Diagnostic breast MR imaging: current status and future directions

Background Parenchymal Enhancement: A Comprehensive Update

Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.

Application value of MRI-guided wire localization to the non-palpable breast lesions only shown in Breast MRI

Introduction

Magnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women.

Methods

A total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed.

Results

All of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P<0.05). All the lesions were assessed according to BI-RADS category of MRI (C4A=77, C4B=40, C4C=12, C5=2). The malignancy rates were as follows: 16.88% for 4A lesions (13/77), 37.50% for 4B lesions (15/40), 75.00% for 4C lesions (9/12) and 100% for 5 lesions (2/2). There was a significant correlation between the BI-RADS category and the incidence of benign-to-malignant lesions (P<0.001).

Conclusion

MRI-guided wire localization can assist to remove suspected breast lesions early, safely and accurately. This technique makes up for the deficiency of X-ray and ultrasound, improves the accuracy of diagnosis and resection therapy in intraductal carcinoma and early invasive carcinoma, and helps to improve the the prognosis of breast cancer.

Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women

OBJECTIVE

To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM).

METHODS

Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk.

RESULTS

During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001).

CONCLUSION

Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females.

ADVANCES IN KNOWLEDGE

• Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years. • The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.

Can magnetic resonance imaging texture analysis change the breast imaging reporting and data system category of breast lesions?

PURPOSE

This study aimed to reveal magnetic resonance imaging (MRI) texture analysis (TA)'s contribution to categorizing breast lesions according to the Breast Imaging-Reporting and Data System (BI-RADS) lexicon.

METHOD

Two hundred and seventeen women with BI-RADS category 3, 4, and 5 lesions on breast MRI were included in the study. For TA, the region of interest was drawn manually to encompass the entire lesion on the fat-suppressed T2W and the first post-contrast T1W images. To identify the independent predictors of breast cancer, multivariate logistic regression analyses were performed using texture parameters. Estimated benign and malignant groups were formed according to the TA regression model.

RESULTS

Texture parameters extracted from T2WI, including median, gray-level co-occurrence matrix (GLCM) contrast, GLCM correlation, GLCM joint entropy, GLCM sum entropy, and GLCM sum of squares, and parameters extracted from T1WI, including maximum, GLCM contrast, GLCM joint entropy, GLCM sum entropy, were independent predictors of breast cancer. In the estimated new groups according to the TA regression model, 19 (91%) of the benign 4a lesions were downgraded to BI-RADS category 3.

CONCLUSIONS

The addition of quantitative parameters obtained by MRI TA to BI-RADS criteria significantly increased the accuracy rate in differentiating benign and malignant breast lesions. When categorizing BI-RADS 4a lesions, the use of MRI TA in addition to conventional imaging findings may reduce unnecessary biopsy rates.

Characterization of breast lesions using multi-parametric diffusion MRI and machine learning

Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andβand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofβ;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.

MRI background parenchymal enhancement, breast density and breast cancer risk factors: A cross-sectional study in pre- and post-menopausal women

Breast tissue enhances on contrast MRI and is called background parenchymal enhancement (BPE). Having high BPE has been associated with an increased risk of breast cancer. We examined the relationship between BPE and the amount of fibroglandular tissue on MRI (MRI-FGT) and breast cancer risk factors. This was a cross-sectional study of 415 women without breast cancer undergoing contrast-enhanced breast MRI at Memorial Sloan Kettering Cancer Center. All women completed a questionnaire assessing exposures at the time of MRI. Prevalence ratios (PR) and 95% confidence intervals (CI) describing the relationship between breast cancer risk factors and BPE and MRI-FGT were generated using modified Poisson regression. In multivariable-adjusted models a positive association between body mass index (BMI) and BPE was observed, with a 5-unit increase in BMI associated with a 14% and 44% increase in prevalence of high BPE in pre- and post-menopausal women, respectively. Conversely, a strong inverse relationship between BMI and MRI-FGT was observed in both pre- (PR = 0.66, 95% CI 0.57, 0.76) and post-menopausal (PR = 0.66, 95% CI 0.56, 0.78) women. Use of preventive medication (e.g., tamoxifen) was associated with having low BPE, while no association was observed for MRI-FGT. BPE is an imaging marker available from standard contrast-enhanced MRI, that is influenced by endogenous and exogenous hormonal exposures in both pre- and post-menopausal women.

The progesterone-receptor modulator, ulipristal acetate, drastically lowers breast cell proliferation

PURPOSE

The proliferation of breast epithelial cells increases during the luteal phase of the menstrual cycle, when they are exposed to progesterone, suggesting that ulipristal acetate, a selective progestin-receptor modulator (SPRM), may reduce breast cell proliferation with potential use in breast cancer chemoprevention.

METHODS

Women aged 18-39 were randomized 1:1 to ulipristal 10-mg daily or to a combination oral contraceptive (COC) for 84 days. Participants underwent a breast biopsy and breast MRI at baseline and at end of study treatment. Proliferation of breast TDLU cells was evaluated by Ki67 immunohistochemical stain. We evaluated the breast MRIs for background parenchymal enhancement (BPE). All slides and images were masked for outcome evaluation.

