A clinically feasible method to estimate pharmacokinetic parameters in breast cancer

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the MRI technique of choice for detecting breast cancer, which can be roughly classified as either quantitative or semiquantitative. The major advantage of quantitative DCE-MRI is its ability to provide pharmacokinetic parameters such as volume transfer constant (Ktrans) and extravascular extracellular volume fraction (ve). However, semiquantitative DCE-MRI is still the clinical MRI technique of choice for breast cancer diagnosis due to several major practical difficulties in the implementation of quantitative DCE-MRI in a clinical setting, including (1) long acquisition necessary to acquire 3D T1(0) map, (2) challenges in obtaining accurate artery input function (AIF), (3) long computation time required by conventional nonlinear least square (NLS) fitting, and (4) many illogical values often generated by conventional NLS method. The authors developed a new analysis method to estimate pharmacokinetic parameters Ktrans and ve from clinical DCE-MRI data, including fixed T1(0) to eliminate the long acquisition for T1(0) map and "reference region" model to remove the requirement of measuring AIF. Other techniques used in our analysis method are (1) an improved formula to calculate contrast agent (CA) concentration based on signal intensity of SPGR data, (2) FCM clustering-based techniques for automatic segmentation and generation of a clustered concentration data set (3) an empirical formula for CA time course to fit the clustered data sets, and (4) linear regression for the estimation of pharmacokinetic parameters. Preliminary results from computer simulation and clinical study of 39 patients have demonstrated (1) the feasibility of their analysis method for estimating Ktrans and ve from clinical DCE-MRI data, (2) significantly less illogical values compared to NLS method (typically less than 1% versus more than 7%), (3) relative insensitivity to the noise in DCE-MRI data; (4) reduction in computation time by a factor of more than 30 times compared to NLS method on average, (5) high statistic correlation between the method used and NLS method (correlation coefficients: 0.941 for Ktrans and 0.881 for ve), and (6) the potential clinical usefulness of the new method.