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

  • 0Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Frontiers in Physiology +

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November 14, 2024

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November 14, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a new computational framework for personalized prostate cancer (PCa) modeling using MRI data. It simulates tumor growth and Prostate-Specific Antigen (PSA) dynamics, aiding in digital twin development for PCa patients.

Area Of Science

  • Computational biology and bioinformatics
  • Medical imaging and in silico medicine
  • Oncology and urology

Background

  • Prostate cancer (PCa) poses a significant global health challenge.
  • Current diagnostic tools like Prostate-Specific Antigen (PSA) tests, biopsies, and MRI have limitations in assessing cancer aggressiveness.
  • Magnetic Resonance Imaging (MRI) offers potential for personalized medicine through image biomarkers, supporting the development of digital twins.

Purpose Of The Study

  • To develop a novel framework for creating personalized PCa models using integrated clinical MRI data.
  • To simulate and predict temporal tumor growth and Prostate-Specific Antigen (PSA) dynamics within a personalized prostate model.
  • To advance the creation of digital twins for PCa patients, offering personalized insights into tumor progression.

Main Methods

  • Integration of patient-specific clinical MRI data (prostate/tumor geometry, cell distribution, vasculature) to construct a personalized prostate model.
  • Finite Element Method (FEM) simulation of tumor growth dynamics, oxygen transport, and cellular processes (proliferation, differentiation, apoptosis).
  • Multi-objective optimization to calibrate model parameters using data from two patients simultaneously; validation with data from four patients with multiple MRI follow-ups.

Main Results

  • The framework successfully creates personalized PCa models from diagnostic MRI.
  • Simulations accurately predict prostate and tumor volume growth, and serum PSA levels over time, validated against follow-up MRIs.
  • The model effectively integrates tumor growth dynamics with PSA level predictions.

Conclusions

  • This work presents a validated computational framework for personalized PCa modeling.
  • The approach enables prediction of tumor growth and PSA dynamics, crucial for personalized treatment strategies.
  • This represents a significant preliminary step towards developing functional digital twins for prostate cancer management.

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