Are complex DCE-MRI models supported by clinical data?

Purpose 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. Methods 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. Results 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)]. Conclusion 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