Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings.

OBJECTIVE: To investigate the diagnostic value of dual-phase apparent diffusion coefficient (ADC) compared to traditional ADC values in quantitative diffusion-weighted imaging (DWI) for differentiating between benign and malignant breast masses. MATERIALS AND METHODS: Diffusion-weighted images of pathologically confirmed 88 benign and 85 malignant lesions acquired using a 3.0T MR scanner were analyzed. Small region-of-interests focusing on the highest signal intensity of lesions were used. Lesion ADC estimates were obtained separately from all b-value images (ADC; b=50, 400 and 800s/mm2), lower b-value images (ADClow; b=50 and 400s/mm2) and higher b-value images (ADChigh; b=400 and 800s/mm2). A set of dual-phase ADC (dpADC) models were constructed using ADClow, ADChigh and a perfusion influence factor ranging from 0 to 1. RESULTS: Strong positive correlation is observable between ADC and all dpADCs (ρ=0.80-1.00). Differences in ADC and dpADCs between the benign and the malignant lesions are all significant (p<0.05). In detecting malignancy, traditional lesion ADC provides a good performance (AUC=89.9%) however dpADC0.5 (dpADC with a factor of 0.5) accomplishes a better performance (AUC=90.8%). At optimal thresholds, ADC achieves 94.1% sensitivity, 72.7% specificity and 83.2% accuracy while dpADC0.5 leads to 92.9% sensitivity, 79.5% specificity and 86.1% accuracy. CONCLUSION: Dual-phase ADC modelling may improve the accuracy in breast cancer diagnosis using DWI. Further prospective studies are needed to justify its benefit in clinical setting.

Diagnostic performance of diffusion tensor imaging parameters in breast cancer and correlation with the prognostic factors.

PURPOSE: To evaluate the diagnostic performances of the diffusion tensor imaging (DTI) parameters in the diagnosis of breast cancer and to investigate the variations in DTI parameters according to the breast cancer biomarkers. MATERIALS AND METHODS: At 3.0 Tesla (T), DTI was performed in 85 patients with 92 enhancing breast lesions. λ1 , λ2 , λ3 , mean diffusivity (MD), radial diffusivity (RD), fractional anisotropy (FA), relative anisotropy (RA), and geodesic anisotropy (GA) were studied and compared with diffusion-weighted imaging-derived apparent diffusion coefficient. Lesions were analyzed according to BIRADS lexicon. Logistic regression models were constructed to determine the contribution of DTI to the specificity and the accuracy of DCE-MRI. Breast cancer biomarkers; estrogen receptor (ER), HER-2 status, and Ki-67 were correlated with DTI in malignant cases. RESULTS: Malignant lesions exhibited significantly lower MD, RD, λ1 , λ2 , λ3 and higher FA, RA, GA values (P < 0.001). Logistic regression models showed that MD, RD, λ1 , λ2 , λ3 , FA, and RA increase the specificity of the DCE-MRI (from 83.0% to 89.4-93.6%; P < 0.05). Higher RD, λ2 , λ3 and lower FA, RA, and GA values were observed in ER-negative breast cancer (P < 0.05). Ki-67 showed significant, negative correlation with FA, RA, GA, λ1 -λ3 and λ1 -λ2 (r = -0.336 to -0.435; P < 0.05). CONCLUSION: Besides its ability to differentiate malignant breast lesions, DTI improves the specificity of conventional 3.0T breast MRI and shows correlation with biomarkers ER and Ki-67. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:660-672.

Quantitative differentiation of breast lesions at 3T diffusion-weighted imaging (DWI) using the ratio of distributed diffusion coefficient (DDC).

PURPOSE: To investigate the accuracy of diffusion coefficients and diffusion coefficient ratios of breast lesions and of glandular breast tissue from mono- and stretched-exponential models for quantitative diagnosis in diffusion-weighted magnetic resonance imaging (MRI). MATERIALS AND METHODS: We analyzed pathologically confirmed 170 lesions (85 benign and 85 malignant) imaged using a 3.0T MR scanner. Small regions of interest (ROIs) focusing on the highest signal intensity for lesions and also for glandular tissue of contralateral breast were obtained. Apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were estimated by performing nonlinear fittings using mono- and stretched-exponential models, respectively. Coefficient ratios were calculated by dividing the lesion coefficient by the glandular tissue coefficient. RESULTS: A stretched exponential model provides significantly better fits then the monoexponential model (P < 0.001): 65% of the better fits for glandular tissue and 71% of the better fits for lesion. High correlation was found in diffusion coefficients (0.99-0.81 and coefficient ratios (0.94) between the models. The highest diagnostic accuracy was found by the DDC ratio (area under the curve [AUC] = 0.93) when compared with lesion DDC, ADC ratio, and lesion ADC (AUC = 0.91, 0.90, 0.90) but with no statistically significant difference (P > 0.05). At optimal thresholds, the DDC ratio achieves 93% sensitivity, 80% specificity, and 87% overall diagnostic accuracy, while ADC ratio leads to 89% sensitivity, 78% specificity, and 83% overall diagnostic accuracy. CONCLUSION: The stretched exponential model fits better with signal intensity measurements from both lesion and glandular tissue ROIs. Although the DDC ratio estimated by using the model shows a higher diagnostic accuracy than the ADC ratio, lesion DDC, and ADC, it is not statistically significant. J. Magn. Reson. Imaging 2016;44:1633-1641.

Correlation of clot distribution with morphometric measurements and pleuroparenchymal findings in acute pulmonary embolism: experience with 692 cases.

PURPOSE: The aim of the study is to determine if clot distribution in acute pulmonary embolism (PE) correlates with morphometric measurements of right heart function, reflux in inferior vena cava (IVC), and pleuroparenchymal findings. MATERIALS AND METHODS: A total of 692 computed tomography pulmonary angiographies with PE were enrolled, and patients were grouped according to clot localization. Parenchymal findings, morphometric measurements of right heart function, and contrast reflux in IVC were noted. RESULTS: Differences were found between groups for most measurements, and central PE was associated with significantly higher right ventricle (RV) and pulmonary artery diameters, ratio of RV diameter to left ventricle (LV) diameter (RV/LV) ≥ 1, and IVC reflux. CONCLUSIONS: Significant association was present among clot distribution in PE, morphometrics, IVC reflux, and pleuroparenchymal findings.