• Can Onaygil, Handan Kaya, Mustafa Umit Ugurlu, and Erkin Aribal.
• Oberlausitz-Kliniken gGmbH, Institute of Diagnostic and Interventional Radiology, Bautzen, Germany.
• J Magn Reson Imaging. 2017 Mar 1; 45 (3): 660-672.

PurposeTo evaluate the diagnostic performances of the diffusion tensor imaging (DTI) parameters in the diagnosis of breast cancer and to investigate the variations in DTI parameters according to the breast cancer biomarkers.Materials And MethodsAt 3.0 Tesla (T), DTI was performed in 85 patients with 92 enhancing breast lesions. λ1 , λ2 , λ3 , mean diffusivity (MD), radial diffusivity (RD), fractional anisotropy (FA), relative anisotropy (RA), and geodesic anisotropy (GA) were studied and compared with diffusion-weighted imaging-derived apparent diffusion coefficient. Lesions were analyzed according to BIRADS lexicon. Logistic regression models were constructed to determine the contribution of DTI to the specificity and the accuracy of DCE-MRI. Breast cancer biomarkers; estrogen receptor (ER), HER-2 status, and Ki-67 were correlated with DTI in malignant cases.ResultsMalignant lesions exhibited significantly lower MD, RD, λ1 , λ2 , λ3 and higher FA, RA, GA values (P <  0.001). Logistic regression models showed that MD, RD, λ1 , λ2 , λ3 , FA, and RA increase the specificity of the DCE-MRI (from 83.0% to 89.4-93.6%; P <  0.05). Higher RD, λ2 , λ3 and lower FA, RA, and GA values were observed in ER-negative breast cancer (P <  0.05). Ki-67 showed significant, negative correlation with FA, RA, GA, λ1 -λ3 and λ1 -λ2 (r = -0.336 to -0.435; P <  0.05).ConclusionBesides its ability to differentiate malignant breast lesions, DTI improves the specificity of conventional 3.0T breast MRI and shows correlation with biomarkers ER and Ki-67.Level Of Evidence1 J. Magn. Reson. Imaging 2017;45:660-672.© 2016 International Society for Magnetic Resonance in Medicine.

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