Image-based biomarkers for engineering neuroblastoma patient-specific computational models

Hervas-Raluy, Silvia (Universidad de Zaragoza) ; Sainz-DeMena, Diego (Universidad de Zaragoza) ; Gómez-Benito, Maria José (Universidad de Zaragoza) ; García-Aznar, José Manuel (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1007/s00366-024-01964-6

Año: 2024

Publicado en: ENGINEERING WITH COMPUTERS 40 (2024), 3215–3231
ISSN: 0177-0667

Factor impacto JCR: 4.9 (2024)

Categ. JCR: ENGINEERING, MECHANICAL rank: 30 / 182 = 0.165 (2024) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 43 / 175 = 0.246 (2024) - Q1 - T1

Factor impacto SCIMAGO: 1.11 - Engineering (miscellaneous) (Q1) - Software (Q1) - Modeling and Simulation (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/2019-23
Financiación: 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
Financiación: info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541
Financiación: info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/PID2021-122409OB-C21

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)