Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests.

Journal: NPJ digital medicine
Published Date: Jul 29, 2025

Abstract

Existing prostate cancer monitoring methods, reliant on prostate-specific antigen (PSA) measurements in blood tests often fail to detect tumor growth. We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based modeling and machine learning in digital twins. The physics-based model considers PSA secretion and flux from tissue to blood, depending on local vascularity. This model is enhanced by deep learning, which regulates tumor growth dynamics through the patient's PSA blood tests and 3D spatial interactions of physiological variables of the digital twin. We showcase our framework by reconstructing tumor growth in real patients over 2.5 years from diagnosis, with tumor volume relative errors ranging from 0.8% to 12.28%. Additionally, our results reveal scenarios of tumor growth despite no significant rise in PSA levels. Therefore, our framework serves as a promising tool for prostate cancer monitoring, supporting the advancement of personalized monitoring protocols.

Authors

  • Daniel Camacho-Gomez
    Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of Zaragoza, Zaragoza, Spain.
  • Carlos Borau
    Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of Zaragoza, Zaragoza, Spain.
  • Jose Manuel Garcia-Aznar
    University of Zaragoza, Zaragoza, Spain.
  • Maria Jose Gomez-Benito
    Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of Zaragoza, Zaragoza, Spain.
  • Mark Girolami
    Department of Statistics, University of Warwick, UK.
  • Maria Angeles Perez
    Multiscale in Mechanical and Biological Engineering, Aragon Institute of Engineering Research (I3A), Aragon Institute of Healthcare Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain.

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