Szczegóły publikacji

Opis bibliograficzny

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma / C. Borau, K. Y. Wertheim, S. Hervas-Raluy, D. Sainz-DeMena, D. Walker, R. Chisholm, P. Richmond, V. Varella, M. Viceconti, A. Montero, E. Gregori-Puigjané, J. Mestres, M. KASZTELNIK, J. M. García-Aznar // Computer Methods and Programs in Biomedicine ; ISSN 0169-2607. — 2023 — vol. 241 art. no. 107742, s. 1–14. — Bibliogr. s. 13–14, Abstr. — Publikacja dostępna online od: 2023-08-01

Autorzy (14)

  • Borau C.
  • Wertheim K. Y.
  • Hervas-Raluy S.
  • Sainz-DeMena D.
  • Walker D.
  • Chisholm R.
  • Richmond P.
  • Varella Vinicius
  • Viceconti Marco
  • Montero A.
  • Gregori-Puigjané E.
  • Mestres J.
  • AGHKasztelnik Marek
  • García-Aznar J. M.

Słowa kluczowe

multi-scalecomputational modellingframeworkcancercloud based decision support systemneuroblastoma

Dane bibliometryczne

ID BaDAP150496
Data dodania do BaDAP2024-01-08
Tekst źródłowyURL
DOI10.1016/j.cmpb.2023.107742
Rok publikacji2023
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaComputer Methods and Programs in Biomedicine

Abstract

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.

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