Discrimination of breast cancer from healthy breast tissue using a three-component diffusion-weighted MRI model.

Purpose: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between pre-defined 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 voxel-wise 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: Pathology-proven breast cancer patients 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 to 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 receiver operating characteristic (ROC) area under the curve (AUC). Results: Mean FPR80 for both datasets was 0.016 (95%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 modelyields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating pre-defining lesions. This novel DW-MRI method may serve as non-contrast alternative to standard-of-care dynamic contrast-enhanced MRI (DCE-MRI).