Initial experience in implementing quantitative DCE-MRI to predict breast cancer therapy response in a multi-center and multi-vendor platform setting.

Quantitative dynamic contrast-enhanced (DCE) MRI as a promising method for the prediction of breast cancer response to neoadjuvant chemotherapy (NAC) has been demonstrated mostly in single-center and single-vendor platform studies. This preliminary study reports the initial experience in implementing quantitative breast DCE-MRI in multi-center (MC) and multi-vendor platform (MP) settings to predict NAC response. MRI data, including B mapping, variable flip angle (VFA) measurements of native tissue R (R), and DCE-MRI, were acquired during NAC at three sites using 3T systems with Siemens, Philips, and GE platforms, respectively. High spatiotemporal resolution DCE-MRI was performed using similar vendor product sequences with k-space undersampling during acquisition and view sharing during reconstruction. A breast phantom was used for quality assurance/quality control (QA/QC) across sites. The Tofts model (TM) and shutter-speed model (SSM) were used for pharmacokinetic (PK) analysis of the DCE data. Additionally, tumor region of interest (ROI)- . voxel-based analyses in combination with the use of VFA-measured R . fixed, literature-reported R were investigated to determine the optimal analysis approach. Results from 15 patients who completed the study are reported. Voxel-based PK analysis using fixed R was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced ≥100% overestimation of R. The semi-quantitative signal enhancement ratio (SER) and quantitative PK parameters outperformed the tumor longest diameter (LD) in the prediction of pathologic complete response (pCR) non-pCR after the first NAC cycle, whereas K consistently provided more accurate predictions than both SER and LD after the first NAC cycle and at the NAC midpoint. Both TM and SSM K and k were excellent predictors of response at the NAC midpoint with ROC AUC >0.90, while the SSM parameters (AUC ≥0.80) performed better than their TM counterparts (AUC <0.80) after the first NAC cycle. The initial experience of this ongoing study indicates the importance of QA/QC using a phantom and suggests that deploying voxel-based PK analysis using a fixed R may mitigate random errors from R measurements across platforms and potentially eliminate the need for B and VFA acquisitions in MC and MP trials.