Diffusion tensor imaging for characterizing tumor microstructure and improving diagnostic performance on breast MRI: a prospective observational study

Abstract

Background Diffusion-weighted imaging (DWI) can increase breast MRI diagnostic specificity due to the tendency of malignancies to restrict diffusion. Diffusion tensor imaging (DTI) provides further information over conventional DWI regarding diffusion directionality and anisotropy. Our study evaluates DTI features of suspicious breast lesions detected on MRI to determine the added diagnostic value of DTI for breast imaging . Methods With IRB approval, we prospectively enrolled patients over a 3-year period who had suspicious (BI-RADS category 4 or 5) MRI-detected breast lesions with histopathological results. Patients underwent multiparametric 3 T MRI with dynamic contrast-enhanced (DCE) and DTI sequences. Clinical factors (age, menopausal status, breast density, clinical indication, background parenchymal enhancement) and DCE-MRI lesion parameters (size, type, presence of washout, BI-RADS category) were recorded prospectively by interpreting radiologists. DTI parameters (apparent diffusion coefficient [ADC], fractional anisotropy [FA], axial diffusivity [ λ 1 ], radial diffusivity [( λ 2 + λ 3 )/2], and empirical difference [ λ 1 − λ 3 ]) were measured retrospectively. Generalized estimating equations (GEE) and least absolute shrinkage and selection operator (LASSO) methods were used for univariate and multivariate logistic regression, respectively. Diagnostic performance was internally validated using the area under the curve (AUC) with bootstrap adjustment. Results The study included 238 suspicious breast lesions (95 malignant, 143 benign) in 194 women. In univariate analysis, lower ADC, axial diffusivity, and radial diffusivity were associated with malignancy (OR = 0.37–0.42 per 1-SD increase, p < 0.001 for each), as was higher FA (OR = 1.45, p = 0.007). In multivariate analysis, LASSO selected only ADC (OR = 0.41) as a predictor for a DTI-only model, while both ADC (OR = 0.41) and FA (OR = 0.88) were selected for a model combining clinical and imaging parameters. Post-hoc analysis revealed varying association of FA with malignancy depending on the lesion type. The combined model (AUC = 0.81) had a significantly better performance than Clinical/DCE-MRI-only (AUC = 0.76, p < 0.001) and DTI-only (AUC = 0.75, p = 0.002) models. Conclusions DTI significantly improves diagnostic performance in multivariate modeling. ADC is the most important diffusion parameter for distinguishing benign and malignant breast lesions, while anisotropy measures may help further characterize tumor microstructure and microenvironment.

Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study

“…While the DTD-derived mean shape E[ D ∆ 2 ] also reports on tissue anisotropy, it does so without being influenced by the orientational order of fibrous tissues at the sub-voxel scale (quantified by the orientational order parameter OP). We found significantly higher mean values of FA and E[ D ∆ 2 ] in tumors compared to FGT, agreeing with the previous literature on FA [ 55 , 65 ], which indicates that both metrics could be appropriate for breast cancer diagnosis. However, the mean shape E[ D ∆ 2 ] appears to be a more reliable metric than FA, as shown by its higher values in tumors with low OP and its higher statistical power for distinguishing tumors and FGT ( p < 0.001 for E[ D ∆ 2 ] versus p = 0.02 for FA).…”

Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body

“…[33] showed that isotropic diffusivity in FGT (Ħ 2 × 10 −3 mm 2 /s) [139][140][141] were significantly higher than cancers which is in agreement with previous findings on MD [142][143][144][145]. [33] reported that the fractional anisotropy and microscopic anisotropy values in FGT were significantly lower than tumors, in line with the previous literature [139,146].In healthy breast tissue, there are elongated structures such as lobules, ducts, and stroma in FGT that have large diameters compared to the mean displacement of water molecules during the diffusion time [139]. This may lead to low microscopic anisotropy and FA values in healthy breast tissue.…”

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

“…However, single diffusion encoding cannot provide detailed information on tumor microstructure. Therefore, DTI is performed with conventional breast MRI, and this enables the estimation of MD and FA, which is used to determine whether a breast lesion is benign or malignant (44)(45)(46). Furthermore, MK can be estimated using DKI.…”