Satoko Ishigaki

Diffusion-Weighted Imaging of Breast Masses: Comparison of Diagnostic Performance Using Various Apparent Diffusion Coefficient Parameters

OBJECTIVE The purpose of our study was to assess the utility of the minimum apparent diffusion coefficient (ADC), average ADC, maximum ADC, and ADC difference value and to find optimum ADC parameters for differentiation between benign and malignant lesions in breast diffusion-weighted imaging (DWI). MATERIALS AND METHODS Sixty-seven women with 75 masslike lesions (27 benign, 48 malignant) were examined with 3-T MRI. To assess heterogeneity within the lesion, the difference between minimum and maximum ADCs was recorded as the ADC difference value. Diagnostic performances of these parameters were compared by receiver operating characteristic (ROC) curve analysis. RESULTS Each ADC parameter showed significant differences between malignant and benign lesions. The optimal cutoff levels for differentiating benign versus malignant lesions were determined by identifying the points where the sensitivity and specificity were equal on the ROC curves. According to ROC analyses, the following sensitivities and specificities were obtained: average ADC, 75.6% and 75.6%; minimum ADC, 85.5% and 85.5%; maximum ADC, 63.5% and 63.5%; ADC difference value, 70.1% and 70.1%. Minimum ADC had the largest area under the ROC curve (AUC) of 0.93. Minimum ADC combined with the ADC difference value improved the AUC to 0.95, with sensitivity and specificity of 89.1% and 89.1%. CONCLUSION Minimum ADC may be an optimal DWI single parameter for differentiation between malignant and benign lesions of breast masses. Furthermore, the combination of the minimum ADC and ADC difference value significantly elevated diagnostic performance of breast DWI in comparison with average ADC.

Predictive Value for Malignancy of Suspicious Breast Masses of BI-RADS Categories 4 and 5 Using Ultrasound Elastography and MR Diffusion-Weighted Imaging

OBJECTIVE The aim of this study is to evaluate the ability of ultrasound elastography and MR diffusion-weighted imaging (DWI) to predict malignancy of breast masses, with subsequent recommendation for biopsy. MATERIALS AND METHODS For 115 breast masses classified as BI-RADS category 4 or 5, which were assessed according to combined findings of mammography, B-mode sonography, and dynamic contrast-enhanced MRI, two radiologists retrospectively evaluated the elasticity scores using ultrasound elastography and the apparent diffusion coefficient (ADC) values using MR DWI. The diagnostic abilities of these two techniques were analyzed by using univariate and multivariate logistic regression analysis. RESULTS In the analysis of all 115 breast masses, the elasticity score was predictive of malignancy, whereas the ADC value was not independently predictive. In an analysis of the 52 masses assessed as BI-RADS category 4, the elasticity score was found to be a significant predictor of malignancy, compared with the ADC value, which was a nonsignificant predictor. In an analysis of the 63 masses assessed as BI-RADS category 5, neither the elasticity score nor the ADC value was a significant predictor of malignancy. CONCLUSION Our results show that elasticity imaging provides relatively reliable predictions for malignancy, especially in BI-RADS category 4 masses, compared with MR DWI.

1H-magnetic resonance spectroscopy of the breast at 3.0-T: Comparison of results obtained before and after administration of gadolinium-based contrast agent

To assess the effects of gadolinium‐based contrast agent (GBCA) on 1H‐magnetic resonance spectroscopy (MRS) of the breast at 3.0‐T.

Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer

BackgroundBreast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes.MethodsWe retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (Ktrans, kep, and ve) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed.ResultsThe mean of ve was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of ve was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of ve was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of ve was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher ve coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006).ConclusionsVarious ve parameters correlated with breast cancer prognostic factors and molecular subtypes.