Background The change in apparent diffusion coefficient (ADC) measured from diffusion‐weighted imaging (DWI) has been shown to be predictive of pathologic complete response (pCR) for patients with locally invasive breast cancer undergoing neoadjuvant chemotherapy. Purpose To investigate the additive value of tumor ADC in a multicenter clinical trial setting. Study Type Retrospective analysis of multicenter prospective data. Population In all, 415 patients who enrolled in the I‐SPY 2 TRIAL from 2010 to 2014 were included. Field Strength/Sequence 1.5T or 3T MRI system using a fat‐suppressed single‐shot echo planar imaging sequence with b‐values of 0 and 800 s/mm2 for DWI, followed by a T1‐weighted sequence for dynamic contrast‐enhanced MRI (DCE‐MRI) performed at pre‐NAC (T0), after 3 weeks of NAC (T1), mid‐NAC (T2), and post‐NAC (T3). Assessment Functional tumor volume and tumor ADC were measured at each MRI exam; pCR measured at surgery was assessed as the binary outcome. Breast cancer subtype was defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. Statistical Tests A logistic regression model was used to evaluate associations between MRI predictors with pCR. The cross‐validated area under the curve (AUC) was calculated to assess the predictive performance of the model with and without ADC. Results In all, 354 patients (128 HR+/HER2–, 60 HR+/HER2+, 34 HR–/HER2+, 132 HR–/HER2–) were included in the analysis. In the full cohort, adding ADC predictors increased the AUC from 0.76 to 0.78 at mid‐NAC and from 0.76 to 0.81 at post‐NAC. In HR/HER2 subtypes, the AUC increased from 0.52 to 0.65 at pre‐NAC for HR+/HER2–, from 0.67 to 0.73 at mid‐NAC and from 0.72 to 0.76 at post‐NAC for HR+/HER2+, from 0.71 to 0.81 at post‐NAC for triple negatives. Data Conclusion The addition of ADC to standard functional tumor volume MRI showed improvement in the prediction of treatment response in HR+ and triple‐negative breast cancer. Level of Evidence: 2 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2019;50:1742–1753.
Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.
We investigated whether serial measurements of circulating tumor DNA (ctDNA) and functional tumor volume (FTV) by magnetic resonance imaging (MRI) can be combined to improve prediction of pathologic complete response (pCR) and estimation of recurrence risk in early breast cancer patients treated with neoadjuvant chemotherapy (NAC). We examined correlations between ctDNA and FTV, evaluated the additive value of ctDNA to FTV-based predictors of pCR using area under the curve (AUC) analysis, and analyzed the impact of FTV and ctDNA on distant recurrence-free survival (DRFS) using Cox regressions. The levels of ctDNA (mean tumor molecules/mL plasma) were significantly correlated with FTV at all time points (p < 0.05). Median FTV in ctDNA-positive patients was significantly higher compared to those who were ctDNA-negative (p < 0.05). FTV and ctDNA trajectories in individual patients showed a general decrease during NAC. Exploratory analysis showed that adding ctDNA information early during treatment to FTV-based predictors resulted in numerical but not statistically significant improvements in performance for pCR prediction (e.g., AUC 0.59 vs. 0.69, p = 0.25). In contrast, ctDNA-positivity after NAC provided significant additive value to FTV in identifying patients with increased risk of metastatic recurrence and death (p = 0.004). In this pilot study, we demonstrate that ctDNA and FTV were correlated measures of tumor burden. Our preliminary findings based on a limited cohort suggest that ctDNA at surgery improves FTV as a predictor of metastatic recurrence and death. Validation in larger studies is warranted.
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