How to manage type 2 curve dilemma in dynamic contrast-enhanced magnetic resonance imaging of the breast: diffusion-weighted imaging or early phase enhancement kinetics?

BACKGROUND: Type 2 time-intensity curves can indicate both malignant and benign breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). PURPOSE: To investigate whether diffusion-weighted imaging (DWI) or early phase kinetics of DCE-MRI is practical to discriminate breast masses that depict type 2 curve in DCE-MRI. MATERIAL AND METHODS: We retrospectively included 107 lesions in 97 patients with type 2 curves in DCE-MRI. Morphological characteristics, early phase dynamic parameters on DCE-MRI, and apparent diffusion coefficient (ADC) values on DWI were evaluated. Diagnostic thresholds of ADC and early phase maximum enhancement ratio (EPMER) to distinguish between benign and malignant masses were calculated. Strongest predictors of malignancy were determined to build the most effective diagnostic model. RESULTS: DWI, EPMER, and all morphological features were found statistically significant to discriminate malignancy (P <0.05). The thresholds of ADC and EPMER were assigned as 1.0 ×10-3 mm2/s and 72%, respectively. The sensitivity and specificity were 80% and 97% for ADC, and 93% and 60% for EPMER, respectively. Two models were established. Model 1 comprised ADC and the lesion margin. Model 2 consisted of ADC, margin, and EPMER with a high specificity (99%) and positive predictive value (97%). CONCLUSION: When combined with DWI, early phase wash-in data provide diagnostic improvement of breast masses presenting type 2 curve in the late phase of DCE-MRI, especially for specificity. Future studies are required to support our findings for the need of a cross-validation.

Dynamic contrast-enhanced mammography and breast MRI in the diagnosis of breast cancer and detection of tumor size.

Background/aim: The aim of this study is to evaluate the performance of contrast-enhanced mammography (CEM) and dynamic breast MRI techniques for diagnosing breast lesions, assess the diagnostic accuracy of CEM's using histopathological findings, and compare lesion size measurements obtained from both methods with pathological size. Materials and methods: This prospective study included 120 lesions, of which 70 were malignant, in 104 patients who underwent CEM and MRI within a week. Two radiologists independently evaluated the MR and CEM images in separate sessions, using the BI-RADS classification system. Additionally, the maximum sizes of lesion were measured. Diagnostic accuracy parameters and the receiver operating characteristics (ROC) curves were constructed for the two modalities. The correlation between the maximum diameter of breast lesions observed in MRI, CEM, and pathology was analyzed. Results: The overall diagnostic values for MRI were as follows: sensitivity 97.1%, specificity 60%, positive predictive value (PPV) 77.3%, negative predictive value (NPV) 93.8%, and accuracy 81.7%. Correspondingly, for CEM, the sensitivity, accuracy, specificity, PPV, and NPV were 97.14%, 81.67%, 60%, 77.27%, and 93.75%, respectively. The ROC analysis of CEM revealed an area under the curve (AUC) of 0.907 for observer 1 and 0.857 for observer 2, whereas MRI exhibited an AUC of 0.910 for observer 1 and 0.914 for observer 2. Notably, CEM showed the highest correlation with pathological lesion size (r = 0.660 for observer 1 and r = 0.693 for observer 2, p < 0.001 for both). Conclusion: CEM can be used with high sensitivity and similar diagnostic performance comparable to MRI for diagnosing breast cancer. CEM proves to be a successful diagnostic method for precisely determining tumor size.

Disparities in the use of screening breast magnetic resonance imaging persist in Louisiana after the Affordable Care Act: A question of access, policy, institutional support, or something else?

