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

ABSTRACT 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 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 C 1 and C 2 and their product, C 1 C 2 , were compared to the image defined on maximum b-value (DWI max ), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app ). Ability to discriminate between cancer and healthy breast tissues was assessed by the false positive rate given sensitivity of 80% (FPR 80 ) and receiver operating characteristic (ROC) area under the curve (AUC). Results Mean FPR 80 for both datasets was 0.016 (95%CI=0.008–0.024) for C 1 C 2 , 0.136 (95%CI=0.092–0.180) for C 1 , 0.068 (95%CI=0.049–0.087) for C 2 , 0.159 (95%CI=0.114–0.204) for DWI max , 0.731 (95%CI=0.692–0.770) for ADC and 0.684 (95%CI=0.660–0.709) for K app . Mean ROC AUC for C 1 C 2 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); removing the need to administer Gadolinium contrast decreases scan time and any accumulation of Gadolinium in the brain.