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. 2017 May;45(5):1385-1393.
doi: 10.1002/jmri.25501. Epub 2016 Oct 20.

Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohort

Sungheon Gene Kim  1   2 , Melanie Freed  1   2 , Ana Paula Klautau Leite  1   2 , Jin Zhang  1   2 , Claudia Seuss  1   2 , Linda Moy  1   2
Affiliations

Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohort

Sungheon Gene Kim et al. J Magn Reson Imaging. 2017 May.

Abstract

Purpose: To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions.

Materials and methods: This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (Ktrans ), interstitial volume fraction (ve ), and vascular volume fraction (vp ) estimated from the lesion and BP were used to separate malignant from benign lesions.

Results: The patients with malignant lesions had significantly (P < 0.05) higher median lesion-Ktrans (0.081 min-1 ), higher median BP-Ktrans (0.032 min-1 ), and BP-vp (0.020) than those without malignant lesions (0.056 min-1 , 0.017 min-1 and 0.012, respectively). The area under the receiver operating characteristic curve (AUC) of the BP-Ktrans (0.687) was highest among the single parameters and higher than that of the lesion-Ktrans (0.664). The combined logistic regression model of lesion-Ktrans , lesion-ve , BP-Ktrans , BP-ve , and BP-vp had the highest AUC of 0.730.

Conclusion: Our results suggest that the contrast kinetic analysis of DCE-MRI data can be used to differentiate the malignant lesions from the benign and high-risk lesions among the indeterminate breast lesions recommended for MRI-guided biopsy exams.

Level of evidence: 3 J. MAGN. RESON. IMAGING 2017;45:1385-1393.

Keywords: biopsy; breast cancer; breast compression; contrast kinetic analysis; dynamic contrast enhanced MRI.

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Figures

Figure 1
Figure 1
Selection of voxels for measurement of background parenchymal enhancement. A. Voxels within the red lines were selected based on the principal component analysis method that identifies voxels strongly associated with the background parenchymal enhancement. B. Plotted is the time concentration curve of the breast background parenchyma that was calculated from the average time intensity curve of the selected voxels in A.
Figure 2
Figure 2
Comparisons of patients with benign, high-risk (HR) or malignant lesions in terms of initial enhancement ratio (A) and delayed enhancement ratio (B) using conventional breast DCE-MRI data. Neither IER nor DER shows a significant difference among patient groups with different lesion types.
Figure 3
Figure 3
Comparison of patients groups with different lesion types in terms of contrast kinetic model parameters estimated using the high-temporal resolution images. The boxes for individual groups represent the interquartile ranges between the first and third quartiles, along with the lines in the middle for the median values. The entire ranges of the data are shown by the whiskers. The pairs of groups with a significant difference between their median values are noted.
Figure 4
Figure 4
Examples of lesions (A, C) and their contrast enhancement curves (B, D) acquired during MRI-guided biopsy exams. The first example shown in A and B is a 40-year-old woman with a benign lesion that had the highest Ktrans value in the benign group. This case could not be classified as a benign case using any of the kinetic model parameters or their combinations. The second example shown in C and D is a 49-year-old woman with invasive ductal carcinoma that had the highest Ktrans value in the malignant group. These examples demonstrate the challenges in diagnosing these lesions.
Figure 5
Figure 5
A. Comparison of diagnostic accuracies of contrast kinetic model parameters estimated in lesions (L) and background parenchyma (B). The combined model with the highest AUC value was comprised of lesion Ktrans, lesion ve, BP Ktrans, and BP vp. B. The receiver operating characteristic curve of the combined model.
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