BACKGROUND: Louisiana continues to have one of the highest breast cancer mortality rates in the nation, and Black women are disproportionally affected. Louisiana has made advances in improving access to breast cancer screening through the expansion of Medicaid. There remains, however, broad underuse of advanced imaging technology such as screening breast magnetic resonance imaging (MRI), particularly for Black women. METHODS: Breast MRI has been proven to be very sensitive for the early detection of breast cancer in women at high risk. MRI is more sensitive than mammography for aggressive, invasive breast cancer types, which disproportionally affect Black women. Here the authors identify potential barriers to breast MRI screening in Black women, propose strategies to address disparities in access, and advocate for specific recommendations for change. RESULTS: Cost was identified as one of the greatest barriers to screening breast MRI. The authors propose implementation of cost-saving, abbreviated protocols to address cost along with lobbying for further expansion of the Affordable Care Act (ACA) to include coverage for screening breast MRI. In addition, addressing gaps in communication and knowledge and facilitating providers' ability to readily identify women who might benefit from MRI could be particularly impactful for high-risk Black women in Louisiana communities. CONCLUSIONS: Since the adoption of the ACA in Louisiana, Black women have continued to have disproportionally high breast cancer mortality rates. This persistent disparity provides evidence that additional change is needed. This change should include exploring innovative ways to make advanced imaging technology such as breast MRI more accessible and expanding research to specifically address community and culturally specific barriers.

Prediction of nipple involvement in breast cancer after neoadjuvant chemotherapy: Should we rely on breast MRI to preserve the nipple?

BACKGROUND: Indications for nipple sparing mastectomy (NSM) is extending to post-neoadjuvant chemotherapy (NAC) setting. Eligibility for NSM with an optimum tumor-nipple distance (TND) after NAC is unclear. We examined predictive factors for nipple tumor involvement in patients undergoing total mastectomy following NAC. METHODS: Clinical and pathological data from prospectively collected medical records of women with invasive breast carcinoma, who were undergone NAC and total mastectomy with sentinel lymph node biopsy and/or axillary lymph node dissection were analyzed. PreNAC and postNAC magnetic resonance imaging (MRI) views were examined and a cut-off TND value for predicting the negative nipple tumor status was determined. RESULTS: Among 180 women, the final mastectomy specimen analysis revealed that 12 (7%) had nipple involvement as invasive carcinoma. Patients with nipple involvement had more postNAC multifocal/multicentric tumors (p: 0.03), larger tumors on preNAC and postNAC images (p: 0.002 and p < 0.001), shorter median TNDs on preNAC and postNAC images (7 mm-IQR 1.5-14, p: 0.005 and 8.5 mm-IQR 3-15.5, p < 0.001, respectively), more nipple retraction on preNAC and postNAC images (p: 0.007 and p: 0.006) and more nipple areola complex skin thickening (> 2mm) on preNAC and postNAC images (p < 0.001 and p: 0.01). The best likelihood ratios (LR) belonged to the postNAC positivity of the < 20 mm TND, with a + LR of 3.40, and - LR of 0.11 for nipple involvement. PreNAC positivity of the < 20 mm TND also had a similar - LR of 0.14. CONCLUSION: A TND-cut-off  ≥ 2 cm on preNAC and postNAC MRI was shown to be highly predictive of negative nipple tumor involvement.

A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.

BACKGROUND: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. PURPOSE: To develop an unenhanced radiomics model with using non-gadolinium based sequences for detecting breast cancer based on T2-weighted (T2W) and diffusion-weighted (DW) MRI. STUDY TYPE: Retrospective analysis followed by retrospective and prospective cohorts study. POPULATION: 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. FIELD STRENGTH/SEQUENCE: 3.0T MR scanners from two institution. T2WI, DWI, and first contrast-enhanced T1-weighted sequence. ASSESSMENT: AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, weighted kappa test, and DeLong's test. RESULTS: The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818-0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317-0.816), as well as the two radiologists (P, 0.181-0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. DATA CONCLUSION: An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Gadoteridol-enhanced MRI of the breast: can contrast agent injection rate impact background parenchymal enhancement?

