Background: The apparent diffusion coefficient (ADC) presents a biomarker that is sensitive to tumor cellularity. ADC maps can be calculated from non-contrast diffusion-weighted magnetic resonance imaging (DW-MRI) measurements. ACRIN 6698, a sub-study of clinical trial I-SPY 2, investigated mean ADC – averaged over the whole tumor – as a marker to predict pathologic complete response (pCR) . This work compares a group of histogram-based ADC metrics in addition to mean ADC for early prediction of pCR in patients stratified by breast cancer subtype.
Methods: We performed a retrospective analysis of DW-MRI, dynamic-contrast enhanced (DCE) MRI, and clinical outcome (i.e., pCR at surgery) in a cohort of 79 female patients who were diagnosed with high-risk, stage II/III breast cancer. Patients underwent neoadjuvant chemotherapy (NAC) with paclitaxel (12 weeks), followed by doxorubicin plus cyclophosphamide (12 weeks). The included population represents a subset of the I-SPY 2 cohort and comprises 48 patients with hormone receptor [HR]+/HER2-, and 31 patients with HR-/HER2-. DW- and DCE-MRI acquisitions were performed according to the I-SPY 2 protocol at pretreatment (T0) and after three weeks (T1) and were analyzed to find early treatment percentage (%) change (T0 to T1) in any metric M; where %-change = 100 × (M(T1) – M(T0))/M(T0). Histogram analysis provided nine region-of-interest (ROI)-based ADC metrics (Table 1). ROIs were manually delineated by expert observers in three-dimensional ADC maps, focusing on diffusion-restricted regions . DCE-MRI was analyzed for the integral I-SPY 2 imaging marker of %-change in functional tumor volume (FTV) between T0 and T1. Statistical analysis compared the predictive power of ADC metrics and FTV, including: the receiver-operating-characteristic (ROC) curve from a logistic regression model to predict pCR as ‘positive’, area-under-the-curve (AUC) assessment, and rank-sum Wilcoxon test (p < 0.05: statistically significant).
Results: (Table 1): 16 out of 79 (20.3%) patients reached pCR at surgery, with 18.8% pCR among HR+/HER- and 22.6% among HR-/HER2- groups. For all nine computed ADC statistics (listed as median [Q1, Q3], across all patients), %-change was higher in patients who reached pCR than patients with non-pCR (highest value for metric ‘MIN’: 23.9% [-0.9%, 52.5] vs. 16.6% [0.4%, 27.6%], though without statistical significance: p=0.237). Likewise, %-change of FTV was also stronger in pCR patients than non-pCR patients (-58.8% [-80.6%, -22.5%] vs. -28.2% [54.2%, -2.7%], with statistical significance: p=0.036). For all patients combined (n=79), among the various reported ADC metrics, %-change in ‘PCTL_95’ (95th percentile of histogram) yielded the highest AUC (0.7; 95% CI = [0.56, 0.83]; p=0.012). %-change in FTV showed the second highest AUC (0.67; 95% CI = [0.52, 0.82]; p=0.036). By subtype, AUC was highest for %-change of ‘PCTL_95’ (0.69; 95% CI = [0.5, 0.87]; p=0.072) in the HR+/HER2- subgroup; and highest for both %-change of ‘MEAN’ (AUC = 0.73; 95% CI = [0.49, 0.94]; p=0.065) and ‘PCTL_75’ (AUC = 0.73; 95% CI = [0.49, 0.94]; p=0.073) triple negative (HR-/HER2-) subgroup. By comparison, %-change of FTV yielded AUCs of 0.64 (95% CI = [0.41, 0.85]; p=0.191) and 0.71 (95% CI = [0.51, 0.9]; p=0.098) in the HR+/HER2- and triple-negative subgroups, respectively.
Conclusion: Various tumor ADC metrics from non-contrast DW-MRI demonstrate potential biomarkers for assessing responsiveness to NAC at an early treatment timepoint. ADC may have predictive performance that is comparable to FTV, depending on the breast cancer subtype. Observations for %-change in ‘MEAN’ ADC at T1 differed from previous reports , which may be explained by the small sample size and single (paclitaxel) drug arm. Additional studies are warranted to include patients of experimental arms and of HER2+ subtypes.
 Partridge et al., Radiology 289(3):618-27 (2018)
 Nu et al., Tomography 8: 1208-20 (2022)