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.
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 trans), extravascular extracellular volume fraction (v e), blood plasma volume fraction (v p), and the efflux rate constant (k ep = K trans/v e) 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 k ep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC.
The k ep/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 k ep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between k ep/ADC and k ep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018–0.23), whereas the AUCs between k ep/ADC and ADC trended toward significance (−0.11 to 0.24).
The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.
From the *Institute of Imaging Science, †Department of Radiology and Radiological Sciences, ‡the Vanderbilt-Ingram Cancer Center, Departments of §Biostatistics, ∥Radiation Oncology, ¶Medical Oncology, #Surgical Oncology, **Pathology, ††Physics and Astronomy, ‡‡Cancer Biology, and §§Biomedical Engineering, Vanderbilt University, Nashville, TN.
Received for publication April 15, 2014; and accepted for publication, after revision, August 14, 2014.
Conflicts of interest and sources of funding: Supported by the National Institutes of Health through NCI R01CA138599, NCI 1U01CA142565, NCI 1U01CA174706, NCI 1P50 098131, NCI P30 CA068485, and NCRR/NIH UL1 RR024975-01 (Vanderbilt CTSA grant) as well as the Kleberg Foundation (support for the imaging program at our institution) and the AUR-GE Radiology Research Academic Fellowship.
The authors report no conflicts of interest.
Reprints: Thomas E. Yankeelov, PhD, Institute of Imaging Science, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Ave S, Nashville, TN 37232–2310. E-mail: email@example.com.