Diagnostic performance of quantitative diffusion tensor imaging for the differentiation of breast lesions at 3 T MRI


      • Conventional breast magnetic resonance imaging (MRI) may lead to ambiguous diagnosis and unnecessary biopsies.
      • Quantitative Diffusion Tensor Imaging (DTI) may aid the discrimination between benign and malignant breast lesions at 3T MRI.
      • DTI features were calculated and compared between benign and malignant breast lesions using two different software packages.
      • Quantitative DTI can be considered a feasible addition to conventional breast MRI as an adjunct tool in the clinical routine.



      Conventional breast magnetic resonance imaging (MRI), including dynamic contrast-enhanced MR mammography, may lead to ambiguous diagnosis and unnecessary biopsies.


      To investigate the contribution of quantitative diffusion tensor imaging (DTI) in the discrimination between benign and malignant breast lesions at 3 T MRI.

      Material and methods

      The study included a total of 86 lesions (44 benign and 42 malignant) in 58 women (34 with malignant lesions, 23 with benign lesions and 1 with both types of lesions). All patients were examined on a 3 T MRI scanner. Fractional Anisotropy (FA), Mean Diffusivity (MD), Apparent Diffusion Coefficient (ADC), as well as eigenvalues (λ1, λ2, λ3) were calculated and compared between benign and malignant lesions using two different software packages (GE Functool and ExploreDTI).


      Malignant lesions exhibited significantly lower ADC values compared to benign ones (ADCmal = 1.06 × 10−3 mm2/s, ADCben = 1.54 × 10−3 mm2/s, p-value < 0.0001). FA measurements in carcinomas indicated slightly higher values than those in benign lesions (FAmal = 0.20 ± 0.07, FAben = 0.15 ± 0.05, p-value = 0.0003). Eigenvalues λ1, λ2, λ3, showed significantly lower values in malignant tumors compared to benign lesions and normal breast tissue. ROC curve analysis of ADC and DTI metrics demonstrated that ADC provides high diagnostic performance (AUC = 0.944) while, MD and λ1 showed best discriminative results (AUC = 0.906) for the differentiation of malignant and benign lesions in contrast to other DTI parameters.


      The addition of eigenvalue analysis improves DTI's ability to differentiate between benign and malignant breast lesions.


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