041__ $$aeng
000147221 100__ $$aPérez-Benito, Ángela$$uUniversidad de Zaragoza
000147221 245__ $$aPatient-specific prostate tumour growth simulation: a first step towards the digital twin
000147221 260__ $$c2024
000147221 5060_ $$aAccess copy available to the general public$$fUnrestricted
000147221 5203_ $$aProstate cancer (PCa) is a major world-wide health concern. Current diagnostic methods involve Prostate-Specific Antigen (PSA) blood tests, biopsies, and Magnetic Resonance Imaging (MRI) to assess cancer aggressiveness and guide treatment decisions. MRI aligns with in silico medicine, as patient-specific image biomarkers can be obtained, contributing towards the development of digital twins for clinical practice. This work presents a novel framework to create a personalized PCa model by integrating clinical MRI data, such as the prostate and tumour geometry, the initial distribution of cells and the vasculature, so a full representation of the whole prostate is obtained. On top of the personalized model construction, our approach simulates and predicts temporal tumour growth in the prostate through the Finite Element Method, coupling the dynamics of tumour growth and the transport of oxygen, and incorporating cellular processes such as proliferation, differentiation, and apoptosis. In addition, our approach includes the simulation of the PSA dynamics, which allows to evaluate tumour growth through the PSA patient’s levels. To obtain the model parameters, a multi-objective optimization process is performed to adjust the best parameters for two patients simultaneously. This framework is validated by means of data from four patients with several MRI follow-ups. The diagnosis MRI allows the model creation and initialization, while subsequent MRI-based data provide additional information to validate computational predictions. The model predicts prostate and tumour volumes growth, along with serum PSA levels. This work represents a preliminary step towards the creation of digital twins for PCa patients, providing personalized insights into tumour growth.
000147221 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T50-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709
000147221 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000147221 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000147221 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, José Manuel$$uUniversidad de Zaragoza
000147221 700__ $$0(orcid)0000-0002-1878-8997$$aGómez-Benito, María José$$uUniversidad de Zaragoza
000147221 700__ $$0(orcid)0000-0002-2901-4188$$aPérez Ansón, María Ángeles$$uUniversidad de Zaragoza
000147221 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000147221 773__ $$g15 (2024), [22 pp.]$$pFront. physiol.$$tFrontiers in physiology$$x1664-042X
000147221 8564_ $$s791576$$uhttps://zaguan.unizar.es/record/147221/files/texto_completo.pdf$$yVersión publicada
000147221 8564_ $$s2310748$$uhttps://zaguan.unizar.es/record/147221/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000147221 909CO $$ooai:zaguan.unizar.es:147221$$particulos$$pdriver
000147221 951__ $$a2024-12-16-11:27:34
000147221 980__ $$aARTICLE