Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial

Abstract

Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy. © RSNA, 2018 Online supplemental material is available for this article.

Figure 1a:

(a) ACRIN 6698 trial schema and (b) study patient inclusion and exclusion flowchart. Exp = experimental, Tx = treatment.

Figure 1b:

(a) ACRIN 6698 trial schema and (b) study patient inclusion and exclusion flowchart. Exp = experimental, Tx = treatment.

Figure 2:

Serial diffusion-weighted (DW) MR images in a 54-year-old woman who underwent neoadjuvant treatment for grade III triple-negative (hormone receptor–negative/human epidermal growth factor receptor 2–negative) cancer and who experienced pathologic complete response. Imaging was performed with a 3.0-T MRI unit. Shown are axial postcontrast dynamic contrast-enhanced (DCE) MRI images (left), noncontrast DW MRI (b = 800 sec/mm²) images (center), and apparent diffusion coefficient (ADC) maps (right). At each time point, a whole-tumor region of interest (ROI) was defined across multiple sections at DW MRI, and mean ADC was calculated for all voxels in the composite ROI. The tumor manifested as a 4.2-cm mass at pretreatment DCE MRI (top), and the ROI was defined to avoid a central necrotic region. Serial ADC measures increased progressively with treatment, with ΔADC = 18% at early treatment/3 weeks, 28% at midtreatment/12 weeks, and 47% at posttreatment.

Figure 3:

Serial diffusion-weighted (DW) MR images in a 51-year-old woman who underwent neoadjuvant treatment for grade III hormone receptor–positive/human epidermal growth factor receptor 2–negative cancer and who had residual disease at surgery (and thus did not experience pathologic complete response). Imaging was performed with a 3.0-T MRI unit. Shown are axial postcontrast dynamic contrast-enhanced (DCE) MRI images (left), noncontrast DW MRI (b = 800 sec/mm²) images (center), and apparent diffusion coefficient (ADC) maps (right). At each time point, a whole-tumor region of interest (ROI) was defined across multiple sections at DW MRI, and mean ADC was calculated for all voxels in the composite ROI. The tumor appeared as a 7.5-cm mass at pretreatment DCE MRI (top). Serial ADC measures increased only slightly with treatment, with ΔADC = 9.5% at early treatment/3 weeks, 14% at midtreatment/12 weeks, and 29% at posttreatment.

Figure 4a:

Time course of tumor apparent diffusion coefficient (ADC) response during treatment. Plots show mean ADCs at pretreatment, early treatment (3 weeks), midtreatment (12 weeks) and posttreatment time points. Error bars = standard deviations. Shown are results for all 242 patients stratified by (a) pathologic complete response (pCR) versus non-pCR pathologic outcome and (b) cancer hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) subtype. Tx = treatment.

Figure 4b:

Time course of tumor apparent diffusion coefficient (ADC) response during treatment. Plots show mean ADCs at pretreatment, early treatment (3 weeks), midtreatment (12 weeks) and posttreatment time points. Error bars = standard deviations. Shown are results for all 242 patients stratified by (a) pathologic complete response (pCR) versus non-pCR pathologic outcome and (b) cancer hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) subtype. Tx = treatment.

Figure 5a:

Receiver operating characteristic curves for predicting pathologic complete response on the basis of percentage change in tumor apparent diffusion coefficient, stratified by tumor subtype. Curves and calculated areas under the curves are shown with 95% confidence intervals at (a) early treatment/3 weeks, (b) midtreatment/12 weeks, and (c) posttreatment/presurgery time points. HR = hormone receptor, HER2 = human epidermal growth factor receptor 2.

Figure 5b:

Receiver operating characteristic curves for predicting pathologic complete response on the basis of percentage change in tumor apparent diffusion coefficient, stratified by tumor subtype. Curves and calculated areas under the curves are shown with 95% confidence intervals at (a) early treatment/3 weeks, (b) midtreatment/12 weeks, and (c) posttreatment/presurgery time points. HR = hormone receptor, HER2 = human epidermal growth factor receptor 2.

Figure 5c:

Receiver operating characteristic curves for predicting pathologic complete response on the basis of percentage change in tumor apparent diffusion coefficient, stratified by tumor subtype. Curves and calculated areas under the curves are shown with 95% confidence intervals at (a) early treatment/3 weeks, (b) midtreatment/12 weeks, and (c) posttreatment/presurgery time points. HR = hormone receptor, HER2 = human epidermal growth factor receptor 2.

Figure 6:

Receiver operating characteristic curves for predictive models at midtreatment/12 weeks. Each model was tested in the same randomly selected group of 86 study patients whose data were not used for model training. Curves reflect the performance in predicting pathologic complete response, with area under the curve (AUC) and 95% confidence intervals given for each. Predictive models incorporating change in apparent diffusion coefficient (ΔADC) alone (solid line), ΔADC + hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) subtype (dashed line), and ΔADC + change in functional tumor volume (ΔFTV) + HR/HER2 subtype (dotted line) produced AUCs of 0.57, 0.72, and 0.71, respectively.