title: A model describing diffusion in prostate cancer.
abstract: Quantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill-defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non-Gaussian behavior of tissue diffusion.
  
  
  
  The model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two-compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10-fold cross-validation.
  
  
  
  Agreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature.
  
  
  
  A reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316-326, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Norwich Medical School, University of East Anglia, Norwich, United Kingdom.
authors.forename: Nima
authors.initials: N
authors.lastname: Gilani
authors.affiliation: Norfolk and Norwich University Hospital, Norwich, United Kingdom.
authors.forename: Paul
authors.initials: P
authors.lastname: Malcolm
authors.affiliation: Norwich Medical School, University of East Anglia, Norwich, United Kingdom.
authors.forename: Glyn
authors.initials: G
authors.lastname: Johnson
mesh_terms: D000465:Algorithms; D001834:Body Water / Q000737:chemistry* / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000737:chemistry* / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Gleason score; biexponential diffusion; diffusion; kurtosis; prostate MRI