BACKGROUND: Normal background parenchymal enhancement (BPE) is a dynamic parameter affected by multiple factors. PURPOSE: To determine whether contrast agent injection rate affects the degree of BPE in women undergoing breast magnetic resonance imaging (MRI). MATERIAL AND METHODS: A total of 85 patients included in our prospective study randomly received 0.1 mmol/kg gadoteridol at a rate of 3 mL/s (group A; n = 46) or 2 mL/s (group B; n = 39). Breast MRI was performed at 3T using a standard protocol including postcontrast axial 3D GRE T1-weighted sequences. Two expert breast radiologists, blinded to clinical and radiological information, independently quantified BPE on early postcontrast subtracted images, assigning a score of 1-4. Mean comparison and regression analysis were performed to assess the influence of injection rate on BPE. RESULTS: Groups were homogeneous in terms of age and final BI-RADS score. The mean BPE score was significantly lower among patients in group A (mean of two readers: 1.36 vs. 1.90; P < 0.01) with 70%-72% of patients assigned a BPE score of 1, compared with 36%-38% of patients in group B. Lower BPE scores were noted with the higher flow rate in subgroup analyses of both pre- and postmenopausal women, although the effect was more evident in premenopausal women. Regression analysis confirmed that the likelihood of a BPE 1 score was significantly increased with a higher flow rate (P < 0.01). The inter-reader agreement was excellent (0.83). CONCLUSION: A higher contrast agent injection flow rate (3 mL/s) during breast MRI significantly reduces the degree of BPE, potentially allowing improved diagnostic accuracy by reducing false-positive and false-negative findings.

Deep learning in breast radiology: current progress and future directions.

This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be integrated not only into detection and classification of breast lesions, but also into areas such as risk estimation and prediction of tumor responses to therapy. Remaining challenges include limited availability of high-quality data with expert annotations and ground truth determinations, the need for further validation of initial results, and unresolved medicolegal considerations. KEY POINTS: • Deep learning (DL) continues to push the boundaries of what can be accomplished by artificial intelligence (AI) in breast imaging with distinct advantages over conventional computer-aided detection. • DL-based AI has the potential to augment the capabilities of breast radiologists by improving diagnostic accuracy, increasing efficiency, and supporting clinical decision-making through prediction of prognosis and therapeutic response. • Remaining challenges to DL implementation include a paucity of prospective data on DL utilization and yet unresolved medicolegal questions regarding increasing AI utilization.

Does the Addition of Breast MRI Add Value to the Diagnostic Workup of Invasive Lobular Carcinoma?

BACKGROUND: Invasive lobular carcinoma (ILC) has unique histologic growth pattern. Few studies have focused on the value of breast magnetic resonance imaging (MRI) specifically for ILC. We hypothesized that MRI adds value to the diagnostic workup in ILC by better defining the extent of disease and identifying additional foci of malignancy, which can change the surgical plan. MATERIALS AND METHODS: This was a single-institution retrospective review of women diagnosed with ILC from 1/2012 to 7/2019 who underwent preoperative MRI. Patient, tumor characteristics, and initial surgical plan were reviewed. MRI had added value if ILC size correlated best to final pathologic size or if additional malignancy was identified. MRI was considered harmful if additional biopsies were benign or if the size was overestimated. RESULTS: ILC was identified in 166 breasts in 165 women. Original surgical plan was for lumpectomy in 86 (52%), mastectomy in 49 (30%), and undecided in 31 (18%). MRI changed the plan in 25 (19%) with 24 (96%) changing from lumpectomy to mastectomy. Additional biopsy was performed in 28% after MRI, the majority (n = 41, 72%) were benign or high risk and 16 (28%) identified additional malignancy. MRI was not a better size estimate than mammogram/ultrasound. Re-excision rate after lumpectomy was 6.8% (5/73). MRI added value in 48 (28.9%) and was harmful in 48 (28.9%). CONCLUSIONS: Using breast MRI in the diagnostic workup of ILC has both positive and negative implications on surgical treatment planning. A shared decision-making conversation is warranted before proceeding with MRI to maximize value and minimize harms associated with this diagnostic tool.

Breast cancer and background parenchymal enhancement at breast magnetic resonance imaging: a meta-analysis.

BACKGROUND: The background parenchymal enhancement at breast magnetic resonance imaging use to predict breast cancer attracts many searchers to draw a possible relationship. However, the results of their relationships were conflicting. This meta-analysis was performed to assess breast cancer frequency associations with background parenchymal enhancement. METHODS: A systematic literature search up to January 2020 was performed to detect studies recording associations between breast cancer frequency and background parenchymal enhancement. We found thirteen studies including 13,788 women at the start with 4046 breast cancer. We calculated the odds ratio (OR) and the 95% confidence intervals (CIs) between breast cancer frequency and background parenchymal enhancement by the dichotomous technique with a random or fixed-effect model. RESULTS: Women with minimal or mild background parenchymal enhancement at breast magnetic resonance imaging did not have any risk of breast cancer compared to control women (OR, 1.20; 95% CI 0.54-2.67). However, high background parenchymal enhancement at breast magnetic resonance imaging (OR, 2.66; 95% CI 1.36-5.19) and moderate (OR, 2.51; 95% CI 1.49-4.21) was associated with a significantly higher rate of breast cancer frequency compared to control women. CONCLUSIONS: Our meta-analysis showed that the women with high and moderate background parenchymal enhancement at breast magnetic resonance imaging have higher risks, up to 2.66 fold, of breast cancer. We suggest that women with high or moderate background parenchymal enhancement at breast magnetic resonance imaging to be scheduled for more frequent follow-up and screening for breast cancer to avoid any complications.