RESULTS

Twenty-eight treatment-compliant participants completed the study; 25 of whom had evaluable Ki67 results at baseline and on-treatment. From baseline to end of treatment, Ki67 % positivity (Ki67%+) decreased a median of 84% in the ulipristal group (N = 13; 2-sided p (2p) = 0.040) versus a median increase of 8% in the COC group (N = 12; 2p = 0.85). Median BPE scores decreased from 3 to 1 in the ulipristal group (p = 0.008) and did not decrease in the COC group.

CONCLUSION

Ulipristal was associated with a major decrease in Ki67%+ and BPE. Ulipristal would warrant further investigation for breast cancer chemoprevention were it not for concerns about its liver toxicity. Novel SPRMs without liver toxicity could provide a new approach to breast cancer chemoprevention.

TRIAL REGISTRATION

NCT02922127, 4 October 2016.

Multispectral Imaging for Metallic Biopsy Marker Detection During MRI-Guided Breast Biopsy: A Feasibility Study for Clinical Translation

Purpose

To assess the feasibility and diagnostic accuracy of multispectral MRI (MSI) in the detection and localization of biopsy markers during MRI-guided breast biopsy.

Methods

This prospective study included 20 patients undergoing MR-guided breast biopsy. In 10 patients (Group 1), MSI was acquired following tissue sampling and biopsy marker deployment. In the other 10 patients (Group 2), MSI was acquired following tissue sampling but before biopsy marker deployment (to simulate deployment failure). All patients received post-procedure mammograms. Group 1 and Group 2 designations, in combination with the post-procedure mammogram, served as the reference standard. The diagnostic performance of MSI for biopsy marker identification was independently evaluated by two readers using two-spectral-bin MR and one-spectral-bin MR. The κ statistic was used to assess inter-rater agreement for biopsy marker identification.

Results

The sensitivity, specificity, and accuracy of biopsy marker detection for readers 1 and 2 using 2-bin MSI were 90.0% (9/10) and 90.0% (9/10), 100.0% (10/10) and 100.0% (10/10), 95.0% (19/20) and 95.0% (19/20); and using 1-bin MSI were 70.0% (7/10) and 80.0% (8/10), 100.0% (8/8) and 100.0% (10/10), 85.0% (17/20) and 90.0% (18/20). Positive predictive value was 100% for both readers for all numbers of bins. Inter-rater agreement was excellent: κ was 1.0 for 2-bin MSI and 0.81 for 1-bin MSI.

Conclusion

MSI is a feasible, diagnostically accurate technique for identifying metallic biopsy markers during MRI-guided breast biopsy and may eliminate the need for a post-procedure mammogram.

MRI background parenchymal enhancement, fibroglandular tissue, and mammographic breast density in patients with invasive lobular breast cancer on adjuvant endocrine hormonal treatment: associations with survival

BACKGROUND

To investigate if baseline and/or changes in contralateral background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) measured on magnetic resonance imaging (MRI) and mammographic breast density (MD) can be used as imaging biomarkers for overall and recurrence-free survival in patients with invasive lobular carcinomas (ILCs) undergoing adjuvant endocrine treatment.

METHODS

Women who fulfilled the following inclusion criteria were included in this retrospective HIPAA-compliant IRB-approved study: unilateral ILC, pre-treatment breast MRI and/or mammography from 2000 to 2010, adjuvant endocrine treatment, follow-up MRI, and/or mammography 1-2 years after treatment onset. BPE, FGT, and mammographic MD of the contralateral breast were independently graded by four dedicated breast radiologists according to BI-RADS. Associations between the baseline levels and change in levels of BPE, FGT, and MD with overall survival and recurrence-free survival were assessed using Kaplan-Meier survival curves and Cox regression analysis.

RESULTS

Two hundred ninety-eight patients (average age = 54.1 years, range = 31-79) fulfilled the inclusion criteria. The average follow-up duration was 11.8 years (range = 2-19). Baseline and change in levels of BPE, FGT, and MD were not significantly associated with recurrence-free or overall survival. Recurrence-free and overall survival were affected by histological subtype (p < 0.0001), number of metastatic axillary lymph nodes (p < 0.0001), age (p = 0.01), and adjuvant endocrine treatment duration (p < 0.001).

CONCLUSIONS

Qualitative evaluation of BPE, FGT, and mammographic MD changes cannot predict which patients are more likely to benefit from adjuvant endocrine treatment.

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm

To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-approved study was performed in 216 patients with available pre-treatment MRIs and immunohistochemical staining pathology data. First post-contrast MRI images were used for 3D segmentation using 3D slicer. A CNN architecture was designed with 14 layers. Residual connections were used in the earlier layers to allow stabilization of gradients during backpropagation. Inception style layers were utilized deeper in the network to allow learned segregation of more complex feature mappings. Extensive regularization was utilized including dropout, L2, feature map dropout, and transition layers. The class imbalance was addressed by doubling the input of underrepresented classes and utilizing a class sensitive cost function. Parameters were tuned based on a 20% validation group. A class balanced holdout set of 40 patients was utilized as the testing set. Software code was written in Python using the TensorFlow module on a Linux workstation with one NVidia Titan X GPU. Seventy-four luminal A, 106 luminal B, 13 HER2+, and 23 basal breast tumors were evaluated. Testing set accuracy was measured at 70%. The class normalized macro area under receiver operating curve (ROC) was measured at 0.853. Non-normalized micro-aggregated AUC was measured at 0.871, representing improved discriminatory power for the highly represented Luminal A and Luminal B subtypes. Aggregate sensitivity and specificity was measured at 0.603 and 0.958. MRI analysis of breast cancers utilizing a novel CNN can predict the molecular subtype of breast cancers. Larger data sets will likely improve our model.

