Yabuuchi H, Matsuo Y, Okafuji T, et al. 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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- "Bogner et al. (31) stated that the higher field strength at (3.0 T) may enable a DWI acquisition with thinner slices that can better demonstrate small or diffuse lesions. Yabuuchi et al. (25), who mentioned that DWI and ADC may have lower sensitivity than DCE-MRI for detecting breast cancer. This has been described previously; with 6–37.5% of malignant breast lesions reportedly not visible on DWI. "
ABSTRACT: The purpose of this study was to evaluate the usefulness of apparent diffusion coefficient (ADC value) in differentiating between probably benign breast lesions and, suspicious lesions (ACR-BIRADS categories 3 and 4 respectively).03/2015; 193(2). DOI:10.1016/j.ejrnm.2015.02.004
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- "A recognized weakness of both DCE-MRI and DW-MRI is their low specificity in discriminating between benign and malignant lesions (37–86%) [15-17]; therefore, biopsy tests are frequently adopted as a remedy, which inevitably introduce sampling errors. Recent studies focus on comparing and retrospectively integrating the contributions from different modalities by combining the merits of different modalities [18,19]. This work has highlighted the potential of combining multi-modality characteristics to differentiate the core of the tumor from peritumoral tissues and normal tissues, and thus to provide richer information on lesion status than individual imaging modalities [20,21]. "
ABSTRACT: Background The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla. Methods This retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status. Results Three classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group. Conclusions The combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.BMC Cancer 05/2014; 14(1):366. DOI:10.1186/1471-2407-14-366 · 3.32 Impact Factor
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- "Preliminary data of DWI studies of the breast showed high sensitivity for detecting cancer, based on low diffusivity in carcinomas due to higher cell density (Park et al., 2007; Yoshikawa et al., 2008). Furthermore, quantitative DWI analyses have shown that the apparent diffusion coefficient (ADC) is significantly lower in many breast carcinomas compared with benign lesions, is supporting as a potential diagnostic tool (Guo et al., 2002; Kinoshita et al., 2002; Sinha et al., 2002; Wenkel et al ., 2002; Woodhams et al., 2005; Rubesova et al., 2006; Park et al., 2007; Hatakenaka et al., 2008; Peters et al., 2008; Yabuuchi et al., 2008; Yoshikawa et al., 2008; Lo et al., 2009; Partridge et al., 2010; Kul et al., 2011; Sonmez et al., 2011). "
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