title: Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.
abstract: BACKGROUND
  Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains crucial information about tumour heterogeneity and the transport limitations that reduce drug efficacy. Mathematical modelling of drug delivery and cellular responsiveness based on underutilised DCE-MRI data has the unique potential to predict therapeutic responsiveness for individual patients.
  
  
  METHODS
  To interpret DCE-MRI data, we created a modelling framework that operates over multiple time and length scales and incorporates intracellular metabolism, nutrient and drug diffusion, trans-vascular permeability, and angiogenesis. The computational methodology was used to analyse DCE-MR images collected from eight breast cancer patients at Baystate Medical Center in Springfield, MA.
  
  
  RESULTS
  Computer simulations showed that trans-vascular transport was correlated with tumour aggressiveness because increased vessel growth and permeability provided more nutrients for cell proliferation. Model simulations also indicate that vessel density minimally affects tissue growth and drug response, and nutrient availability promotes growth. Finally, the simulations indicate that increased transport heterogeneity is coupled with increased tumour growth and poor drug response.
  
  
  CONCLUSIONS
  Mathematical modelling based on DCE-MRI has the potential to aid treatment decisions and improve overall cancer care. This model is the critical first step in the creation of a comprehensive and predictive computational method.
authors.affiliation: Department of Chemical Engineering, University of Massachusetts, 159 Goessmann Hall, 686 North Pleasant Street, Amherst, MA 01003, USA.
authors.forename: R
authors.initials: R
authors.lastname: Venkatasubramanian
authors.forename: R B
authors.initials: RB
authors.lastname: Arenas
authors.forename: M A
authors.initials: MA
authors.lastname: Henson
authors.forename: N S
authors.initials: NS
authors.lastname: Forbes
mesh_terms: D000328:Adult; D000368:Aged; D000970:Antineoplastic Agents / Q000493:pharmacokinetics* / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000188:drug therapy* / Q000473:pathology; D003198:Computer Simulation; D003287:Contrast Media; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D008954:Models, Biological; D009389:Neovascularization, Pathologic / Q000175:diagnosis / Q000188:drug therapy; D011237:Predictive Value of Tests
chemical_list: D000970:Antineoplastic Agents; D003287:Contrast Media