Article

Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images

Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Journal of Magnetic Resonance Imaging (Impact Factor: 2.79). 11/2008; 28(5):1157-65. DOI: 10.1002/jmri.21570
Source: PubMed

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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