Recent Progress for the Techniques of MRI-Guided Breast Interventions and their applications on Surgical Strategy

With a high sensitivity of breast lesions, MRI can detect suspicious lesions which are occult in traditional breast examination equipment. However, the lower and variable specificity of MRI makes the MRI-guided intervention, including biopsies and localizations, necessary before surgery, especially for patients who need the treatment of breast-conserving surgery (BCS). MRI techniques and patient preparation should be first carefully considered before the intervention to avoid lengthening the procedure time and compromising targeting accuracy. Doctors and radiologists need to reconfirm the target of the lesion and be very familiar with the process approach and equipment techniques involving the computer-aided diagnosis (CAD) tools and the biopsy system and follow a correct way. The basic steps of MRI-guided biopsy and localization are nearly the same regardless of the vendor or platform, and this article systematically introduces detailed methods and techniques of MRI-guided intervention. The two interventions both face different challenging situations during procedures with solutions given in the article. Post-operative statistics show that the complications of MRI-guided intervention are infrequent and mild, and MRI-guided biopsy provides the pathological information for the subsequent surgical decisions and MRI-guided localization fully prepared for follow-up surgical biopsy. New techniques for MRI-guided intervention are also elaborated in the article, which leads to future development. In a word, MRI-guided intervention is a safe, accurate, and effective technique with a low complication rate and successful MRI-guided intervention is truly teamwork with efforts from patients to surgeons, radiologists, MRI technologists, and nurses.

Dipole modeling of multispectral signal for detecting metallic biopsy markers during MRI-guided breast biopsy: a pilot study

PURPOSE

During MRI-guided breast biopsy, a metallic biopsy marker is deployed at the biopsy site to guide future interventions. Conventional MRI during biopsy cannot distinguish such markers from biopsy site air, and a post-biopsy mammogram is therefore performed to localize marker placement. The purpose of this pilot study is to develop dipole modeling of multispectral signal (DIMMS) as an MRI alternative to eliminate the cost, inefficiency, inconvenience, and ionizing radiation of a mammogram for biopsy marker localization.

METHODS

DIMMS detects and localizes the biopsy marker by fitting the measured multispectral imaging (MSI) signal to the MRI signal model and marker properties. MSI was performed on phantoms containing titanium biopsy markers and air to illustrate the clinical challenge that DIMMS addresses and on 20 patients undergoing MRI-guided breast biopsy to assess DIMMS feasibility for marker detection. DIMMS was compared to conventional MSI field map thresholding, using the post-procedure mammogram as the reference standard.

RESULTS

Biopsy markers were detected and localized in 20 of 20 cases using MSI with automated DIMMS post-processing (using a threshold of 0.7) and in 18 of 20 cases using MSI field mapping (using a threshold of 0.65 kHz).

CONCLUSION

MSI with DIMMS post-processing is a feasible technique for biopsy marker detection and localization during MRI-guided breast biopsy. With a 2-min MSI scan, DIMMS is a promising MRI alternative to the standard-of-care post-biopsy mammogram.

Multiview Contouring for Breast Tumor on Magnetic Resonance Imaging

The shape and contour of the lesion are shown to be effective features for physicians to identify breast tumor as benign or malignant. The region of the lesion is usually manually created by the physician according to their clinical experience; therefore, contouring tumors on breast magnetic resonance imaging (MRI) is difficult and time-consuming. For this purpose, an automatic contouring method for breast tumors was developed for less burden in the analysis and to decrease the observed bias to help in making decisions clinically. In this study, a multiview segmentation method for detecting and contouring breast tumors in MRI was represented. The preprocessing of the proposed method reduces any amount of noises but preserves the shape and contrast of the breast tumor. The two-dimensional (2D) level-set segmentation method extracts contours of breast tumors from the transverse, coronal, and sagittal planes. The obtained contours are further utilized to generate appropriate three-dimensional (3D) contours. Twenty breast tumor cases were evaluated and the simulation results show that the proposed contouring method was an efficient method for delineating 3D contours of breast tumors in MRI.

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors

BACKGROUND

Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.

PURPOSE

To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (¹ H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-one patients with breast lesions (50 malignant, 26 benign).

SEQUENCE

DKI, DWI, DCE-MRI, and ¹ H-MRS were performed at 3.0T.

ASSESSMENT

Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.

STATISTICAL TESTS

Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.

RESULTS

MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.

DATA CONCLUSION

MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856.

Discrimination between breast invasive ductal carcinomas and benign lesions by optimizing quantitative parameters derived from dynamic contrast-enhanced MRI using a semi-automatic method

BACKGROUND

To propose a semi-automatic method for distinguishing invasive ductal carcinomas from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS

142 cases were included. In the conventional method, the region of interest for a breast lesion was drawn manually and the corresponding mean time-signal intensity curve (TIC) was qualitatively categorized. Only one quantitative parameter was obtained: the maximum slope of increase (MSI). By contrast, the proposed method extracted the suspicious breast lesion semi-automatically. Besides MSI, more quantitative parameters reflecting perfusion information were derived from the mean TIC and lesion region, including the signal intensity slope (SI_slope), initial percentage of enhancement, percentage of peak enhancement, early signal enhancement ratio, and second enhancement percentage. The mean TIC was categorized quantitatively according to the value of SI_slope. Regression models were established. The diagnostic performance differed between the new and conventional methods according to the Wilcoxon rank-sum test and receiver operating characteristic analysis.

