Metaplastic Carcinoma of the Breast: MRI Features with Clinical and Histopathologic Correlation.

RATIONALE AND OBJECTIVES: Metaplastic carcinoma of the breast (MCB) is a rare type of breast carcinoma, and there are only limited data about the MRI findings of MCB. This article aimed to review the MRI features of MCB and correlate the MRI findings with clinical and histopathologic characteristics. MATERIALS AND METHODS: A total of 23 patients were enrolled from a signal institution. The clinical data, histological diagnosis and MRI findings were retrospectively reviewed. RESULTS: The average age of the 23 patients was 51.6 years. Twenty-one (91.3%) patients presented with a palpable mass. Squamous cell carcinoma was the most common subtype (10/23, 43.5%). Axillary lymph node metastasis was detected in 4 (17.4%) patients. The immunohistochemical profile showed the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor-2 (Her-2) expression were negative in 87.0%, 95.7%, and 91.3% of the patients respectively. On MRI, most cases exhibited an irregular mass (18/23, 78.3%) with high signal intensity on T2-weighted images (22/23,95.7%), non-circumscribed margin (19/23, 82.6%), heterogeneous enhancement (13/23, 56.5%), Type III (12/23, 52.2%) time intensity curve, and diffusion restriction (20/23, 87.0%) on diffusion weighted images (DWI). CONCLUSION: MCB most commonly demonstrated definitely malignant MRI features, which may reflect the histopathologic characteristic of this rare and aggressive breast carcinoma.

The value of DCE- MRI of the breast as a diagnostic tool in assessing amorphous calcifications in screening mammography.

Purpose: To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging in differentiating benign and malignant amorphous calcifications. Methods: This study included 193 female patients with 197 suspicious amorphous calcifications detected on screening mammography. The patients' demographics, clinical follow-up, imaging, and pathology outcomes were reviewed, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DCE-MRI were calculated. Results: Of 197 lesions (193 patients) included in the study, 50 (25.4%) were histologically proved to be malignant. DCE-MRI based on breast imaging report and diagnosis system (BI-RADS) had a sensitivity of 94.4%, specificity of 85.7%, PPV of 69.1%, and NPV of 97.7% for the detection of malignant amorphous calcifications. Notably, diagnosis solely based on the presence or absence of DCE-MRI enhancement showed the same sensitivity but significantly decreased specificity (44.8%, p < 0.001) and PPV (44.8%, p < 0.001). In patients with a minimal or mild degree of background parenchymal enhancement (BPE), the sensitivity, specificity, PPV, and NPV increased to 100%, 90.6%, 78.6%, and 100%, respectively. However, in patients with a moderate degree of BPE, MRI resulted in three false negatives of ductal carcinoma in situ (DCIS). Overall, the addition of DCE-MRI detected all invasive lesions and could decrease unnecessary biopsy by 65.5%. Conclusion: DCE-MRI based on BI-RADS has the potential to improve the diagnosis of suspicious amorphous calcifications and avoid unnecessary biopsy, especially for those with low-degree BPE.

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.

Comprehensive Analysis of Monocarboxylate Transporter 4 (MCT4) expression in breast cancer prognosis and immune infiltration via integrated bioinformatics analysis.

Lactate blunts the anticancer immune response in breast cancer (BC). However, little is known about the exact effect of lactate transporters such as monocarboxylate transporter 4 (MCT4) on immunotherapy. In this study, we investigated the expression status and prognostic value of MCT4 in BC through large-scale transcriptome data. Our results showed that MCT4 was overexpressed in BC, particularly in the basal-like molecular subtype. Overexpression of MCT4 was significantly correlated with high BC lesion grade and poor prognosis. Enrichment analysis indicated that the MCT4-related genes were involved in immune- and metabolism-related bioprocesses, such as myeloid leukocyte activation, the adaptive immune system, and catabolic process. We also found that the expression of MCT4 in BC lesions was associated with immune cell infiltration and glycolytic rate-limiting enzymes like pyruvate kinase M2 (PKM2) and hexokinases-3 (HK3). Our observations indicate that MCT4 may play a pivotal role in the maintenance of the tumor immune microenvironment (TIME) through metabolic reprogramming. The enzymes of the glycolysis pathway (MCT4, PKM2, and HK3) may thus serve as new targets to modulate the TIME and enhance immunotherapy efficiency.[Figure: see text].

