Skip to main content

ORIGINAL RESEARCH article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1421591
This article is part of the Research Topic Multiscale cancer modeling, in silico oncology, in silico psycho-oncology and digital (virtual) twins in the cancer domain View all 3 articles

Patient-specific prostate tumour growth simulation: A first step towards the digital twin

Provisionally accepted
Ángela Pérez-Benito Ángela Pérez-Benito 1,2Jose Manuel Garcia-Aznar Jose Manuel Garcia-Aznar 1,2Maria Jose Gomez-Benito Maria Jose Gomez-Benito 1,2Maria Angeles Perez Anson Maria Angeles Perez Anson 1,2*
  • 1 University of Zaragoza, Zaragoza, Spain
  • 2 Institute of Engineering Research in Aragon, University of Zaragoza, Zaragoza, Aragon, Spain

The final, formatted version of the article will be published soon.

    Prostate 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 two 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.

    Keywords: prostate cancer, In-silico model, patient-specific, imaging biomarkers, computational oncology, Finite Element Method (FEM)

    Received: 22 Apr 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 Pérez-Benito, Garcia-Aznar, Gomez-Benito and Perez Anson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Maria Angeles Perez Anson, Institute of Engineering Research in Aragon, University of Zaragoza, Zaragoza, 50018, Aragon, Spain

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.