RESULTS

According to the TIC categorization results, the accuracies of the traditional and the new method were 59.16% and 76.05%, respectively (P < 0.05). The accuracy was 63.35% for MSI, which was derived from the manual method. For the semi-automatic method, the accuracies were 81.0% and 78.9% for the lesion region and the corresponding mean TIC regression models, respectively.

CONCLUSIONS

The results demonstrate that our proposed semi-automatic method is beneficial for discriminating breast IDCs and benign lesions based on DCE-MRI, and this method should be considered as a supplementary tool for subjective diagnosis by clinical radiologists.

Screening BRCA1 and BRCA2 Mutation Carriers for Breast Cancer

Women with BRCA mutations, who choose to decline or defer risk-reducing mastectomy, require a highly sensitive breast screening regimen they can begin by age 25 or 30. Meta-analysis of multiple observational studies, in which both mammography and magnetic resonance imaging (MRI) were performed annually, demonstrated a combined sensitivity of 94% for MRI plus mammography compared to 39% for mammography alone. There was negligible benefit from adding screening ultrasound or clinical breast examination to the other two modalities. The great majority of cancers detected were non-invasive or stage I. While the addition of MRI to mammography lowered the specificity from 95% to 77%, the specificity improved significantly after the first round of screening. The median follow-up of women with screen-detected breast cancer in the above observational studies now exceeds 10 years, and the long-term breast cancer-free survival in most of these studies is 90% to 95%. However, ongoing follow-up of these study patients, as well of women screened and treated more recently, is necessary. Advances in imaging technology will make highly sensitive screening accessible to a greater number of high-risk women.

Background parenchymal enhancement in breast magnetic resonance imaging: A review of current evidences and future trends

Background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) is a dynamic process, which varies among women and within the same woman over time due to different factors. BPE has profound implications for women with or at risk of breast cancer. Breast radiologist should be aware of factors that could potentially influence BPE and have to be familiar with its typical appearance. Marked BPE could indeed affect the diagnostic accuracy of breast MRI, but this shortcoming can be minimized through evaluation by dedicated radiologists, in order to correctly interpret and properly manage the additional findings. BPE shows promise as an imaging biomarker but many issues need to be addressed before it can be used either to determine screening strategy or the value of risk-reducing interventions. This review analyzes the clinical influence of BPE on breast MRI interpretation, breast cancer staging and surgical outcome and discusses current available evidences about BPE as an imaging biomarker.

Histopathologic characteristics of background parenchymal enhancement (BPE) on breast MRI

PURPOSE

Breast fibroglandular tissue (FGT), as visualized on a mammogram (mammographic density, MD), is one of the strongest known risk factors for breast cancer. FGT is also visible on breast MRI, and increased background parenchymal enhancement (BPE) in the FGT has been identified as potentially a major breast cancer risk factor. The aim of this exploratory study was to examine the biologic basis of BPE.

METHODS

We examined the unaffected contra-lateral breast of 80 breast cancer patients undergoing a prophylactic mastectomy before any treatment other than surgery of their breast cancer. BPE was classified on the BI-RADS scale (minimal/mild/moderate/marked). Slides were stained for microvessel density (MVD), CD34 (another measure of endothelial density), glandular tissue within the FGT and VEGF. Spearman correlations were used to evaluate the associations between BPE and these pathologic variables.

RESULTS

In pre-menopausal patients, BPE was highly correlated with MVD, CD34 and glandular concentration within the FGT, and the pathologic variables were themselves highly correlated. The expression of VEGF was effectively confined to terminal duct lobular unit (TDLU) epithelium. The same relationships of the four pathologic variables with BPE were seen in post-menopausal patients, but the relationships were much weaker and not statistically significant.

CONCLUSION

The strong correlation of BPE and MVD together with the high correlation of MVD with glandular concentration seen in pre-menopausal patients indicates that increased breast cancer risk associated with BPE in pre-menopausal women is likely to result from its association with increased concentration of glandular tissue in the FGT. The effective confinement of VEGF expression to the TDLUs shows that the signal for MVD growth arises directly from the glandular tissue. Further studies are needed to understand the basis of BPE in post-menopausal women.

A Gradient-Based Approach for Breast DCE-MRI Analysis

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

Discrimination between malignant and benign mass-like lesions from breast dynamic contrast enhanced MRI: semi-automatic vs. manual analysis of the signal time-intensity curves

Purpose: To investigate the performance of a new semi-automatic method for analyzing the signal time-intensity curve (TIC) obtained by breast dynamic contrast enhancement (DCE)-MRI.

Methods: In the conventional method, a circular region of interest was drawn manually onto the map reflecting the maximum slope of increase (MSI) to delineate the suspicious lesions. The mean TIC was determined subjectively as one of three different wash-out patterns. In the new method, the lesion area was identified semi-automatically. The mean TIC was categorized quantitatively. In addition to the MSI, other quantitative parameters were calculated, including the signal intensity slope (SI_slope), initial percentage of enhancement (E_initial), percentage of peak enhancement (E_peak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP). The performances were compared with receiver operating characteristic (ROC) analysis and Wilcoxon's test.

