Assessing Detection, Discrimination, and Risk of Breast Cancer According to Anisotropy Parameters of Diffusion Tensor Imaging

BACKGROUND: The aim of this study was to investigate whether the anisotropy parameters are helpful in the detection and discrimination of breast cancers, and to determine its value in predicting the risk of cancers. MATERIAL/METHODS: There were 56 patients with 56 lesions (34 malignant, 22 benign) included in the study. DTI was performed in every patient and apparent diffusion coefficient (ADC), fractional anisotropy (FA), and eigenvalues E1, E2, and E3 were measured in every lesion and the normal breast tissue. RESULTS: ADC, FA, and eigenvalues of E1, E2, E3, and E1–E3 in breast cancers were all significantly lower than in normal tissue (P<0.001 for all) with mean reduction of (32±17)%, (24±13)%, (33±19)%, (32±17)%, (31±18)%, and (37±20)% for ADC, FA, E1, E2, E3, and E1–E3, respectively. These parameters were also statistically lower in cancers than in benign lesions (P<0.01 for all), except FA (P>0.05). ADC, E1, E2, and E3 were very similar in discriminating breast cancers and benign lesions, with area under the curve (AUC) 0.885–0.898, sensitivity 73.5–85.3%, and specificity 90.9–100%. CONCLUSIONS: ADC, E1, E2, E3, and E1–E3 are much lower in breast cancers than in normal tissue and benign lesions. The reduction of ADC, E1, E2, E3, and E1–E3 of a mass in the breast is highly associated with the risk of breast cancer, but the FA has no utility in breast cancer risk prediction.