Discrimination of breast cancer from healthy breast tissues 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, the ability of DW-MRI to discriminate cancer tissue from all other breast tissues on a voxel-level in a clinical setting is unknown. Here we explore the ability to distinguish breast cancer from healthy breast tissues 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). Ability to discriminate between cancer and healthy breast tissues was assessed by the false positive rate given 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, 0.466 (95%CI=0.428-0.503) for F1F2, 0.823 (95%CI=0.784-0.861) for F1, 0.172 (95%CI=0.146-0.197) for F2, 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 three-component model yields a clinically useful discrimination between cancer and healthy breast tissues, superior to other DW-MRI methods and obliviating pre-defining lesions by radiologists. This novel DW-MRI method may serve as non-contrast alternative to standard-of-care dynamic contrast-enhanced MRI (DCE-MRI).