http://radiology.rsnajnls.org/cgi/content/full/245/3/684/DC1.

MR-directed ultrasound of MRI-detected lesions was useful for decision making as part of the diagnostic workup. Malignant lesions were likely to have an ultrasound correlate, especially when they presented as masses on MRI. However, the sonographic findings of these lesions were often subtle, and careful scanning technique was needed for successful MRI-ultrasound correlation.

No abstract

• Tumour ADC was significantly lower in LVI-positive than LVI-negative breast cancer. • Peritumoral maximum-ADC was significantly higher in LVI-positive than LVI-negative breast cancer. • Peritumour-tumour ADC ratio was significantly higher in LVI-positive breast cancer. • Diagnostic performance of the peritumour-tumour ADC ratio was highest for positive LVI. • Peritumour-tumour ADC ratio showed higher diagnostic ability in postmenopausal than premenopausal patients.

A multi-parametric computer-aided diagnosis (CADx) scheme that combines information from T1-weighted DCE-MRI and T2-weighted MRI was investigated using a database of 110 malignant and 86 benign breast lesions. Automatic lesion segmentation was performed, and three categories of lesion features (geometric, T1-weighted DCE, and T2-weighted) were automatically extracted. Stepwise feature selection was performed considering only geometric features, only T1-weighted DCE features, only T2-weighted features, and all features. Features were merged with Bayesian artificial neural networks, and diagnostic performance was evaluated by ROC analysis. With leave-one-lesion-out cross-validation, an AUC value of 0.77 ± 0.03 was achieved with T2-weighted-only features, indicating high diagnostic value of information in T2-weighted images. AUC values of 0.79 ± 0.03 and 0.80 ± 0.03 were obtained for geometric-only features and T1-weighted DCE-only features, respectively. When all features were considered, an AUC value of 0.85 ± 0.03 was achieved. We observed p-values of 0.0006, 0.023, and 0.0014 between the {geometric-only, T1-weighted DCE-only, and T2-weighted-only features} and all features conditions, respectively. When ranked, the p-values satisfied the Holm-Bonferroni multiple-comparison test; thus, the improvement of multi-parametric CADx was statistically significant. A CADx scheme that combines information from T1-weighted DCE and T2-weighted MRI may be advantageous over conventional T1-weighted DCE-MRI CADx.

Purpose To compare the pathology and kinetic characteristics of breast lesions with focus, mass and nonmass-like enhancement. Materials and Methods 852 MRI detected breast lesions in 697 patients were selected for an IRB approved review. Patients underwent dynamic contrast enhanced MRI using one pre and three to six post-contrast T1 weighted images. The ‘type’ of enhancement was classified as mass, non-mass or focus, and kinetic curves quantified by the initial enhancement percentage (E1), time to peak enhancement (Tpeak) and signal enhancement ratio (SER). These kinetic parameters were compared between malignant and benign lesions within each morphologic type. Results 552 lesions were classified as mass (396 malignant, 156 benign), 261 as nonmass (212 malignant,49 benign) and 39 as focus (9 malignant,30 benign). The most common pathology of malignant/benign lesions by morphology: for mass, invasive ductal carcinoma/fibroadenoma; for nonmass, ductal carcinoma in situ (DCIS)/fibrocystic change(FCC); for focus, DCIS/FCC. Benign mass lesions exhibited significantly lower E1, longer Tpeak and lower SER compared with malignant mass lesions (p < 0.0001). Benign nonmass lesions exhibited only a lower SER compared to malignant nonmass lesions (p<0.01). Conclusions By considering the diverse pathology and kinetic characteristics of different lesion morphologies, diagnostic accuracy may be improved.

In a population of 220 sequentially diagnosed breast cancer lesions, we found seven (3.2%) MRI-occult cancers, fewer than seen in other published studies. Small tumor size and diffuse parenchymal enhancement were the principal reasons for these false-negative results. Although the overall sensitivity of cancer detection was high (96.8%), it should be emphasized that a negative MRI should not influence the management of a lesion that appears to be of concern on physical examination or on other imaging techniques.

OBJECTIVE The purpose of this study was to compare MRI kinetic curve data acquired with three systems in the evaluation of malignant lesions of the breast. MATERIALS AND METHODS The cases of 601 patients with 682 breast lesions (185 benign, 497 malignant) were selected for review. The malignant lesions were classified as ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and other. The dynamic MRI protocol consisted of one unenhanced and three to seven contrast-enhanced images acquired with one of three imaging protocols and systems. An experienced radiologist analyzed the shapes of the kinetic curves according to the BI-RADS lexicon. Several quantitative kinetic parameters were calculated, and the kinetic parameters of malignant lesions were compared across the three systems. RESULTS Imaging protocol and system 1 were used to image 304 malignant lesions (185 IDC, 62 DCIS); imaging protocol and system 2, 107 lesions (72 IDC, 21 DCIS); and imaging protocol and system 3, 86 lesions (64 IDC, 17 DCIS). Compared with those visualized with imaging protocols and systems 1 and 2, IDC lesions visualized with imaging protocol and system 3 had significantly less initial enhancement, longer time to peak enhancement, and a slower washout rate (p < 0.004). Only 47% of IDC lesions imaged with imaging protocol and system 3 exhibited washout type curves, compared with 75% and 74% of those imaged with imaging protocols and systems 2 and 1, respectively. The diagnostic accuracy of kinetic analysis was lowest for imaging protocol and system 3, but the difference was not statistically significant. CONCLUSION The kinetic curve data on malignant lesions acquired with one system showed significantly lower initial contrast uptake and a different curve shape in comparison with data acquired with the other two systems. Differences in k-space sampling, T1 weighting, and magnetization transfer effects may be explanations for the difference.