Results: For TIC categorization results, the diagnostic accuracy rates were 61.54% with the traditional manual method and 76.92% with the new method. For the mean MSI values from the manual method, the accuracy was 63.41%. For the mean TIC derived using the semi-automatic method, the diagnostic accuracy were 82.05% for SI_slope, 67.31% for MSI, 61.53% for E_initial, 64.75% for E_peak, 64.74% for ESER, and 52.56% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the diagnostic accuracy for above mentioned parameters were 80.13%, 69.87%, 61.54%, 63.47%, 64.74% and 55.13%, respectively.

Conclusion: With respect to the analysis of TIC from breast DCE-MRI, the results demonstrated that the new method increased the diagnostic accuracy, and should be considered as a supplementary tool for distinguishing benign and malignant lesions.

MRI background parenchymal enhancement, breast density and serum hormones in postmenopausal women

Background parenchymal enhancement (BPE) is the degree to which normal breast tissue enhances on contrast-enhanced magnetic resonance imaging (MRI). MRI-density is a volumetric measure of breast density that is highly correlated with mammographic density, an established breast cancer risk factor. Endogenous estrogen concentrations are positively associated with postmenopausal breast cancer risk and BPE has been shown to be sensitive to hormonal exposures. The objective of our study was to examine the relationship between BPE and MRI-density and serum hormone concentrations in postmenopausal women. This was a study of cancer-free postmenopausal women undergoing contrast-enhanced breast MRI (N = 118). At the time of MRI all women completed a self-administered questionnaire and blood samples were collected for hormone analyses. Serum concentrations of estrone (E1), estradiol (E2) and bioavailable E2 were examined by category of BPE and MRI-density. Compared to women with "minimal" BPE, those who had "marked" BPE had significantly higher serum concentrations of E1, E2 and bioavailable E2 (90% increase, p_trend across all categories = 0.001; 150% increase, p_trend  = 0.001; and 158% increase, p_trend  = 0.001, respectively). These associations were only affected to a minor extent by adjustment for BMI and other variables. After adjustment for BMI, no significant associations between MRI-density and serum E1, E2 and bioavailable E2 were observed. Serum estrogen concentrations were significantly positively associated with BPE. Our study provides further evidence of the hormone-sensitive nature of BPE, indicating a potential role for BPE as an imaging marker of endogenous and exogenous hormonal exposures in the breast.

The correlation of background parenchymal enhancement in the contralateral breast with patient and tumor characteristics of MRI-screen detected breast cancers

PURPOSE

Higher background parenchymal enhancement (BPE) could be used for stratification of MRI screening programs since it might be related to a higher breast cancer risk. Therefore, the purpose of this study is to correlate BPE to patient and tumor characteristics in women with unilateral MRI-screen detected breast cancer who participated in an intermediate and high risk screening program. As BPE in the affected breast may be difficult to discern from enhancing cancer, we assumed that BPE in the contralateral breast is a representative measure for BPE in women with unilateral breast cancer.

MATERIALS AND METHODS

This retrospective study was approved by our local institutional board and a waiver for consent was granted. MR-examinations of women with unilateral breast cancers screen-detected on breast MRI were evaluated by two readers. BPE in the contralateral breast was rated according to BI-RADS. Univariate analyses were performed to study associations. Observer variability was computed.

RESULTS

Analysis included 77 breast cancers in 76 patients (age: 48±9.8 years), including 62 invasive and 15 pure ductal carcinoma in-situ cases. A negative association between BPE and tumor grade (p≤0.016) and a positive association with progesterone status (p≤0.021) was found. The correlation was stronger when only considering invasive disease. Inter-reader agreement was substantial.

CONCLUSION

Lower BPE in the contralateral breast in women with unilateral breast cancer might be associated to higher tumor grade and progesterone receptor negativity. Great care should be taken using BPE for stratification of patients to tailored screening programs.

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

Background

In this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute.

Methods

Our retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff’s α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman’s rank correlation coefficients were used to compare HEIP and CEIP.

Results

Inter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with p < 10⁻¹². CEIP of shape, specifically sphericity and irregularity, replicated HEIP with both p values < 0.001. CEIP did not demonstrate agreement with HEIP of tumour margin or internal enhancement.

Conclusions

Quantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.

Evaluation of T1/T2 ratios in a pilot study as a potential biomarker of biopsy: proven benign and malignant breast lesions in correlation with histopathological disease stage

Aim:

Early breast cancer detection is important for intervention and prognosis. Advances in treatment and outcome require diagnostic tools with highly positive predictive value.

Purpose:

To study the potential role of quantitative MRI (qMRI) using T1/T2 ratios to differentiate benign from malignant breast lesions.

Methods:

A cross-sectional study of 69 women with 69 known or suspicious breast lesions were scanned with mixed-turbo spin echo pulse sequence. Patients were grouped according to histopathological assessment of disease stage: untreated malignant tumor, treated malignancy and benign disease.

