UC San Diego

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 C ₁ and C ₂ and their product, C ₁ C ₂, and signal fractions F ₁, F ₂, and F ₁ F ₂ were compared with the image defined on maximum b-value (DWI _max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K _app). 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 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, 0.462 (95% CI, 0.425-0.499) for F ₁ F ₂, 0.832 (95% CI, 0.797-0.868) for F ₁, 0.176 (95% CI, 0.150-0.203) for F 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 C ₁ C ₂ 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.