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 [λ1], radial diffusivity [(λ2 + λ3)/2], and empirical difference [λ1 - λ3]) 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.

中文翻译：

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扩散张量成像用于表征肿瘤微结构并改善乳腺MRI的诊断性能：一项前瞻性观察性研究。

背景技术由于恶性肿瘤限制扩散的趋势，扩散加权成像（DWI）可以增加乳房MRI诊断的特异性。扩散张量成像（DTI）在常规DWI上提供了有关扩散方向性和各向异性的更多信息。我们的研究评估了在MRI上检测到的可疑乳腺病变的DTI特征，以确定DTI对乳腺成像的附加诊断价值。方法经IRB批准，我们在3年内前瞻性招募了可疑（BI-RADS第4或5类）MRI检出的乳腺病变并具有组织病理学结果的患者。患者接受了动态对比增强（DCE）和DTI序列的多参数3 T MRI。临床因素（年龄，绝经状态，乳房密度，临床适应症，通过解释放射科医生的前瞻性记录了本底实质增强）和DCE-MRI病变参数（大小，类型，洗脱的存在，BI-RADS类别）。回顾性地测量了DTI参数（表观扩散系数[ADC]，分数各向异性[FA]，轴向扩散率[λ1]，径向扩散率[（λ2+λ3）/ 2]和经验差[λ1-λ3]）。分别使用广义估计方程（GEE）和最小绝对收缩和选择算子（LASSO）方法进行单变量和多变量logistic回归。使用曲线下面积（AUC）进行自举调整，在内部验证了诊断性能。结果该研究包括了194名妇女中的238例可疑乳腺病变（95例恶性，143例良性）。在单变量分析中，较低的ADC，轴向扩散率，径向扩散率和放射扩散率与恶性肿瘤相关（每1-SD升高OR = 0.37-0.42，每种升高p <0.001），而较高的FA也是如此（OR = 1.45，p = 0.007）。在多变量分析中，LASSO仅选择ADC（OR = 0.41）作为仅DTI模型的预测指标，而同时选择ADC（OR = 0.41）和FA（OR = 0.88）作为结合临床和影像学参数的模型。事后分析显示，FA与恶性程度之间的关联有所不同，具体取决于病变类型。组合模型（AUC = 0.81）的性能明显优于仅使用Clinical / DCE-MRI的模型（AUC = 0.76，p <0.001）和仅使用DTI的模型（AUC = 0.75，p = 0.002）。结论DTI显着提高了多变量建模的诊断性能。ADC是区分良性和恶性乳腺病变的最重要的扩散参数，