Results & Discussion:

Elevated T1/T2 means were observed for biopsy-proven malignant lesions and for malignant lesions treated prior to qMRI with chemotherapy and/or radiation, as compared with benign lesions. The qMRI-obtained T1/T2 ratios correlated with histopathology. Analysis revealed correlation between elevated T1/T2 ratio and disease stage. This could provide valuable complementary information on tissue properties as an additional diagnostic tool.

Early detection is important for successful intervention in breast cancer. We studied the potential role of quantitative MRI (qMRI) using T1/T2 ratios to differentiate benign from malignant breast lesions. Sixty nine women with breast lesions were scanned with qMRI. Elevated ratios were observed for biopsy-proven malignant lesions and for malignant lesions that were treated prior to qMRI with chemotherapy and/or radiation, as compared with benign lesions. With further studies, this approach could provide valuable information concerning tissue properties in addition to established breast imaging sequences and be an additional diagnostic tool.

Predictive performance of BI-RADS magnetic resonance imaging descriptors in the context of suspicious (category 4) findings

Objective

To determine the positive predictive value (PPV) and likelihood ratio for magnetic resonance imaging (MRI) characteristics of category 4 lesions, as described in the Breast Imaging Reporting and Data System (BI-RADS^®) lexicon, as well as to test the predictive performance of the descriptors using multivariate analysis and the area under the curve derived from a receiver operating characteristic (ROC) curve.

Materials and Methods

This was a double-blind review study of 121 suspicious findings from 98 women examined between 2009 and 2013. The terminology was based on the 2013 edition of the BI-RADS.

Results

Of the 121 suspicious findings, 53 (43.8%) were proven to be malignant lesions, with no significant difference between mass and non-mass enhancement (p = 0.846). The PPVs were highest for masses with a spiculated margin (71%) and round shape (63%), whereas segmental distribution achieved a high PPV (80%) for non-mass enhancement. Kinetic analyses performed poorly, except for type 3 curves applied to masses (PPV of 73%). Logistic regression models were significant for both patterns, although the results were better for masses, particularly when kinetic assessments were included (p = 0.015; pseudo R² = 0.48; area under the curve = 90%).

Conclusion

Some BI-RADS MRI descriptors have high PPV and good predictive performance-as demonstrated by ROC curve and multivariate analysis-when applied to BI-RADS category 4 findings. This may allow future stratification of this category.

Comparison of Background Parenchymal Enhancement at Contrast-enhanced Spectral Mammography and Breast MR Imaging

Purpose To assess the extent of background parenchymal enhancement (BPE) at contrast material-enhanced (CE) spectral mammography and breast magnetic resonance (MR) imaging, to evaluate interreader agreement in BPE assessment, and to examine the relationships between clinical factors and BPE. Materials and Methods This was a retrospective, institutional review board-approved, HIPAA-compliant study. Two hundred seventy-eight women from 25 to 76 years of age with increased breast cancer risk who underwent CE spectral mammography and MR imaging for screening or staging from 2010 through 2014 were included. Three readers independently rated BPE on CE spectral mammographic and MR images with the ordinal scale: minimal, mild, moderate, or marked. To assess pairwise agreement between BPE levels on CE spectral mammographic and MR images and among readers, weighted κ coefficients with quadratic weights were calculated. For overall agreement, mean κ values and bootstrapped 95% confidence intervals were calculated. The univariate and multivariate associations between BPE and clinical factors were examined by using generalized estimating equations separately for CE spectral mammography and MR imaging. Results Most women had minimal or mild BPE at both CE spectral mammography (68%-76%) and MR imaging (69%-76%). Between CE spectral mammography and MR imaging, the intrareader agreement ranged from moderate to substantial (κ = 0.55-0.67). Overall agreement on BPE levels between CE spectral mammography and MR imaging and among readers was substantial (κ = 0.66; 95% confidence interval: 0.61, 0.70). With both modalities, BPE demonstrated significant association with menopausal status, prior breast radiation therapy, hormonal treatment, breast density on CE spectral mammographic images, and amount of fibroglandular tissue on MR images (P < .001 for all). Conclusion There was substantial agreement between readers for BPE detected on CE spectral mammographic and MR images. ^© RSNA, 2016.

Breast cancer molecular subtype classifier that incorporates MRI features

PURPOSE

To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes.

MATERIALS AND METHODS

This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test.

RESULTS

Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR-/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR-/HER2+), and 81.0% (TN).

CONCLUSION

We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129.

Magnetic Resonance Imaging Guided Vacuum Assisted and Core Needle Biopsies

OBJECTIVE

The purpose of this study to present the results of Magnetic resonance imaging (MRI) guided cutting needle biopsy procedures of suspicious breast lesions that can be solely detected on Magnetic resonance (MR) examination.

MATERIALS AND METHODS

The study included 48 patients with 48 lesions which were solely be observed in breast MRI, indistinguishable in ultrasonography and mammography, for MR guided vacuum-assisted cutting needle biopsy and 42 patients with 42 lesions for MR guided cutting needle biopsy for the lesions of the same nature. MR imaging was performed using a 1.5-Tesla MRI device. Acquired MR images were determined and biopsy protocol was performed using computer-aided diagnosis system on the workstation. Vacuum biopsies were performed using 10 G or 12 G automatic biopsy systems, cutting needle biopsy procedures were performed using fully automated 12 G biopsy needle.