Diagnostic efficacy of contrast-enhanced breast MRI versus X-ray mammography in women with different degrees of breast density.

BACKGROUND: Detection of breast cancer in women with high breast densities is a clinical challenge. PURPOSE: To study the influence of different degrees of breast density on the sensitivity of contrast-enhanced breast magnetic resonance imaging (CE-BMRI) versus X-ray mammography (XRM). MATERIAL AND METHODS: We performed an additional analysis of two large Phase III clinical trials (G1; G2) which included women with histologically proven breast cancers, called "index cancers." Additional cancers were detected during image reading. We compared the sensitivity of CE-BMRI and XRM in women with different breast densities (ACR A→D; Version 5). For each study, six blinded readers evaluated the images. Results are given as the "Median Reader." RESULTS: A total of 774 patients were included, 169 had additional cancers. While sensitivity of CE-BMRI for detecting all index cancers was independent of breast density (ACR A→D) (G1: 83%→83%; G2: 91%→91%) the sensitivity of XRM declined (ACR A→D) (G1: 79%→62%; G2: 82%→64%). Thus, the sensitivity difference between both imaging modalities in ACR A breasts of 3% (G1) and 9% (G2) increased to 21% (G1) and 26% (G2) in ACR D breasts. Sensitivity of CE-BMRI for detecting at least one additional cancer increased with increasing breast density (ACR A→D) (G1: 50%→73%, G2: 57%→81%). XRM's sensitivity decreased (G1: 34%→20%) or remained stable (G2: 24%→25%). CONCLUSION: CE-BMRI showed significantly higher sensitivity compared to XRM.

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization.

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.

Lobular neoplasia occult on conventional imaging and diagnosed on MRI-guided biopsy: can we estimate upgrade on surgical pathology?

PURPOSE: The goal of this study is to evaluate the frequency and imaging features of lobular neoplasia (LN) diagnosed on MRI-guided biopsy, determine the upgrade rate to malignancy, and assess for any features that may be associated with an upgrade on surgical excision. MATERIALS AND METHODS: Research ethical board approved the review of consecutive patients with MRI-detected LN between January 2009 and December 2018 with differentiation between pure LN and LN with associated other high-risk lesions. The final outcome was determined by final pathology results from surgical excision or 24 months of follow-up. Appropriate statistical tests were used. RESULTS: Out of 1250 MRI-guided biopsies performed, 76 lesions (6%) fulfilled the inclusion criteria and formed the study cohort. Of the 76 lesions, 54 (71%) were pure LN while the rest had coexistent high-risk lesion. Non-mass enhancement (NME) was the most common lesion type (62, 82%). Fifty-nine lesions (78%) were surgically excised, the other 17 had benign follow-up. Overall, 8 lesions (11%) were upgraded to malignancy on final pathology. Malignant outcome was associated with larger lesion size (5.5 versus 1.9 cm, P < 0.001) and a clumped NME pattern (75% versus 24%, P = 0.006). Lesion size and clumped NME remained significantly associated with upgrade on sub-analysis of the pure LN group. CONCLUSION: Larger lesion size and clumped NME are imaging findings associated with upgrade of LN diagnosed by MRI-guided biopsy. This may influence patient management in this clinical setting. Additional larger studies are needed to consolidate our results and to potentially detect additional factors associated with upgrade.

One-millimeter isotropic breast diffusion-weighted imaging: Evaluation of a superresolution strategy in terms of signal-to-noise ratio, sharpness and apparent diffusion coefficient.

