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Diffusion-based microstructure models in brain tumours: Fitting in presence of a model-microstructure mismatch.

Umberto Villani1, Erica Silvestri1, Marco Castellaro1

  • 1Padova Neuroscience Center, University of Padova, Padova, Italy; Department of Information Engineering, University of Padova, Padova, Italy.

Neuroimage. Clinical
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Summary

Diffusion imaging models show stable fitting in brain tumors, offering reliable biomarkers for disease monitoring. Analysis confirmed similar precision in tumor and healthy tissues, identifying redundant metrics for data-driven approaches.

Keywords:
Brain tumoursDiffusion MRIFisher Information MatrixMicrostructureSensitivity analysis

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Oncology

Background:

  • Diffusion-based biophysical models are increasingly used to study brain tumor microenvironments.
  • The interpretation of model parameters and their stability in cancerous regions require further investigation.
  • Reliable biomarkers are needed for monitoring brain tumor progression and treatment response.

Purpose of the Study:

  • To assess the mathematical stability and reliability of diffusion imaging models in brain tumor tissues.
  • To compare parameter estimation from Neurite Orientation Dispersion and Density Imaging (NODDI), Spherical Mean Technique (SMT), and Diffusion Kurtosis Imaging (DKI) in healthy and tumoral brain regions.
  • To identify potential biomarkers by analyzing the correlation between different diffusion imaging metrics.

Main Methods:

  • Analysis of fitting results from NODDI, SMT, and DKI in 11 brain tumor patients.
  • Computation of residual sum of squares for goodness-of-fit assessment.
  • Evaluation of parameter precision using standard deviation and sensitivity functions across different tissue types.

Main Results:

  • Diffusion models (NODDI, SMT, DKI) demonstrated stable fitting within tumoral brain lesions.
  • Goodness-of-fit and parameter precision were comparable between healthy and pathological brain tissues.
  • Significant collinearity was observed among certain diffusion imaging metrics, suggesting redundancy in data-driven analyses.

Conclusions:

  • Diffusion imaging models are mathematically stable for analyzing brain tumor microenvironments.
  • These models provide reliable parameter estimates in both healthy and cancerous brain tissues, supporting their use as biomarkers.
  • Identifying and excluding collinear metrics can optimize data-driven approaches for brain tumor analysis.