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

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. Patients with pathology-proven breast cancer from two datasets ( = 81 and = 25) underwent multi-b-value DW-MRI. The three-component signal contributions and and their product, , and signal fractions , , and were compared with the image defined on maximum b-value ( ), conventional apparent diffusion coefficient ( ), and apparent diffusion kurtosis ( ). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR ) and ROC AUC. Mean FPR for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for , 0.136 (95% CI, 0.092-0.180) for , 0.068 (95% CI, 0.049-0.087) for , 0.462 (95% CI, 0.425-0.499) for , 0.832 (95% CI, 0.797-0.868) for , 0.176 (95% CI, 0.150-0.203) for , 0.159 (95% CI, 0.114-0.204) for , 0.731 (95% CI, 0.692-0.770) for , and 0.684 (95% CI, 0.660-0.709) for . Mean ROC AUC for was 0.984 (95% CI, 0.977-0.991). The 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.