PURPOSE: To quantitatively evaluate a superresolution technique for 3D, one-millimeter isotropic diffusion-weighted imaging (DWI) of the whole breasts. METHODS: Isotropic 3D DWI datasets are obtained using a combination of (i) a readout-segmented diffusion-weighted-echo-planar imaging (DW-EPI) sequence (rs-EPI), providing high in-plane resolution, and (ii) a superresolution (SR) strategy, which consists of acquiring 3 datasets with thick slices (3 mm) and 1-mm shifts in the slice direction, and combining them into a 1 × 1 × 1-mm3 dataset using a dedicated reconstruction. Two SR reconstruction schemes were investigated, based on different regularization schemes: conventional Tikhonov or Beltrami (an edge-preserving constraint). The proposed SR strategy was compared to native 1 × 1 × 1-mm3 acquisitions (i.e. with 1-mm slice thickness) in 8 healthy subjects, in terms of signal-to-noise ratio (SNR) efficiency, using a theoretical framework, Monte Carlo simulations and region-of-interest (ROI) measurements, and image sharpness metrics. Apparent diffusion coefficient (ADC) values in normal breast tissue were also compared. RESULTS: The SR images resulted in an SNR gain above 3 compared to native 1 × 1 × 1-mm3 using the same acquisition duration (acquisition gain 3 and reconstruction gain >1). Beltrami-SR provided the best results in terms of SNR and image sharpness. The ADC values in normal breast measured from Beltrami-SR were preserved compared to low-resolution images (1.91 versus 1.97 ×10-3 mm2 /s, P = .1). CONCLUSION: A combination of rs-EPI and SR allows 3D, 1-mm isotropic breast DWI data to be obtained with better SNR than a native 1-mm isotropic acquisition. The proposed DWI protocol might be of interest for breast cancer monitoring/screening without injection.

Patterns of Breast Imaging Use Among Women with a Personal History of Breast Cancer.

BACKGROUND: National patterns of breast imaging in women with a personal history of breast cancer (PHBC) are unknown making evaluation of annual surveillance recommendations a challenge. OBJECTIVE: To describe variation in use of mammography and breast magnetic resonance imaging (MRI) examinations beginning 6 months after diagnosis among women with PHBC in US community practice. We report on the breast imaging indication, imaging intervals, and time since breast cancer diagnosis by examination type. DESIGN: Longitudinal study using cross-sectional data. SETTING: Breast Cancer Surveillance Consortium breast imaging facilities. PARTICIPANTS: 19,955 women diagnosed between 2005 and 2012 with AJCC stage 0-III incident breast cancer who had 69,386 mammograms and 3,553 breast MRI examinations from January 2005 to September 2013; median follow-up of 37.6 months (interquartile range, 22.1-60.7). MAIN MEASURES: Breast imaging indication, imaging intervals, and time since breast cancer diagnosis by examination type. KEY RESULTS: Among women with a PHBC who received breast imaging, 89.4% underwent mammography alone, 0.8% MRI alone, and 10.3% had both mammography and MRI. About half of mammograms and MRIs were indicated for surveillance vs. diagnostic, with an increase in the proportion of surveillance exams as time from diagnosis increased (mammograms, 45.7% at 1 year to 72.2% after 5 years; MRIs, 54.8% at 1 year to 78.6% after 5 years). In the first post-diagnosis period, 32.8% of women had > 2 breast imaging examinations and of these, 65.8% were less than 6 months apart. During the first 5-year post-diagnosis, the frequency of examinations per year decreased and the interval between examinations shifted towards annual examinations. CONCLUSION: In women with a PHBC who received post-diagnosis imaging, a third underwent multiple breast imaging examinations per year during the first 2-year post-diagnosis despite recommendations for annual exams. As time since diagnosis increases, imaging indication shifts from diagnostic to surveillance.

MRI breast screening revisited.

Contrast-enhanced breast MRI is a standard evidence-based component of supplemental screening in conjunction with mammography for higher-risk populations because of its high sensitivity for detecting breast cancer; the use of breast MRI for screening in high-risk populations is recommended in multiple national and international guidelines. The current MRI exam, however, is expensive relative to other screening technologies such as mammography, and relatively more time-intensive. Recent investigations have focused on techniques that have the potential for improving efficiency of the breast MRI exam, decreasing acquisition and reading times, without impacting diagnostic accuracy. The purpose of this article is therefore to provide an overview of current MRI guidelines for screening for breast cancer and to discuss evolving technological approaches to breast MRI, in particular abbreviated and ultrafast MRI protocols, as well as MRI protocols without contrast that have the potential to improve MRI screening. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;49:1212-1221.

