title: Are complex DCE-MRI models supported by clinical data?
abstract: To ascertain whether complex dynamic contrast enhanced (DCE) MRI tracer kinetic models are supported by data acquired in the clinic and to determine the consequences of limited contrast-to-noise.
  
  
  
  Generically representative in silico and clinical (cervical cancer) DCE-MRI data were examined. Bayesian model selection evaluated support for four compartmental DCE-MRI models: the Tofts model (TM), Extended Tofts model, Compartmental Tissue Uptake model (CTUM), and Two-Compartment Exchange model.
  
  
  
  Complex DCE-MRI models were more sensitive to noise than simpler models with respect to both model selection and parameter estimation. Indeed, as contrast-to-noise decreased, complex DCE models became less probable and simpler models more probable. The less complex TM and CTUM were the optimal models for the DCE-MRI data acquired in the clinic. [In cervical tumors, Ktrans, Fp, and PS increased after radiotherapy (P = 0.004, 0.002, and 0.014, respectively)].
  
  
  
  Caution is advised when considering application of complex DCE-MRI kinetic models to data acquired in the clinic. It follows that data-driven model selection is an important prerequisite to DCE-MRI analysis. Model selection is particularly important when high-order, multiparametric models are under consideration. (Parameters obtained from kinetic modeling of cervical cancer clinical DCE-MRI data showed significant changes at an early stage of radiotherapy.) Magn Reson Med 77:1329-1339, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Department of Chemistry, Washington University, Saint Louis, Missouri, USA.
authors.forename: Chong
authors.initials: C
authors.lastname: Duan
authors.affiliation: Department of Medical Physics, Aarhus University, Aarhus, Denmark.|Department of Oncology, Aarhus University, Aarhus, Denmark.
authors.forename: Jesper F
authors.initials: JF
authors.lastname: Kallehauge
authors.affiliation: Department of Radiology, Washington University, Saint Louis, Missouri, USA.
authors.forename: G Larry
authors.initials: GL
authors.lastname: Bretthorst
authors.affiliation: Department of Oncology, Aarhus University, Aarhus, Denmark.|Department of Radiation Oncology, Washington University, Saint Louis, Missouri, USA.|Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.
authors.forename: Kari
authors.initials: K
authors.lastname: Tanderup
authors.affiliation: Department of Chemistry, Washington University, Saint Louis, Missouri, USA.|Department of Radiology, Washington University, Saint Louis, Missouri, USA.|Department of Medicine, Washington University, Saint Louis, Missouri, USA.|Alvin J Siteman Cancer Center, Washington University, Saint Louis, Missouri, USA.
authors.forename: Joseph J H
authors.initials: JJ
authors.lastname: Ackerman
authors.affiliation: Department of Radiology, Washington University, Saint Louis, Missouri, USA.|Alvin J Siteman Cancer Center, Washington University, Saint Louis, Missouri, USA.
authors.forename: Joel R
authors.initials: JR
authors.lastname: Garbow
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D019317:Evidence-Based Medicine; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007700:Kinetics; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D002583:Uterine Cervical Neoplasms / Q000188:drug therapy* / Q000378:metabolism*
chemical_list: D003287:Contrast Media
keywords: Bayesian inference; DCE-MRI; cervical cancer; model selection; pharmacokinetics; tracer kinetic modeling