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. 2016 Feb;9(1):8-17.
doi: 10.1016/j.tranon.2015.11.016. Epub 2016 Jan 23.

Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI

Affiliations

Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI

Alina Tudorica et al. Transl Oncol. 2016 Feb.

Abstract

The purpose is to compare quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT) and evaluation of residual cancer burden (RCB). Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed with the standard Tofts and Shutter-Speed models (TM and SSM). After one NACT cycle the percent changes of DCE-MRI parameters K(trans) (contrast agent plasma/interstitium transfer rate constant), ve (extravascular and extracellular volume fraction), kep (intravasation rate constant), and SSM-unique τi (mean intracellular water lifetime) are good to excellent early predictors of pathologic complete response (pCR) vs. non-pCR, with univariate logistic regression C statistics value in the range of 0.804 to 0.967. ve values after one cycle and at NACT midpoint are also good predictors of response, with C ranging 0.845 to 0.897. However, RECIST LD changes are poor predictors with C = 0.609 and 0.673, respectively. Post-NACT K(trans), τi, and RECIST LD show statistically significant (P < .05) correlations with RCB. The performances of TM and SSM analyses for early prediction of response and RCB evaluation are comparable. In conclusion, quantitative DCE-MRI parameters are superior to imaging tumor size for early prediction of therapy response. Both TM and SSM analyses are effective for therapy response evaluation. However, the τi parameter derived only with SSM analysis allows the unique opportunity to potentially quantify therapy-induced changes in tumor energetic metabolism.

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Figures

Figure 1
Figure 1
Column graphs of the (A) mean V21% change values of RECIST LD and several DCE-MRI metrics (Ktrans, ve, kep, and τi, estimated from the TM and SSM pharmacokinetic analyses) and (B) mean V2 and V3 ve values (TM and SSM) for the pCR (black column) and non-pCR (gray column) patient groups. The error bar represents the standard deviation (SD). V21%: percent change of MRI metric at visit 2 (V2, after one NACT cycle) relative to visit 1 (V1, pre-NACT); V3: visit 3, midpoint of NACT.
Figure 2
Figure 2
V1 (pre-NACT) and V2 (after one NACT cycle) color parametric maps of Ktrans(SSM), ve(SSM), and τi from a pCR (A, left breast, patient 12) and a non-pCR (B, right breast, patient 3) breast tumor. The maps were generated for tumor ROIs defined on multiple image slices, and the ones on the image slice through the central portion of the tumor are displayed here. For each tumor, the color scale of each DCE-MRI metric is kept the same between the two visits for easy visualization of NACT-induced changes.
Figure 3
Figure 3
Scatter plots of pathologically measured RCB and in-breast RCB index values (from post-NACT resection specimens) against post-NACT (V4) MRI metrics: (A) RECIST LD, (B) Ktrans(SSM), and (C) τi. The straight line in each panel represents a linear regression. The Spearman correlation coefficient R and P values for the three imaging metrics are listed in Table 3B and shown in each panel. Note the inverse relationship between RCB (and in-breast RCB) and τi. Imaging results are missing from a pCR patient (patient 20), who declined the V4 MRI study due to personal reasons.

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