Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer

Abstract

Objectives: The purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer.

Materials and methods: Patients with stage II/III breast cancer were enrolled in an institutional review board-approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC.

Results: The kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018-0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (-0.11 to 0.24).

Conclusions: The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

Figure 1

The averaged post-contrast MR images with K^trans, k_ep, v_e, v_p, and ADC maps superimposed at three time points (three columns) for one pathological complete responder. The numbers under panels are the mean values for K^trans, k_ep, v_e, v_p, and ADC at different time points. For this complete responder, both the mean K^trans and the mean k_ep were decreased after one cycle of therapy, while the mean values of v_e, v_p, and ADC were increased.

Figure 2

The averaged post-contrast MR images with K^trans, k_ep, v_e, v_p, and ADC maps superimposed at three time points (three columns) for one non-pCR. The numbers under panels are the mean values for K^trans, k_ep, v_e, v_p, and ADC at different time points.

Figure 3

ROC for k_ep (dotted line), ADC (dashed line), and k_ep/ADC (solid line). k_ep and ADC alone yielded an AUC of 0.77 and 0.81 with the optimal cut-off point of 0.28 min⁻¹ and 1.4 mm²/s × 10⁻³ (marked as a triangle and a square), respectively, while k_ep/ADC had an AUC of 0.86 with the optimal cut-off point of 3.32 1/mm² (marked as a dot). The sensitivity, specificity, and PPV at the cut-off points are 0.83, 0.65, and 0.59 for k_ep, 0.83, 0.67, and 0.59 for ADC, and 0.92, 0.75, and 0.69 for k_ep/ADC, respectively.

Figure 4

Boxplots of k_ep (left panel), ADC (middle panel), and k_ep/ADC (right panel) at t₂ for non-pCRs and pCRs. The central marks show the median and the edges of the box are the 25th and 75th percentiles. Outliers are not shown in the figure. The medians of k_ep for non-pCRs and pCRs were 0.32 min⁻¹ and 0.23 min⁻¹, respectively, while they were 1.24 mm²/s × 10⁻³ and 1.59 mm²/s × 10⁻³ for ADC, and 4.27 1/mm² and 2.63 1/mm² for k_ep/ADC, respectively.

Figure 5

The figure displays density distributions of the AUC differences between k_ep/ADC and k_ep (dotted line), and between k_ep/ADC and ADC (solid line) after one cycle of NAC. The 95% CIs of the AUC differences between k_ep/ADC and k_ep were (0.0062, 0.20), while the 95% CIs of the AUC differences between k_ep/ADC and ADC were (−0.12, 0.24). The areas outside the 95% CIs are shadowed for both distributions.