RESULTS

All biopsy procedures were finalized successfully without major complications. The lesions were 54 mass (60%), 28 were non-mass contrast enhancement (31%) and 8 were foci (9%) in the MR examination. Histopathological evaluation revealed 18 malignant (invasive, in-situ ductal carcinoma and lobular carcinoma), 66 benign (apocrine metaplasia, fibrosis, fibroadenomatoid lesion, sclerosing adenosis, fibrocystic disease and mild-to-severe epithelial proliferation) and 6 high-risk (atypical ductal hyperplasia, intraductal papilloma, radial scar) lesions.

CONCLUSION

Magnetic resonance guided vacuum and cutting needle biopsy methods are successful methods fort he evaluation of solely MRI detected suspicious breast lesions. There are several advantages relative to each other in both methods.

Comparison of contrast enhancement and diffusion-weighted magnetic resonance imaging in healthy and cancerous breast tissue

OBJECTIVE

To measure background parenchymal enhancement (BPE) and compare with other contrast enhancement values and diffusion-weighted MRI parameters in healthy and cancerous breast tissue at the clinical level.

MATERIALS AND METHODS

This HIPAA-compliant, IRB approved retrospective study enrolled 77 patients (38 patients with breast cancer - mean age 51.8 ± 10.0 years; 39 high-risk patients for screening evaluation - mean age 46.3 ± 11.7 years), who underwent contrast-enhanced 3T breast MRI. Contrast enhanced MRI and diffusion-weighted imaging were performed to quantify BPE, lesion contrast enhancement, and apparent diffusion coefficient (ADC) metrics in fibroglandular tissue (FGT) and lesions.

RESULTS

BPE did not correlate with ADC values. Mean BPE for the lesion-bearing patients was higher (43.9%) compared to that of the high-risk screening patients (28.3%, p=0.004). Significant correlation (r=0.37, p<0.05) was found between BPE and lesion contrast enhancement.

CONCLUSION

No significant association was observed between parenchymal or lesion enhancement with conventional apparent diffusion metrics, suggesting that proliferative processes are not co-regulated in cancerous and parenchymal tissue.

Effective factors to raise diagnostic performance of breast MRI for diagnosing pathologic complete response in breast cancer patients after neoadjuvant chemotherapy

BACKGROUND

Although MRI is a highly effective tool in evaluating residual disease after neoadjuvant chemotherapy (NAC), there are many reports of discordance between the response of MRI and pathology. To increase MR accuracy, additional methods, which reflect post-NAC changes, should be considered in diagnosis.

PURPOSE

To evaluate effective methods that raise the diagnostic performance of MRI for predicting pathologic complete response (pCR) in breast cancer after neoadjuvant chemotherapy (NAC).

MATERIAL AND METHODS

For 98 invasive breast carcinoma patients, chemotherapeutic response to MRI was evaluated for the following parameters: tumor size, tumor distribution pattern, kinetic curve analysis, and background parenchymal enhancement pattern (BPE). BPE was categorized as "minimal", "mild", "moderate", or "marked", according to the ACR BI-RADS criteria.

RESULTS

After NAC, the mean size of tumors decreased by 40% in non-pCR and by 59% in pCR groups, respectively. The sensitivity, specificity, false positive rate and false negative rate of MRI were 96% (78/81), 53% (9/17), 47% (8/17), and 4% (3/81), respectively. At pre-NAC MRI, the most common kinetic curve was delayed washout pattern (68%, 67/98); however, at post-NAC MRI the persistent pattern (55%, 47/86). Grouped lesion was the most common tumor distribution pattern on pre-NAC MRI (28%, 27/98), while on post-NAC solitary mass (40%, 34/86). The most common BPE at pre- and post-NAC MRI was mild and minimal enhancement, respectively.

CONCLUSION

To improve the diagnostic accuracy of MRI, we should consider additional factors including: tumor distribution pattern, BPE, kinetic curve analysis, and tumor size.

Beyond the mammography debate: a moderate perspective

After some decades of contention, one can almost despair and conclude that (paraphrasing) "the mammography debate you will have with you always." Against that sentiment, in this review I argue, after reflecting on some of the major themes of this long-standing debate, that we must begin to move beyond the narrow borders of claim and counterclaim to seek consensus on what the balance of methodologically sound and critically appraised evidence demonstrates, and also to find overlooked underlying convergences; after acknowledging the reality of some residual and non-trivial harms from mammography, to promote effective strategies for harm mitigation; and to encourage deployment of new screening modalities that will render many of the issues and concerns in the debate obsolete. To these ends, I provide a sketch of what this looking forward and beyond the current debate might look like, leveraging advantages from abbreviated breast magnetic resonance imaging technologies (such as the ultrafast and twist protocols) and from digital breast tomosynthesis-also known as three-dimensional mammography. I also locate the debate within the broader context of mammography in the real world as it plays out not for the disputants, but for the stakeholders themselves: the screening-eligible patients and the physicians in the front lines who are charged with enabling both the acts of screening and the facts of screening at their maximally objective and patient-accessible levels to facilitate informed decisions.

Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay

PURPOSE

To investigate the association between a validated, gene-expression-based, aggressiveness assay, Oncotype Dx RS, and morphological and texture-based image features extracted from magnetic resonance imaging (MRI).

