Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models

Background/objectives: Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used in tumor diagnosis, staging and assessment of therapy response for different types of tumors, thanks to its capability to provide important functional information about tissue microvasculature. Tracer kinetic models used for estimating microcirculatory parameters can be broadly categorized as conventional compartmental (CC) or distributed- parameter (DP) models. While DP models seem to be more realistic, CC models (in particular the Tofts and the Brix models) have been widely used in clinical investigations over the past two decades. However, to date there is no direct comparison of CC vs DP models on real breast DCE-MRI data; moreover, a direct comparison between Tofts and Brix models, has not yet been reported on real breast data. Therefore, the purpose of this study was two-fold: on the one hand we analyzed the performance, on real breast DCE-MRI data, of CC vs DP models in terms of goodness-of-fit metrics; on the other hand we compared Tofts and Brix models on the basis of real breast DCE-MRI data. Methods: Three models were compared: two CC models (the Tofts and the Brix models) and one DP model (the ATH model). We gathered data in two different scenarios: DCE-MRI with high temporal resolution obtained by means of a k -space under-sampling and data sharing method known as Time-resolved angiography With Stochastic Trajectories (TWIST) and DCE-MRI with low temporal resolution obtained by means of the Spoiled Gradient-Echo k -space scheme known as Fast Low Angle Shot (FLASH). The performances of the three models were evaluated by means of three goodness-of-fit metrics: the Residual Sum of Squares, the Bayesian Information Criterion and the Akaike Information Criterion on four breast DCE-MRI examinations. Results: Although not conclusive, the results of this study suggest that the ATH model can achieve better fit in comparison to the Tofts and Brix models for TWIST data; and that the Brix model can achieve better fit with respect to the Tofts model for FLASH data. Conclusion: Given the current typical settings of clinical breast DCE-MRI examinations, there seems not to be a clear advantage, in terms of goodness-of-fit, of ATH with respect to Tofts and Brix models; moreover, at lower temporal resolution the Brix model can achieve better fit than the Tofts model.