Department of Radiology, University of Washington School of Medicine, 825 Eastlake Avenue East, Seattle, WA, 98109, USA.
Department of Pathology, University of Washington School of Medicine, 1959 NE Pacific St. Box 356100, Seattle, WA, 98195, USA.
Breast Cancer Res. 2019 Sep 4;21(1):102. doi: 10.1186/s13058-019-1183-3.
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.
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], radial diffusivity [(λ + λ)/2], and empirical difference [λ - λ]) 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.
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.
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.
扩散加权成像（DWI）可以通过限制恶性肿瘤的扩散来提高乳腺 MRI 的诊断特异性。扩散张量成像（DTI）提供了关于扩散方向和各向异性的常规 DWI 之外的更多信息。我们的研究评估了 MRI 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。
在获得机构审查委员会批准后，我们前瞻性地招募了在 3 年期间具有可疑（BI-RADS 类别 4 或 5）MRI 检测到的乳腺病变且具有组织病理学结果的患者。患者接受了多参数 3T MRI 检查，包括动态对比增强（DCE）和 DTI 序列。由解释放射科医师前瞻性地记录临床因素（年龄、绝经状态、乳腺密度、临床指征、背景实质增强）和 DCE-MRI 病变参数（大小、类型、有无洗脱、BI-RADS 类别）。回顾性测量 DTI 参数（表观扩散系数[ADC]、各向异性分数[FA]、轴向扩散系数[λ]、径向扩散系数[（λ+λ）/2]和经验差[λ-λ]）。使用广义估计方程（GEE）和最小绝对收缩和选择算子（LASSO）方法分别进行单变量和多变量逻辑回归。使用内部验证曲线下面积（AUC）进行调整后的 bootstrap 验证来评估诊断性能。
该研究包括 194 名女性中的 238 个可疑乳腺病变（95 个恶性，143 个良性）。在单变量分析中，ADC、轴向扩散系数和径向扩散系数降低与恶性肿瘤相关（每增加 1-SD，OR=0.37-0.42，p<0.001），FA 升高（OR=1.45，p=0.007）也与恶性肿瘤相关。在多变量分析中，LASSO 仅选择 ADC（OR=0.41）作为 DTI 模型的预测因子，而 ADC（OR=0.41）和 FA（OR=0.88）都被选为结合临床和影像学参数的模型的预测因子。事后分析显示，FA 与恶性肿瘤的关联因病变类型而异。联合模型（AUC=0.81）的性能明显优于仅临床/DCE-MRI 模型（AUC=0.76，p<0.001）和仅 DTI 模型（AUC=0.75，p=0.002）。
DTI 在多变量建模中显著提高了诊断性能。ADC 是区分良性和恶性乳腺病变的最重要的扩散参数，而各向异性测量值可能有助于进一步描述肿瘤的微观结构和微环境。