Factors associated with breast MRI use among women with a family history of breast cancer.

Although annual breast magnetic resonance imaging (MRI) is recommended for women at high risk for breast cancer as an adjunct to screening mammography, breast MRI use remains low. We examined factors associated with breast MRI use in a cohort of women with a family history of breast cancer but no personal cancer history. Study participants came from the Sister Study cohort, a nationwide, prospective study of women with at least 1 sister who had been diagnosed with breast cancer but who themselves had not ever had breast cancer (n = 17 894). Participants were surveyed on breast cancer beliefs, cancer worry, breast MRI use, provider communication, and genetic counseling and testing. Logistic regression was used to assess factors associated with having a breast MRI overall and for those at high risk. Breast MRI was reported by 16.1% and was more common among younger women and those with higher incomes. After adjustment for demographics, ever use of breast MRI was associated with actual and perceived risk. Odds ratios (OR) were 12.29 (95% CI, 8.85-17.06), 2.48 (95% CI, 2.27-2.71), and 2.50 (95% CI, 2.09-2.99) for positive BRCA1/2 test, lifetime breast cancer risk ≥ 20%, and being told by a health care provider of higher risk, respectively. Women who believed they had much higher risk than others or had higher level of worry were twice as likely to have had breast MRI; OR = 2.23 (95% CI, 1.82-2.75) and OR = 1.76 (95% CI, 1.52-2.04). Patterns were similar among women at high risk. Breast cancer risk, provider communication, and personal beliefs were determinants of breast MRI use. To support shared decisions about the use of breast MRI, women could benefit from improved understanding of the chances of getting breast cancer and increased quality of provider communications.

Use of Breast Magnetic Resonance Imaging in Women Diagnosed With Atypical Ductal Hyperplasia at Core Needle Biopsy Helps Select Women for Surgical Excision.

PURPOSE: The study sought to investigate the role of breast magnetic resonance imaging (MRI) in patients diagnosed with atypical ductal hyperplasia (ADH) at core needle biopsy (CNB). METHODS: The breast MRI database at our centre was queried for studies performed between January 2010 and December 2016 for the clinical indication of ADH diagnosed at CNB. Medical files were reviewed for demographic data, clinical information, and radiology and pathology reports. Pathological results of the surgical specimens were considered the gold standard for comparison with breast MRI findings. In women not undergoing excision, at least 2 years of follow-up was used to ascertain the benign nature of the finding. RESULTS: Fifty patients were included in the study. Thirty-one (62%) patients had surgical excision of the ADH lesion, and 7 (23%) were upgraded to malignancy. Breast MRI accurately identified 6 of the 7 cases. Six of the 12 women (50%) with positive MRI findings at the biopsy site were upgraded to malignancy on surgical pathology, compared with only 1 of 19 (5%) with negative MRI findings. Forty-nine percent of the women with a negative MRI did not undergo surgical excision of the ADH lesion, compared with 8% of the women with a positive MRI (P = .009), with no cancer diagnosed during follow-up. The sensitivity, specificity, negative predictive value, and positive predictive value of breast MRI for predicting upgrade to malignancy were 86%, 83%, 97%, and 46%, respectively. CONCLUSIONS: MRI may have a role in the management of women diagnosed with ADH on CNB, to minimize diagnostic excisional biopsies.

Performance evaluation of breast cancer diagnosis with mammography, ultrasonography and magnetic resonance imaging.

