Background parenchymal enhancement (BPE) describes the natural phenomenon observed on breast MRI in which normal breast tissue demonstrates signal enhancement from uptake of intravenous contrast. BPE may provide independent and additive value for prediction of pathologic complete response (pCR) using MRI measured functional tumor volume (FTV), which has only moderate discrimination (FTV AUC ~ 0.7). We evaluated the additive value of quantitative whole breast BPE to a FTV model for prediction of pCR to neoadjuvant chemotherapy in the ISPY-2 trial.
In this HIPAA-compliant/IRB-approved study, women 18 years of age and older diagnosed with stage II or III breast cancer and with tumor size measured ≥ 2.5 cm were eligible to enroll in the I-SPY 2 TRIAL. Participants received a weekly dose of paclitaxel alone (control) or in combination with Veliparib and Carboplatin for 12 weekly cycles followed by four (every 2-3 weeks) cycles of anthracycline-cyclophosphamide prior to surgery. All breast cancers in these drug arms were Her2 negative. MRI was performed before the initiation of neoadjuvant therapy or “baseline” (T0), after three weeks of therapy or “early treatment” (T1), after twelve weeks between drug regimens or “inter-regimen” (T2), and after neoadjuvant therapy completion and prior to surgery or “pre-surgery” (T3). MRI segmentation was manually performed of the whole unaffected contralateral breast, and tissue classification was performed using fuzzy c-means clustering. BPE was calculated as the average enhancement of all tissue voxels at the ﬁrst postcontrast acquisition. Predictor variables were parameterized as absolute values of BPE/FTV for each treatment time point or relative change values for each treatment time period. Logistic regression, stratified by hormone receptor (HR) subtype, was performed using 1) univariate models of FTV/BPE predictors alone and 2) multivariate models using all possible combinations of FTV/BPE predictors and HR status. Additive benefit for multivariate models was evaluated by estimating change in the area under the curve (AUC) for overall diagnostic performance with 10-repeat 5-fold cross validation. The 95% confidence interval (CI) of cross-validated AUC was estimated using 1,000 bootstrap resamples.
A total of 88 patients (29 pCR, 59 non-pCR) were evaluated with serial breast MRIs to assess neoadjuvant response. Among univariate models, women with HR+ cancers who had PCR demonstrated a significantly greater decrease in BPE from baseline to pre-surgery compared to non-PCR (OR = 0.64, 95% CI = 0.39-0.92, p-value = 0.04). The associated AUC was 0.77 (95% CI 0.56-0.98), comparable to the range of univariate FTV AUC values (0.57-0.87). Among optimized multivariate models, the highest cross-validated AUC for FTV and HR predictors was 0.81 (95% CI 0.73-0.90), while adding BPE slightly increased AUC to 0.82 (95% CI 0.74-0.92).
Changes in BPE in response to neoadjuvant therapy, which represents normal breast tissue changes measurable on any breast MRI, demonstrated significant association with pCR in women with HR+ breast cancer. Moreover, it had a similar diagnostic performance to univariate prediction with tumor volume. However, additive prediction of BPE to multivariate FTV models was only marginal.