Are Complex DCE-MRI Models Supported by Clinical Data?

作者：Duan Chong; Kallehauge Jesper F; Bretthorst G Larry; Tanderup Kari; Ackerman Joseph J H; Garbow Joel R^*

来源：Magnetic Resonance in Medicine, 2017, 77(3): 1329-1339.

DOI：10.1002/mrm.26189

• DCE-MRI
• tracer kinetic modeling
• pharmacokinetics
• model selection
• Bayesian inference
• cervical cancer

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.

• 出版日期2017-3