Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images
ABSTRACT To evaluate the diagnostic accuracy of a combination of dynamic contrast-enhanced MR imaging (DCE-MRI) and diffusion-weighted MR imaging (DWI) in characterization of enhanced mass on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy.
We analyzed consecutive breast MR images in 270 patients; however, 13 lesions in 93 patients were excluded based on our criteria. We analyzed tumor size, shape, margin, internal mass enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators of malignancy and calculate a predictive probability for malignancy. We added the corresponding categories to these prediction probabilities for malignancy and calculated diagnostic accuracy when we consider category 4b, 4c, and 5 lesions as malignant and category 4a, 3, and 2 lesions as benign. In a validation study, 75 enhancing lesions in 71 patients were examined consecutively.
Irregular margin, heterogeneous internal enhancement, rim enhancement, plateau time-intensity curve (TIC) pattern, and washout TIC pattern were the strongest indicators of malignancy as well as past studies, and ADC values less than 1.1x10(-3) mm2/s were also the strongest indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92% (56/61), 86% (12/14), 97% (56/58), 71% (12/17), and 91% (68/75), respectively.
The combination of DWI and DCE-MRI could produce high diagnostic accuracy in the characterization of enhanced mass on breast MR imaging.
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ABSTRACT: The role of magnetic resonance diffusion-weighted imaging (DWI) to differentiate between malignant and benign lesions in the breast using mean apparent diffusion coefficient (ADC) values was evaluated prospectively in this study. Fifty female patients with 61 histopathologically proven solid breast lesions underwent dynamic contrast-enhanced magnetic resonance imaging and DWI using the spin-echo echo-planar technique. ADC maps have been obtained and ADCs of the lesions were calculated without knowledge of histopathological diagnosis. Golden standard was histology to define benign and malignant lesions. Statistical analysis was used to compare ADC values in the benign and malignant group and to calculate best cut-off value for distinguishing both groups based on receiver operator-curve characteristics (ROC). Differentiation of the benign and the malignant masses revealed that the threshold value of the ADC in maximum sensitivity and specificity was 1.22×10-3 mm2/s; at this threshold sensitivity was 96.2%, its specificity was 88.5%, and its positive predictive value was 86.2%. Its negative predictive value was 96.9%, and the accuracy rate was 91.8%. ROC analysis showed an area under the curve of 0.924 (p<0.001). Breast MRI with DWI using ADC measurements can be useful in the differentiation of benign and malignant breast lesions.Journal of Experimental and Clinical Medicine 01/2013; 30(4):305-310. DOI:10.5835/jecm.omu.30.04.005
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ABSTRACT: In order to better predict and follow treatment responses in cancer patients, there is growing interest in noninvasively characterizing tumor heterogeneity based on MR images possessing different contrast and quantitative information. This requires mechanisms for integrating such data and reducing the data dimensionality to levels amenable to interpretation by human readers. Here we propose a two-step pipeline for integrating diffusion and perfusion MRI that we demonstrate in the quantification of breast lesion heterogeneity. First, the images acquired with the two modalities are aligned using an intermodal registration. Dissimilarity-based clustering is then performed exploiting the information coming from both modalities. To this end an ad hoc distance metric is developed and tested for tuning the weighting for the two modalities. The distributions of the diffusion parameter values in subregions identified by the algorithm are extracted and compared through nonparametric testing for posterior evaluation of the tissue heterogeneity. Results show that the joint exploitation of the information brought by DCE and DWI leads to consistent results accounting for both perfusion and microstructural information yielding a greater refinement of the segmentation than the separate processing of the two modalities, consistent with that drawn manually by a radiologist with access to the same data.International Journal of Biomedical Imaging 11/2012; 2012:676808. DOI:10.1155/2012/676808
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ABSTRACT: The purpose of this study is to investigate whether an adjacent vessel sign (AVS) observed on the maximum intensity projections (MIPs) from the subtracted images can help distinguish between malignant and benign breast lesions and to test whether the combination of breast imaging reporting and data system (BI-RADS) category and AVS can increase the specificity and diagnostic accuracy of breast magnetic resonance imaging (MRI). The study included 63 histologically verified lesions which underwent dynamic breast MRI before biopsy. All magnetic resonance (MR) images were evaluated by two radiologists in consensus, who were unaware of the histopathological outcome. The MR images of all cases were analyzed according to BI-RADS-MRI assessment category. Levels of suspicion were reported as categories of I-V. The presence of vessels either entering the enhancing lesion or in contact with the lesion edge on MIP images was considered as the presence of AVS. Final analysis of 63 masses revealed 41 malignant lesions (65.1%) and 22 benign lesions (34.9%). Thirty seven out of 41 malignant lesions and 3 out of 22 benign lesions were associated with adjacent vessel, with highly significant difference between benign and malignant lesions (P < 0.001), especially for lesions smaller than 2.0 cm. The corresponding specificity, sensitivity and accuracy of contrast-enhanced 3.0-T breast were 86.4%, 82.9% and 84.1%, respectively. Based on BI-RADS-MRI category, the specificity, sensitivity and accuracy of breast MRI were 54.5%, 100% and 84.1%, respectively. After combining BI-RADS category and AVS, the specificity, sensitivity and accuracy of breast MRI were 90.9%, 82.9% and 85.7%, respectively. AVS can help differentiate malignant from benign breast lesions, especially for the lesions smaller than 2.0 cm. The combination of BI-RADS category and AVS can increase the specificity and the diagnostic accuracy of breast MRI.Biotechnology & Biotechnological Equipment 11/2014; 28(6):1121-1126. DOI:10.1080/13102818.2014.974016 · 0.38 Impact Factor