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

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.

Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve pathologic complete response (pCR) at the time of surgery. Thirty-three patients underwent DCE-MRI (to estimate K (trans), v e, k ep, and v p) and DW-MRI [to estimate the apparent diffusion coefficient (ADC)] at baseline (t 1) and after the first cycle of neoadjuvant chemotherapy (t 2). Four analyses were performed and evaluated using receiver-operating characteristic (ROC) analysis to test their ability to predict pCR. First, a region of interest (ROI) level analysis input the mean K (trans), v e, k ep, v p, and ADC into the logistic model. Second, a voxel-based analysis was performed in which a longitudinal registration algorithm aligned serial parameters to a common space for each patient. The voxels with an increase in k ep, K (trans), and v p or a decrease in ADC or v e were then detected and input into the regression model. In the third analysis, both the ROI and voxel level data were included in the regression model. In the fourth analysis, the ROI and voxel level data were combined with selected clinical data in the regression model. The overfitting-corrected area under the ROC curve (AUC) with 95% confidence intervals (CIs) was then calculated to evaluate the performance of the four analyses. The combination of k ep, ADC ROI, and voxel level data achieved the best AUC (95% CI) of 0.87 (0.77-0.98).

DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings.

PURPOSE: The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response. METHODS: Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semiquantitative parameters included longest dimension, tumor volume, initial area under the curve, and signal enhancement ratio related parameters, while quantitative parameters included K(trans), v(e), k(ep), v(p), and τ(i) estimated using the standard Tofts-Kety, extended Tofts-Kety, and fast exchange regime models. RESULTS: Our preliminary results indicated that the signal enhancement ratio washout volume and k(ep) were significantly different between pathologic complete responders from nonresponders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic analysis showed that the AUC of the signal enhancement ratio washout volume was 0.75, and the AUCs of k(ep) estimated by three models were 0.78, 0.76, and 0.73, respectively. CONCLUSION: In summary, the signal enhancement ratio washout volume and k(ep) appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.

A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy.

There is currently a paucity of reliable techniques for predicting the response of breast tumors to neoadjuvant chemotherapy. The standard approach is to monitor gross changes in tumor size as measured by physical exam and/or conventional imaging, but these methods generally do not show whether a tumor is responding until the patient has received many treatment cycles. One promising approach to address this clinical need is to integrate quantitative in vivo imaging data into biomathematical models of tumor growth in order to predict eventual response based on early measurements during therapy. In this work, we illustrate a novel biomechanical mathematical modeling approach in which contrast enhanced and diffusion weighted magnetic resonance imaging data acquired before and after the first cycle of neoadjuvant therapy are used to calibrate a patient-specific response model which subsequently is used to predict patient outcome at the conclusion of therapy. We present a modification of the reaction-diffusion tumor growth model whereby mechanical coupling to the surrounding tissue stiffness is incorporated via restricted cell diffusion. We use simulations and experimental data to illustrate how incorporating tissue mechanical properties leads to qualitatively and quantitatively different tumor growth patterns than when such properties are ignored. We apply the approach to patient data in a preliminary dataset of eight patients exhibiting a varying degree of responsiveness to neoadjuvant therapy, and we show that the mechanically coupled reaction-diffusion tumor growth model, when projected forward, more accurately predicts residual tumor burden at the conclusion of therapy than the non-mechanically coupled model. The mechanically coupled model predictions exhibit a significant correlation with data observations (PCC = 0.84, p < 0.01), and show a statistically significant >4 fold reduction in model/data error (p = 0.02) as compared to the non-mechanically coupled model.

An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results.

