Diagnostic performance of DCE-MRI, multiparametric MRI and multimodality imaging for discrimination of breast non-mass-like enhancement lesions.

OBJECTIVE: The aim of this study was to investigate and compare the diagnostic performance of dynamic contrast-enhanced (DCE)-MRI, multiparametric MRI (mpMRI), and multimodality imaging (MMI) combining mpMRI and mammography (MG) for discriminating breast non-mass-like enhancement (NME) lesions. METHODS: This retrospective study enrolled 193 patients with 199 lesions who underwent 3.0 T MRI and MG from January 2017 to December 2019. The features of DCE-MRI, turbo inversion recovery magnitude (TIRM), and diffusion-weighted imaging (DWI) were assessed by two breast radiologists. Then, all lesions were divided into microcalcification and non-microcalcification groups to assess the features of MG. Comparisons were performed between groups using univariate analyses. Then, multivariate analyses were performed to construct diagnostic models for distinguishing NME lesions. Diagnostic performance was evaluated by using the area under the curve (AUC) and the differences between AUCs were evaluated by using the DeLong test. RESULTS: Overall (n = 199), mpMRI outperformed DCE-MRI alone (AUCmpMRI = 0.924 vs. AUCDCE-MRI = 0.884; p = 0.007). Furthermore, MMI outperformed both mpMRI and MG (the microcalcification group [n = 140]: AUCMMI = 0.997 vs. AUCmpMRI = 0.978, p = 0.018 and AUCMMI = 0.997 vs. AUCMG = 0.912, p < 0.001; the non-microcalcification group [n = 59]: AUCMMI = 0.857 vs. AUCmpMRI = 0.768, p = 0.044 and AUCMMI = 0.857 vs. AUCMG = 0.759, p = 0.039). CONCLUSION & ADVANCES IN KNOWLEDGE: DCE-MRI combined with DWI and TIRM information could improve the diagnostic performance for discriminating NME lesions compared with DCE-MRI alone. Furthermore, MMI combining mpMRI and MG showed better discrimination than both mpMRI and MG.

Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI.

BACKGROUND: Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features. MATERIALS AND METHODS: From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis. RESULT: Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC90 were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC90>1.47×10-3 mm2/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%). CONCLUSION: Circumscribed margin and rim enhancement on s-MRI and ADC90 are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection.

Preoperative predicting malignancy in breast mass-like lesions: value of adding histogram analysis of apparent diffusion coefficient maps to dynamic contrast-enhanced magnetic resonance imaging for improving confidence level.

OBJECTIVE: This study aims to find out the benefits of adding histogram analysis of apparent diffusion coefficient (ADC) maps onto dynamic contrast-enhanced MRI (DCE-MRI) in predicting breast malignancy. METHODS: This study included 95 patients who were found with breast mass-like lesions from January 2014 to March 2016 (47 benign and 48 malignant). These patients were estimated by both DCE-MRI and diffusion-weighted imaging (DWI) and classified into two groups, namely, the benign and the malignant. Between these groups, the DCE-MRI parameters, including morphology, enhancement homogeneity, maximum slope of increase (MSI) and time-signal intensity curve (TIC) type, as well as histogram parameters generated from ADC maps were compared. Then, univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables in predicting malignancy. Receiver operating characteristic curve analyses were taken to assess their clinical values. RESULTS: The lesion morphology, MSI and TIC Type (p < 0.05) were significantly different between the two groups. Multivariate logistic regression analyses revealed that irregular morphology, TIC Type II/III and ADC10 were important predictors for breast malignancy. Increased area under curve (AUC) and specificity can be achieved with Model 2 (irregular morphology + TIC Type II/III + ADC10 < 1.047 ×10-3 mm2 s-1) as the criterion than Model 1 (irregular morphology + TIC Type II/III) only (Model 2 vs Model 1; AUC, 0.822 vs 0.705; sensitivity, 68.8 vs 75.0%; specificity, 95.7 vs 66.0%). CONCLUSION: Irregular morphology, TIC Type II/III and ADC10 are indicators for predicting breast malignancy. Histogram analysis of ADC maps can provide additional value in predicting breast malignancy. Advances in knowledge: The morphology, MSI and TIC types in DCE-MRI examination have significant difference between the benign and malignant groups. A higher AUC can be achieved by using ADC10 as the diagnostic index than other ADC parameters, and the difference in AUC based on ADC10 and ADCmean was statistically significant. The irregular morphology, TIC Type II/III and ADC10 were significant predictors for malignant lesions.