A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Borau, C. (Universidad de Zaragoza) ; Wertheim, K.Y. ; Hervas-Raluy, S. (Universidad de Zaragoza) ; Sainz-DeMena, D. (Universidad de Zaragoza) ; Walker, D. ; Chisholm, R. ; Richmond, P. ; Varella, V. ; Viceconti, M. ; Montero, A. ; Gregori-Puigjané, E. ; Mestres, J. ; Kasztelnik, M. ; García-Aznar, J.M. (Universidad de Zaragoza)

Resumen: Neuroblastoma 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.

Idioma: Inglés

DOI: 10.1016/j.cmpb.2023.107742

Año: 2023

Publicado en: Computer Methods and Programs in Biomedicine 241 (2023), 107742 [14 pp.]
ISSN: 0169-2607

Factor impacto JCR: 4.9 (2023)

Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 32 / 170 = 0.188 (2023) - Q1 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 9 / 44 = 0.205 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 30 / 123 = 0.244 (2023) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, THEORY & METHODS rank: 20 / 144 = 0.139 (2023) - Q1 - T1

Factor impacto CITESCORE: 12.3 - Software (Q1) - Health Informatics (Q1) - Computer Science Applications (Q1)

Factor impacto SCIMAGO: 1.189 - Computer Science Applications (Q1) - Software (Q1) - Health Informatics (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

Tipo y forma: Artículo (Versión definitiva)

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