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.
SourceAvailable from: Everty Rocha[Show abstract] [Hide abstract]
ABSTRACT: Breast cancer is the second most common cancer worldwide and the most common among women. The high mortality rate may be related to the fact that the disease is diagnosed in advanced stages. Magnetic resonance imaging (MRI) of the breasts has proved to be a powerful method adjunct to mammography and ultrasound on the breast pathologies diagnosis. Breast MRI provides information related to the morphology of the lesion and on functional aspects such as the kinetics of contrast enhancement, showing high sensitivity but low specificity for breast tumor characterization. However, there is an overlap between benign and malignant findings. Diffusion-weighted is a method that makes use of MRI to represent the mobility of water molecules in a given voxel for applying gradient. This sequence provides images different from those observed on T1 and T2, conventional sequences weighted images. Thus, this sequence is a useful tool for detecting and characterizing tumor, helping to differentiate benign and malignant lesions, classification and stage of breast cancer, as well as monitoring patient response to chemotherapy.Femina: revista da Federação Brasileira das Sociedades de Ginecologia e Obstetrícia 10/2012; 40(5):281-286.
Conference Paper: Automated analysis of spatio-temporal features for non-masses[Show abstract] [Hide abstract]
ABSTRACT: Non-mass enhancing lesions represent one of the most challenging types of lesions when it comes to both manual and computer-assisted diagnosis. Compared to the well-characterized mass-enhancing lesions, non-masses have not well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus non-discriminative for malignant and benign non-masses. A valuable feature descriptor should capture the heterogeneity of enhancement as well as the speed of enhancement in the tissue. We apply and evaluate both textural and spatio-temporal descriptors to the pertinent feature extraction of these lesions. An automated computer-aided diagnosis system evaluates the atypical behavior of these lesions, and additionally considers the impact of non-rigid motion compensation on a correct diagnosis.SPIE Defense, Security, and Sensing; 05/2013
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ABSTRACT: Purposes The purpose of our meta-analysis was to assess the overall diagnostic value of diffusion-weighted magnetic resonance imaging (DW-MRI) in detecting node metastases and investigate whether the apparent diffusion coefficient (ADC) value could be used to discriminate between metastatic and non-metastatic lymph nodes in patients with primary tumors. Materials and methods The meta-analysis included a total of 1,748 metastatic and 6,547 non-metastatic lymph nodes from 39 studies, including 8 different tumor types with lymph node metastases. Results The pooled sensitivity and specificity of DW-MRI were 0.82 (95 % CI 0.76–0.87) and 0.92 (95 % CI 0.88–0.94), respectively. The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and the area under the curve were 9.8 (95 % CI 6.9–14.0), 0.20 (95 % CI 0.15–0.26) and 0.93 (95 % CI 0.91–0.95), respectively. The probability of 42 % can be viewed as the cutoff pretest probability for DW-MRI to diagnosis lymph node metastases; when the more chance of metastatic increased from 42 % that the pretest probability was estimated, it was more suitable to emphasize on “ruling in,” on the contrary, and when the more chance of metastatic decreased from 42 %, it was more suitable to emphasize on “ruling out.” Furthermore, the mean ADC value of metastatic lymph nodes was significantly lower than that of non-metastatic (P = 0.001). Conclusions DW-MRI is useful for differentiation between metastatic and non-metastatic lymph nodes. However, DW-MRI has a moderate diagnostic value for physician’s decision making when PLR and NLR took into consideration, while a superior ability for nodal metastases confirmation, but an inferior ability for ruling out. In the future, large-scale, high-quality trials are necessary to evaluate, respectively, their clinical value in different tumor types with nodal metastases.Journal of Cancer Research and Clinical Oncology 12/2014; DOI:10.1007/s00432-014-1895-9 · 3.01 Impact Factor