Improved diagnostic accuracy of breast MRI through combined apparent diffusion coefficients and dynamic contrast-enhanced kinetics

Abstract

This study investigated the relationship between apparent diffusion coefficient (ADC) measures and dynamic contrast-enhanced magnetic resonance imaging (MRI) kinetics in breast lesions and evaluated the relative diagnostic value of each quantitative parameter. Seventy-seven women with 100 breast lesions (27 malignant and 73 benign) underwent both dynamic contrast-enhanced MRI and diffusion weighted MRI. Dynamic contrast-enhanced MRI kinetic parameters included peak initial enhancement, predominant delayed kinetic curve type (persistent, plateau, or washout), and worst delayed kinetic curve type (washout > plateau > persistent). Associations between ADC and dynamic contrast-enhanced MRI kinetic parameters and predictions of malignancy were evaluated. Results showed that ADC was significantly associated with predominant curve type (ADC was higher for lesions exhibiting predominantly persistent enhancement compared with those exhibiting predominantly washout or plateau, P = 0.006), but was not significantly associated with peak initial enhancement or worst curve type (P > 0.05). Univariate analysis showed significant differences between benign and malignant lesions in both ADC (P < 0.001) and worst curve (P = 0.003). In multivariate analysis, worst curve type and ADC were significant independent predictors of benign versus malignant outcome and in combination produced the highest area under the receiver operating characteristic curve (0.85 and 0.78 with 5-fold cross validation).

Figure 1

Boxplots of DWI-MRI ADC and PE values versus diagnosis categories (benign, DCIS, and invasive carcinoma). P values are given for GEE test of differences between benign and malignant (DCIS and invasive carcinoma).

Figure 2

A 2.2 cm enhancing invasive ductal carcinoma detected in an 85-year-old woman who underwent breast MRI for a suspicious palpable mass in the right breast. DCE-MRI showed a lobular mass with spiculated margins that demonstrated significant washout (shown in red) (a). The mass exhibited low ADC (mean ADC, 1.25 ×10^-3 mm²/s) on DWI (b).

Figure 3

Several lesion examples where DCE-MRI and DWI suggested different levels of suspicion. DCE-MR images are shown on the top row and corresponding ADC maps are shown below. In each case, the lesion of interest is delineated by a box on both images and enlarged for clarity (boxes do not represent the ROIs, which were manually drawn within lesion borders as described in the methods). A 1.1 cm invasive ductal carcinoma in a 59 year-old woman that demonstrated only persistent enhancement on DCE-MRI with no washout, but exhibited a low ADC value on DWI (mean ADC, 1.34 ×10^-3 mm²/s) (a). A 1.0 cm benign papilloma detected in a 41 year-old woman that demonstrated concerning washout (red), but exhibited high ADC (mean ADC, 2.04 ×10^-3 mm²/s) (b). A 2.6 cm region of benign fibrocystic change detected in a 43-year old woman that demonstrated no washout on DCE-MRI, but exhibited suspiciously reduced diffusivity, with low ADC values (mean, 1.04 ×10^-3 mm²/s).

Figure 4

Scatterplot of ADC mean versus peak enhancement by lesion class, with GEE fitted line (slope = 0.0012, p = 0.182). No significant association was observed between lesion ADC and peak enhancement.

Figure 5

Boxplots of DWI ADC values versus DCE-MRI delayed phase kinetics (predominant and worst curve type), n=100 lesions. P values are given for GEE test of overall differences between curve type categories (persistent, plateau, and washout).

Figure 6

ROC comparisons of ADC, DCE-MRI worst curve, and a model combining the two parameters for discriminating benign and malignant lesions. Curves represent the diagnostic performance of logistic regression models for (1) worst curve alone: y = 1.16 + 0.81*worst curve, where worst curve has value 1 for washout or 0 for no washout (persistent or plateau), (2) ADC alone: y = -4.36 + 3.63*ADC, where ADC is in units ×10^-3 mm²/s, and (3) Combined ADC and worst curve: y = -4.28 + 3.69*ADC +0.84*worst curve. The area under the curve (AUC) is calculated for each model for comparison of diagnostic performance.