MATERIALS AND METHODS

This retrospective study received Internal Review Board approval and need for informed consent was waived. Between 2006-2012, we identified breast cancer patients with: 1) ER+, PR+, and HER2- invasive ductal carcinoma (IDC); 2) preoperative breast MRI; and 3) Oncotype Dx RS test results. Extracted features included morphological, histogram, and gray-scale correlation matrix (GLCM)-based texture features computed from tumors contoured on pre- and three postcontrast MR images. Linear regression analysis was performed to investigate the association between Oncotype Dx RS and different clinical, pathologic, and imaging features. P < 0.05 was considered statistically significant.

RESULTS

Ninety-five patients with IDC were included with a median Oncotype Dx RS of 16 (range: 0-45). Using stepwise multiple linear regression modeling, two MR-derived image features, kurtosis in the first and third postcontrast images and histologic nuclear grade, were found to be significantly correlated with the Oncotype Dx RS with P = 0.0056, 0.0005, and 0.0105, respectively. The overall model resulted in statistically significant correlation with Oncotype Dx RS with an R-squared value of 0.23 (adjusted R-squared = 0.20; P = 0.0002) and a Spearman's rank correlation coefficient of 0.49 (P < 0.0001).

CONCLUSION

A model for IDC using imaging and pathology information correlates with Oncotype Dx RS scores, suggesting that image-based features could also predict the likelihood of recurrence and magnitude of chemotherapy benefit.

Development of a novel breast MRI phantom for quality control

OBJECTIVE

Many indications for breast MRI exist. MRI screening can identify preinvasive breast cancer in women at high risk and in that regard is superior to mammography and ultrasound. Quality control standards exist for mammography and breast ultra-sound screening with phantoms designed specifically for this purpose. Given the growing importance of breast MRI, we propose the development of a breast MRI phantom for quality control purposes.

MATERIALS AND METHODS

A breast phantom with dual cavities containing water and fat was developed. A resolution plate inside the phantom contains various shapes ranging in size from 1 to 20 mm. Twenty studies of the phantom were performed with a 1.5-T system. STIR, T1-weighted fat-suppressed, and T2-weighted sequences were completed. Relaxation times of water and fat, number of step shapes resolved on STIR and T2-weighted images, number of circles resolved on T2-weighted images, and the diameter of a 20-mm circle on T1-weighted fat-suppressed images were recorded.

RESULTS

On STIR images the TR of fat was 238.70±96.31 ms and of water was 1231.92±399.14 ms. On T2-weighted images the TR of fat was 778.73±62.60 ms and of water was 1737.60±121.63 ms. On STIR images, steps 3 mm and larger were visualized in 95% of instances. On T2-weighted images steps 3 mm and larger were seen in all instances. Measurements of a 20-mm circle were 19±0.3 mm.

CONCLUSION

The proposed breast MRI phantom can be used to obtain reproducible measurements and allows implementation of quality control measures for a modality that is being increasingly used.

Subject-specific models of susceptibility-induced B0 field variations in breast MRI

PURPOSE

To rapidly calculate and validate subject-specific field maps based on the three-dimensional shape of the bilateral breast volume.

MATERIALS AND METHODS

Ten healthy female volunteers were scanned at 3 Tesla using a multi-echo sequence that provides water, fat, in-phase, out-of-phase, and field map images. A shape-specific binary mask was automatically generated to calculate a computed field map using a dipole field model. The measured and computed field maps were compared by visualizing the spatial distribution of the difference field map, the mean absolute error, and the 80% distribution widths of frequency histograms.

RESULTS

The 10 computed field maps had a mean absolute error of 38 Hz (0.29 ppm) compared with the measured field maps. The average 80% distribution widths for the histograms of all of the computed, measured, and difference field maps are 205 Hz, 233 Hz, and 120 Hz, respectively.

CONCLUSION

The computed field maps had substantial overall agreement with the measured field maps, indicating that breast MRI field maps can be computed based on the air-tissue interfaces. These estimates may provide a predictive model for field variations and thus have the potential to improve applications in breast MRI.

Breast MR with special focus on DW-MRI and DCE-MRI

The use of magnetic resonance imaging (MRI) for the assessment of breast lesions was first described in the 1970s; however, its wide application in clinical routine is relatively recent. The basic principles for diagnosis of a breast lesion rely on the evaluation of signal intensity in T2-weighted sequences, on morphologic assessment and on the evaluation of contrast enhancement behaviour. The quantification of dynamic contrast behaviour by dynamic contrast-enhanced (DCE) MRI and evaluation of the diffusivity of water molecules by means of diffusion-weighted MRI (DW-MRI) have shown promise in the work-up of breast lesions. Therefore, breast MRI has gained a role for all indications that could benefit from its high sensitivity, such as detection of multifocal lesions, detection of contralateral carcinoma and in patients with familial disposition. Breast MRI has been shown to have a role in monitoring of neoadjuvant chemotherapy, for the evaluation of therapeutic results during the course of therapy. Breast MRI can improve the determination of the remaining tumour size at the end of therapy in patients with a minor response. DCE-MRI and DW-MRI have shown potential for improving the early assessment of tumour response to therapy and the assessment of residual tumour after the end of therapy. Breast MRI is important in the postoperative work-up of breast cancers. High sensitivity and specificity have been reported for the diagnosis of recurrence; however, pitfalls such as liponecrosis and changes after radiation therapy have to be carefully considered.