Abstract No. 
2012 San Antonio Breast Cancer Symposium
December 4-8

Quantitative DCE-MRI to predict the response of primary breast cancer to neoadjuvant therapy

Li X, Arlinghaus LR, Chakravarthy AB, Abramson RG, Abramson VG, Farley J, Ayers GD, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders ME, Yankeelov, T E,

The goal of this study was to determine if quantitative changes in dynamic contrast enhanced MRI (DCE-MRI) following a single cycle of chemotherapy can be used to separate pathologic responders (i.e., no residual tumor in the breast at surgery) from pathologic non-responders.

28 patients with Stage II/III breast cancer were enrolled in an IRB-approved clinical trial where breast MRI scans were acquired before (t1) and after one cycle of therapy (t2). Imaging was performed on a 3.0T MR scanner (Philips Healthcare, The Netherlands) and employed a 3D spoiled gradient echo sequence with a spatial resolution of 6.6 mm3 and a temporal resolution of 16 seconds collected at 25 time points before and after the intravenous injection of 0.1 mmol/kg of gadopentetate dimeglumine (Magnevist, Wayne, NJ). At surgery, 12 patients were responders while 16 patients were non-responders.

Both semi-quantitative and quantitative analyses were used to summarize the DCE-MRI data. The semi-quantitative parameters were the signal enhancement rate (SER [1]) and tumor volume (TV). Three pharmacokinetic models, the Tofts-Kety (TK), the Extended Tofts-Kety (ETK), and the fast exchange regime (FXR), were used to estimate the following quantitative parameters: the volume transfer constant (Ktrans), efflux rate constant (kep), vascular volume (vp), and the extravascular extracellular volume fraction (ve) [2]. Each parameter was summarized in two ways: 1) the change in mean from t1 to t2, and 2) the mean at t2. Receiver operating characteristic (ROC) analysis was then performed to determine the ability of each parameter to predict treatment response.

The table displays the areas under the ROC curves (AUC) for each parameter. For the early change in parameters, the AUC for TV and SER were 0.48 and 0.66, respectively. The best AUC of the quantitative parameters was 0.73 from kep estimated by the ETK model. The sensitivities/specificities for TV, SER, and kep for predicting pathologic response were 88%/33%, 64%/79%, and 56%/92%, respectively. For the mean parameter values at t2, the AUCs of TV, SER, and kep were 0.50, 0.56, and 0.79, with sensitivities/specificities for predicting pathologic response of 63%/50%, 93%/21%, and 81%/75%, respectively.

Our results can be interpreted in light of the ACRIN 6657/I-SPY trial [3] which found that change in TV and SER at an early time point were the most predictive of response with AUCs of 0.72 and 0.71, respectively. Our preliminary results, especially our AUC of 0.79 for kep at t2, suggest that a more quantitative analysis of higher temporal resolution DCE-MRI data may achieve comparable or even superior results. Our ongoing efforts involve combining multiple parameters in a multivariate analysis with apparent diffusion coefficient data from diffusion weighted MRI.

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