Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

Purpose: Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model. Experimental Design: Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C1 and C2 and their product, C1C2, and signal fractions F1, F2, and F1F2 were compared with the image defined on maximum b-value (DWImax), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (Kapp). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC. Results: Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008–0.024] for C1C2, 0.136 (95% CI, 0.092–0.180) for C1, 0.068 (95% CI, 0.049–0.087) for C2, 0.462 (95% CI, 0.425–0.499) for F1F2, 0.832 (95% CI, 0.797–0.868) for F1, 0.176 (95% CI, 0.150–0.203) for F2, 0.159 (95% CI, 0.114–0.204) for DWImax, 0.731 (95% CI, 0.692–0.770) for ADC, and 0.684 (95% CI, 0.660–0.709) for Kapp. Mean ROC AUC for C1C2 was 0.984 (95% CI, 0.977–0.991). Conclusions: The C1C2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.