Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge

Abstract

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Figures

Figure 1

Two-dimensional image view of the arrays of Ktrans (A) and ve (B) values used to construct the simulated DRO DCE-MRI data without noise. Each color stripe represents one of six Ktrans (0.01, 0.02, 0.05, 0.1, 0.2, and 0.35 min-1) (A) or one of five ve (0.01, 0.05, 0.1, 0.2, and 0.5) (B) values. The numbers along the x- and y-axes represent pixel numbers in both panels. Combination of the two panels results in 30 squares with 10 x 10 pixels each, representing the possible 30 combinations of Ktrans and ve values for simulated DRO data.

Figure 2

Two-dimensional image view of the arrays of estimated Ktrans (A) and ve (B) values obtained by fitting the DRO data with the TM algorithm implemented at OHSU. Good agreements are seen between the estimated Ktrans (A) and simulated “true” Ktrans (Figure 1A) values except for in the lower left areas of the panel with high Ktrans and low ve combinations. There are no visible differences seen between the estimated ve (B) and simulated “true” ve (Figure 1B) values.

Figure 3

The estimated Ktrans values obtained by fitting the DRO data with the TM algorithm implemented at ISM are plotted against the simulated “true” Ktrans values at five simulated ve values. The straight line is the line of unity, representing perfect agreement between the estimated and simulated Ktrans values.

Figure 4

(A) Box plots of V1 (left column) and V2 (right column) mean tumor Ktrans, ve, kep, vp, and Ti values from the 10 patients with breast cancer. (B) Box plots of percentage changes (V2 relative to V1) of the five parameters. The central bar and diamond symbols represent the median and mean values, respectively. Ktrans, ve, and kep are obtainable with all three pharmacokinetic models: TM, ETM, and SSM. The box plots associated with the same model are grouped together: institution abbreviations are labeled in the V1 Ktrans panel, while the model abbreviations are labeled in the V2 Ktrans panel. The same labeling orders also apply to the V1 and V2 ve and kep panels in A and Ktrans, ve, and kep percentage change panels in B. With the models used in this study, the vp and Ti parameters can be derived only with the ETM and SSM, respectively. The institution labeling orders in V1 vp and Ti panels (A) apply to the V2 vp and Ti panels in A and vp and Ti percentage change panels in B, respectively.

Figure 4

(A) Box plots of V1 (left column) and V2 (right column) mean tumor Ktrans, ve, kep, vp, and Ti values from the 10 patients with breast cancer. (B) Box plots of percentage changes (V2 relative to V1) of the five parameters. The central bar and diamond symbols represent the median and mean values, respectively. Ktrans, ve, and kep are obtainable with all three pharmacokinetic models: TM, ETM, and SSM. The box plots associated with the same model are grouped together: institution abbreviations are labeled in the V1 Ktrans panel, while the model abbreviations are labeled in the V2 Ktrans panel. The same labeling orders also apply to the V1 and V2 ve and kep panels in A and Ktrans, ve, and kep percentage change panels in B. With the models used in this study, the vp and Ti parameters can be derived only with the ETM and SSM, respectively. The institution labeling orders in V1 vp and Ti panels (A) apply to the V2 vp and Ti panels in A and vp and Ti percentage change panels in B, respectively.

Figure 5

Column graph of mean wCV for Ktrans, ve, kep, vp, and Ti parameters at V1 (blue) and V2 (red) obtained with all 12 algorithms.

Figure 6

Box plots of mean tumor Ktrans, ve, and kep values obtained with algorithms based on the TM (six algorithms), ETM (four algorithms), and SSM (two algorithms) at V1 (left column) and V2 (right column). The central bar and diamond symbols represent the median and mean values, respectively. The TM, ETM, and SSM labeling orders in the V1 Ktrans plot apply to all the other plots.

Figure 7

Scatter plots of mean tumor Ktrans at V1 (A), V2 (B), and its percentage change (V21) (C) for three pCRs (black circles) and seven non-pCRs (red triangles). Each column represents results returned by one data analysis algorithm. The columns associated with the same kinetic model are grouped together (labeled in A), as explained in Figure 4.

Figure 8

V1 and V2 single slice tumor Ktrans parametric maps generated by all 12 algorithms for a non-pCR (A) and a pCR (B). The primary tumor was in the left breast of the non-pCR and the right breast of the pCR. The color Ktrans maps are overlaid on post-contrast or pre-contrast DCE images. Although the color scales for these maps are different between subjects and among algorithms, they are kept the same for V1 and V2 maps generated by the same algorithm for the same subject.