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Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted

Fredrik Langkilde1, Thiele Kobus2,3, Andriy Fedorov2

  • 1Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.

Magnetic Resonance in Medicine
|July 19, 2017
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Summary
This summary is machine-generated.

This study compared diffusion imaging models for prostate cancer, finding that biexponential and gamma models best fit the data. These models, along with others, effectively differentiate cancerous from healthy prostate tissue.

Keywords:
biexponential modeldiffusion weighted imaginggamma distribution modelkurtosis modelprostate cancerstretched exponential model

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Area of Science:

  • Radiology and Imaging Science
  • Biomedical Engineering
  • Oncology

Background:

  • Diffusion-weighted imaging (DWI) is crucial for prostate cancer assessment.
  • Advanced diffusion models offer potential for improved tissue characterization beyond traditional monoexponential analysis.
  • High b-factor DWI provides more detailed microstructural information.

Purpose of the Study:

  • To compare the fitting performance and tissue discrimination capabilities of biexponential, kurtosis, stretched exponential, and gamma distribution models.
  • To evaluate these models using high b-factor diffusion-weighted images in prostate cancer patients.
  • To determine which model parameters best differentiate normal prostate tissue from cancerous lesions.

Main Methods:

  • Acquired diffusion-weighted images with 15 b-factors (0-3500 s/mm²) in 62 prostate cancer patients.
  • Performed pixel-wise signal decay fitting using biexponential, kurtosis, stretched exponential, and gamma distribution models.
  • Evaluated model fits using Akaike Information Criterion (AIC) and assessed tissue discrimination via receiver operating characteristic (ROC) analysis.

Main Results:

  • Biexponential and gamma distribution models demonstrated superior fit with the lowest AIC values.
  • All evaluated models, including monoexponential fits to lower b-ranges, achieved high areas under the curve (AUCs) for discriminating normal from cancerous tissue (0.93-0.97).
  • Specific parameters from kurtosis and stretched exponential models (single parameters) and biexponential and gamma models (parameter combinations) showed the highest AUCs and correlated with Gleason score.

Conclusions:

  • Advanced diffusion models, particularly biexponential and gamma, offer robust fitting for high b-factor DWI in prostate cancer.
  • Similar to simpler models, these advanced models effectively discriminate between normal and cancerous prostate tissue.
  • The biexponential model, favored statistically, provides valuable insights into microstructural changes associated with prostate cancer and its aggressiveness.