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A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer
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A model describing diffusion in prostate cancer.

Nima Gilani1, Paul Malcolm2, Glyn Johnson1

  • 1Norwich Medical School, University of East Anglia, Norwich, United Kingdom.

Magnetic Resonance in Medicine
|July 22, 2016
PubMed
Summary
This summary is machine-generated.

A new model accurately predicts prostate cancer diffusion MRI behavior by accounting for tissue compartments and exchange effects. This improves understanding of apparent diffusion coefficient (ADC) variability in prostate cancer grading.

Keywords:
Gleason scorebiexponential diffusiondiffusionkurtosisprostate MRI

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Biomedical Engineering
  • Oncology

Background:

  • Quantitative diffusion MRI is used for prostate cancer grading.
  • Prostate tissue complexity (vascular, ductal, epithelial, stromal compartments) complicates diffusion measurements.
  • Current diffusion metrics are an average of tissue compartments.

Purpose of the Study:

  • Develop a model for prostate diffusion MRI analysis.
  • Account for tissue compartmentalization, exchange effects, and non-Gaussian diffusion.
  • Improve interpretation of diffusion MRI in prostate cancer.

Main Methods:

  • Developed a compartmental exchange model for prostate tissue diffusion.
  • Used Monte Carlo simulation and a two-compartment exchange model.
  • Validated model predictions against 41 published studies using 10-fold cross-validation.

Main Results:

  • Model predictions showed good agreement with published results.
  • The model successfully explains the variability in apparent diffusion coefficient (ADC) estimates.
  • Demonstrated reliable prediction of diffusion behavior across different Gleason scores.

Conclusions:

  • A reliable model for predicting prostate tissue diffusion MRI has been developed.
  • The model accounts for tissue complexity and improves ADC estimation.
  • This model aids in the diffusion MRI-based grading of prostate cancer.