OBJECTIVE: Various imaging modalities have been used to diagnose suspicious breast lesions. Purpose of this study is to compare the diagnostic accuracy for breast cancer using mammography, ultrasonography and magnetic resonance imaging (MRI). METHODS: Total 107 patients aged from 19 to 62 years are included in this retrospective study. Mammography, ultrasonography and MRI scans were performed for each patient detected with suspected breast tumor within a month. In addition, the tumor diversity (10 types of benign and 5 types of malignant) was confirmed by pathological findings of tumor biopsy. To compare the diagnosis performance of the three imaging modalities, the overall fraction correct (accuracy), positive predict value (PPV), negative predict value (NPV), sensitivity and specificity were calculated. Meanwhile, the receiver operating characteristic (ROC) analysis was also performed. RESULTS: The diagnostic accuracy ranged from 78.5% to 86.9% among three imaging modalities. All modalities yielded a PPV lower than 77.8% and a NPV higher than 90.0% in identifying the presence of malignant tumors. MRI presented a diagnostic accuracy of 86.9%, as well as a sensitivity of 95.5% and an area under curve (AUC) of 0.948, which are higher than mammography and ultrasonography. CONCLUSION: By using a diverse dataset and comparing the diagnostic accuracy of three imaging modalities commonly used in breast cancer detection and diagnosis, this study also demonstrated that mammography, ultrasonography and MRI had different diagnostic performance in breast tumor identification. Among them, MRI yielded the highest performance even though the unexpected specificity may lead to over-diagnosis, and ultrosonography is slightly better than mammography.

[Comparison of Kinetic Curve between Gadodiamide Hydrate and Gadobutrol on Breast Dynamic Contrast-enhanced Magnetic Resonance Imaging in Invasive Ductal Carcinoma].

The shape of the kinetic curve for gadobutrol is reportedly different compared with that for other conventional contrast agents. We speculate that the shape of gadobutrol kinetic curve may be influenced by different magnetic resonance imaging (MRI) protocols and evaluation methods. The purpose of our study was to assess the influence between gadobutrol and other conventional contrast agent (gadodiamide hydrate) on the kinetic curve in invasive ductal carcinoma (IDC). We assessed 139 women of IDC in this study. Gadodiamide hydrate (2 ml/s) was administered to 69 women, and gadobutrol (1 ml/s) was administrated to 70 women, both contrast agents at 0.1 mmol/kg BW. When the kinetic curves of contrast agents were evaluated between by Breast Imaging Reporting and Data System (BI-RADS) 4th edition and BI-RADS 5th edition, suggested that the analysis method of BI-RADS may affect. Patient group who were administered gadobutrol demonstrated a lower washout rate when compared with patient group who were administered gadodiamide hydrate administration (P<0.01). These results suggest that the kinetic curve characteristics of gadobutrol are an important consideration in diagnosis. Therefore, it is necessary to perform image diagnosis by considering the influence of the contrast agent and the analysis method, when image diagnostic doctor perform image diagnosis.

Magnetic resonance imaging tumor regression shrinkage patterns after neoadjuvant chemotherapy in patients with locally advanced breast cancer: Correlation with tumor biological subtypes and pathological response after therapy.

The objective of this study is to analyze magnetic resonance imaging shrinkage pattern of tumor regression after neoadjuvant chemotherapy and to evaluate its relationship with biological subtypes and pathological response. We reviewed the magnetic resonance imaging studies of 51 patients with single mass-enhancing lesions (performed at time 0 and at the II and last cycles of neoadjuvant chemotherapy). Tumors were classified as Luminal A, Luminal B, HER2+, and Triple Negative based on biological and immunohistochemical analysis after core needle biopsy. We classified shrinkage pattern, based on tumor regression morphology on magnetic resonance imaging at the II cycle, as concentric, nodular, and mixed. We assigned a numeric score (0: none; 1: low; 2: medium; 3: high) to the enhancement intensity decrease. Pathological response on the surgical specimen was classified as complete (grade 5), partial (grades 4-3), and non-response (grades 1-2) according to Miller and Payne system. Fisher test was used to relate shrinkage pattern with biological subtypes and final pathological response. Seventeen patients achieved complete response, 25 partial response, and 9 non-response. A total of 13 lesions showed nodular pattern, 20 concentric, and 18 mixed. We found an association between concentric pattern and HER2+ (p < 0.001) and mixed pattern and Luminal A lesions (p < 0.001). We observed a statistical significant correlation between concentric pattern and complete response (p < 0.001) and between mixed pattern and non-response (p = 0.005). Enhancement intensity decrease 3 was associated with complete response (p < 0.001). Shrinkage pattern and enhancement intensity decrease may serve as early response indicators after neoadjuvant chemotherapy. Shrinkage pattern correlates with tumor biological subtypes.