BACKGROUND: By providing estimates of tumor glucose metabolism, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) can potentially characterize the response of breast tumors to treatment. To assess therapy response, serial measurements of FDG-PET parameters (derived from static and/or dynamic images) can be obtained at different time points during the course of treatment. However, most studies track the changes in average parameter values obtained from the whole tumor, thereby discarding all spatial information manifested in tumor heterogeneity. Here, we propose a method whereby serially acquired FDG-PET breast data sets can be spatially co-registered to enable the spatial comparison of parameter maps at the voxel level. METHODS: The goal is to optimally register normal tissues while simultaneously preventing tumor distortion. In order to accomplish this, we constructed a PET support device to enable PET/CT imaging of the breasts of ten patients in the prone position and applied a mutual information-based rigid body registration followed by a non-rigid registration. The non-rigid registration algorithm extended the adaptive bases algorithm (ABA) by incorporating a tumor volume-preserving constraint, which computed the Jacobian determinant over the tumor regions as outlined on the PET/CT images, into the cost function. We tested this approach on ten breast cancer patients undergoing neoadjuvant chemotherapy. RESULTS: By both qualitative and quantitative evaluation, our constrained algorithm yielded significantly less tumor distortion than the unconstrained algorithm: considering the tumor volume determined from standard uptake value maps, the post-registration median tumor volume changes, and the 25th and 75th quantiles were 3.42% (0%, 13.39%) and 16.93% (9.21%, 49.93%) for the constrained and unconstrained algorithms, respectively (p = 0.002), while the bending energy (a measure of the smoothness of the deformation) was 0.0015 (0.0005, 0.012) and 0.017 (0.005, 0.044), respectively (p = 0.005). CONCLUSION: The results indicate that the constrained ABA algorithm can accurately align prone breast FDG-PET images acquired at different time points while keeping the tumor from being substantially compressed or distorted. TRIAL REGISTRATION: NCT00474604.

Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer.

By fitting dynamic contrast-enhanced MRI data to an appropriate pharmacokinetic model, quantitative physiological parameters can be estimated. In this study, we compare four different models by applying four statistical measures to assess their ability to describe dynamic contrast-enhanced MRI data obtained in 28 human breast cancer patient sets: the chi-square test (χ(2)), Durbin-Watson statistic, Akaike information criterion, and Bayesian information criterion. The pharmacokinetic models include the fast exchange limit model with (FXL_v(p)) and without (FXL) a plasma component, and the fast and slow exchange regime models (FXR and SXR, respectively). The results show that the FXL_v(p) and FXR models yielded the smallest χ(2) in 45.64 and 47.53% of the voxels, respectively; they also had the smallest number of voxels showing serial correlation with 0.71 and 2.33%, respectively. The Akaike information criterion indicated that the FXL_v(p) and FXR models were preferred in 42.84 and 46.59% of the voxels, respectively. The Bayesian information criterion also indicated the FXL_v(p) and FXR models were preferred in 39.39 and 45.25% of the voxels, respectively. Thus, these four metrics indicate that the FXL_v(p) and the FXR models provide the most complete statistical description of dynamic contrast-enhanced MRI time courses for the patients selected in this study.

A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer.

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population-based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIF(ind), and compute a population-averaged AIF, AIF(pop). The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for K(trans), v(p) and v(e) as estimated by AIF(ind) and AIF(pop) are 0.65, 0.74 and 0.31, respectively, based on the region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84 and 0.68 for K(trans), v(p) and v(e), respectively. This work indicates that K(trans) and v(p) show good agreement between AIF(pop) and AIF(ind) while there is a weak agreement on v(e).

Motion correction in diffusion-weighted MRI of the breast at 3T.

PURPOSE: To provide a quantitative assessment of motion and distortion correction of diffusion-weighted images (DWIs) of the breast and to evaluate the effects of registration on the mean apparent diffusion coefficient (mADC). MATERIALS AND METHODS: Eight datasets from four patients with breast cancer and eight datasets from six healthy controls were acquired on a 3T scanner. A 3D affine registration was used to align each set of images and principal component analysis was used to assess the results. Variance in tumor ADC measurements, tumor mADC values, and voxel-wise tumor mADC values were compared before and after registration for each patient. RESULTS: Image registration significantly (P = 0.008) improved image alignment for both groups and significantly (P < 0.001) reduced the variance across individual tumor ADC measurements. While misalignment led to potential under- and overestimation of mADC values for individual voxels, average tumor mADC values did not significantly change (P > 0.09) after registration. CONCLUSION: 3D affine registration improved the alignment of DWIs of the breast and reduced the variance between ADC measurements. Although the reduced variance did not significantly change tumor region-of-interest measures of mADC, it may have a significant impact on voxel-based analyses.