Recent Progress on Immunotherapy for Breast Cancer: Tumor Microenvironment, Nanotechnology and More.

Immunotherapy is a major emerging treatment for breast cancer (BC). However, not all breast cancer patients derive benefit from immunotherapy. Predictive biomarkers of immunotherapy, such as tumor mutation burden and tumor-infiltrating lymphocytes, are promising to stratify the patients with BC and optimize the therapeutic effect. Various targets of the immune response pathway have also been explored to expand the modalities of immunotherapy. The use of nanotechnology for the imaging of predictive biomarkers and the combination with other therapeutic modalities presents a number of advantages for the immunotherapy of BC. In this review, we summary the emerging therapeutic modalities of immunotherapy, present prominent examples of immunotherapy in BC, and discuss the future opportunity of nanotechnology in the immunotherapy of BC.

Autoimmune pancreatitis masquerading as pancreatic cancer: case report of a Chinese man.

Autoimmune pancreatitis (AIP) is a type of chronic pancreatitis with an autoimmune basis, characterized by infiltrating lymphocytes and plasma cells and fibrosis. Imaging examination revealed pancreatic enlargement and irregular stenosis of the pancreatic duct, and laboratory inspection showed elevated serum IgG4 level. Effectiveness of glucocorticoids (hormone) management is a remarkable feature of this disease. It is reported that both AIP and pancreatic cancer show a marked predilection in older men, and AIP is easily misdiagnosed as pancreatic cancer which leads to unnecessary surgery. Today we present a case of type I AIP in a 64-year-old Chinese old man.

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.

A case of giant complicated intraductal papilloma of breast on MRI and literature review.

BACKGROUND: With the increasing use of image-guided biopsy, more breast intraductal papillomas are found than ever. Most intraductal papillomas are small, ranging from several milimeters to 2 cm. CASE: We described a case of giant intraductal papilloma of breast in a 19-year-old girl with nipple bloody discharge and rapid growth. Magnetic resonance imaging diagnostic evaluation and pathologic correlation along with review of relevant literatures are documented. The lesion was characterized by presence of mixture of cystic and solid component with cystic component occupying a large space. The solid component showed morphologic, kinetic, and diffusion weighted imaging features suggestive of malignancy. Infarction with no enhancement was noted. Cystic compartments contained hemorrhage with fluid-fluid level formation. The patient undertook lumpectomy. CONCLUSION: For a giant breast tumor with mixed solid and cystic component, intraductal papilloma should be considered in the differential diagnosis.

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.

Evaluation of the role of dynamic contrast-enhanced MR imaging for patients with BI-RADS 3-4 microcalcifications.

OBJECTIVE: The purpose of study was to prospectively evaluate the diagnostic performance of dynamic contrast-enhanced MR imaging in the differentiation of malignant lesions from benign ones in patients with BI-RADS 3-4 microcalcifications detected by mammography. MATERIALS AND METHODS: 93 women with 100 microcalcifications had undergone breast MRI from June 2010 to July 2013. Subsequently, 91 received open biopsy and 2 received stereotactic vacuum-assisted biopsy. All results were compared with histological findings. The PPV, NPV and area under curve (AUC) of the mammography and breast MRI were calculated. RESULTS: There were 31 (31.0%) BI-RADS 3 microcalcifications and 69 (69.0%) BI-RADS 4. The PPV and NPV of mammography is 65.2% (45/69) and 90.3% (28/31). The PPV and NPV of breast MRI was 90.2% (46/51) and 95.9% (47/49). Among 31 BI-RADS 3 microcalcifications, the PPV and NPV of breast MRI was 100% (3/3) and 100% (28/28). Among 69 BI-RADS 4 microcalcifications, the PPV and NPV of breast MRI was 89.6% (43/48) and 90.5% (19/21). The AUC of mammography and breast MRI assessment were 0.738 (95% CI, 0.639-0.837) and 0.931 (95% CI, 0.874-0.988) (p<0.05). CONCLUSION: Dynamic contrast-enhanced MR imaging of breast is able to be applied to predict the risk of malignance before follow-up for BI-RADS 3 microcalcifications and biopsy for BI-RADS 4 microcalcifications.