{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T22:52:32Z","timestamp":1788821552599,"version":"build-2803163510"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>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<jats:sub>1<\/jats:sub> mapping, variable flip angle (VFA) measurements of native tissue R<jats:sub>1<\/jats:sub> (R<jats:sub>1,0<\/jats:sub>), 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)- <jats:italic>vs<\/jats:italic>. voxel-based analyses in combination with the use of VFA-measured R<jats:sub>1,0<\/jats:sub><jats:italic>vs<\/jats:italic>. fixed, literature-reported R<jats:sub>1,0<\/jats:sub> 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<jats:sub>1,0<\/jats:sub> was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced \u2265100% overestimation of R<jats:sub>1,0<\/jats:sub>. 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) <jats:italic>vs.<\/jats:italic> non-pCR after the first NAC cycle, whereas K<jats:sup>trans<\/jats:sup> 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<jats:sup>trans<\/jats:sup> and k<jats:sub>ep<\/jats:sub> were excellent predictors of response at the NAC midpoint with ROC AUC &amp;gt;0.90, while the SSM parameters (AUC \u22650.80) performed better than their TM counterparts (AUC &amp;lt;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<jats:sub>1,0<\/jats:sub> may mitigate random errors from R<jats:sub>1,0<\/jats:sub> measurements across platforms and potentially eliminate the need for B<jats:sub>1<\/jats:sub> and VFA acquisitions in MC and MP trials.<\/jats:p>","DOI":"10.3389\/fonc.2024.1395502","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T05:13:58Z","timestamp":1732857238000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Initial experience in implementing quantitative DCE-MRI to predict breast cancer therapy response in a multi-center and multi-vendor platform 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