Dynamic Contrast-enhanced Breast MRI Research Articles

Abstract P4-02-10: Breast cancer surveillance in high-risk women with dynamic contrast-enhanced magnetic resonance imaging every 6 months: Results from a single institution study

Abstract Purpose: To develop a novel approach for early detection of breast cancer and examine molecular features of screen detected cancers in prospectively ascertained high-risk women undergoing semi-annual dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) for women at high genetic risk. Background: Women with a personal or family history of breast cancer and genetic mutation carriers of BRCA1 and BRCA2 have a higher than normal risk of breast cancer. An intensified screening surveillance regimen is an early detection strategy in high-risk women. The American Cancer Society recommends annual DCE-MRI in addition to annual mammogram based off several pivotal screening studies that demonstrated improved sensitivity and cancer detection rates and decreased interval cancer rates with the addition of annual DCE-MRI. Questions remain regarding the optimal screening modality and interval regimen in these high-risk women. Methods: Between 2004 and 2016, we assembled a prospective cohort of high-risk women undergoing semi-annual DCE-MRI and annual mammography. To be eligible, women had a lifetime breast cancer risk >20% and/or tested positive for a pathogenic mutation using a cancer gene panel including BRCA1, BRCA2, CDH1, PALB2, CHEK2 and other cancer susceptibility genes in the DNA repair pathway. Somatic mutation events in screen-detected tumors were investigated using UW-OncoPlex cancer gene panel using DNA extracted from FFPE shavings. Results: 295 women were recruited to the study; 44% of the study participants had pathogenic mutations in BRCA1 or BRCA2 genes. At a median follow-up of 3.3 years (range 0-12 years), 3 DCIS and 13 early stage invasive breast cancers were detected, of which 14 occurred in subjects with identifiable pathogenic mutations (11 BRCA1, 2 BRCA2, 1 CDH1). The incidence rate is 1.3% in all subjects, but 3.5 % per year in BRCA1 carriers. DCE-MRI identified all 13 invasive cancers at a mean size of 0.61 cm (range 0.1-1.0 cm); none had lymph node metastasis. No interval cancers occurred. In addition, 7 of the breast cancers were detected on DCE-MRI imaging obtained at the 6 months screening interval; they would be interval cancers if only annual screening were implemented. There was very little DNA for somatic mutation testing in the majority of cases. However, as expected, there was heterogeneity in the spectrum of mutations but the most commonly somatically mutated gene in the early cancers was TP53. Conclusions: DCE-MRI every 6 months performed well for early detection of invasive breast cancer in high-risk women, accomplishing the ultimate goal of breast cancer screening—detecting node-negative, invasive tumors less than 1 cm. Semi-annual DCE-MRI performed especially well in BRCA1 mutation carriers at risk for the most aggressive subtype of breast cancer. Further interventional studies evaluating this novel screening approach are warranted to personalize breast cancer risk assessment and prevention. Citation Format: Whitaker K, Guindalini R, Abe H, Sheeth D, Huo D, Hong S, Churpek J, Verp M, Obeid E, Zheng Y, Amico A, Yoshimatsu T, Olopade O. Breast cancer surveillance in high-risk women with dynamic contrast-enhanced magnetic resonance imaging every 6 months: Results from a single institution study [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-10.

Feasibility analysis of early temporal kinetics as a surrogate marker for breast tumor type, grade, and aggressiveness.

Screening breast MRI has been shown to preferentially detect high-grade ductal carcinoma in situ (DCIS) and invasive carcinoma, likely due to increased angiogenesis resulting in early initial uptake of contrast. As interest grows in abbreviated screening breast MRI (AB-MRI), markers of early contrast washin that can predict tumor grade and potential aggressiveness are of clinical interest. To evaluate the feasibility of using the initial enhancement ratio (IER) as a surrogate marker for tumor grade, hormone receptor status, and prognostic markers, as an initial step to being incorporated into AB-MRI. Retrospective. In all, 162 women (mean 55.0 years, range 32.8-87.7 years) with 187 malignancies imaged January 2012-November 2015. Images were acquired at 3.0T with a T1 -weighted gradient echo fat-suppressed-volume interpolated breath-hold sequence. Subjects underwent dynamic contrast-enhanced breast MRI with a 7-channel breast coil. IER (% signal increase over baseline at the first postcontrast acquisition) was assessed and correlated with background parenchymal enhancement, washout curves, stage, and final pathology. Chi-square test, Spearman rank correlation, Mann-Whitney U-tests, Bland-Altman analysis, and receiver operating characteristic curve analysis. IER was higher for invasive cancer than for DCIS (R1/R2, P < 0.001). IER increased with tumor grade (R1: r = 0.56, P < 0.001, R2: r = 0.50, P < 0.001), as ki-67 increased (R1: r = 0.35, P < 0.001; R2 r = 0.35, P < 0.001), and for node-positive disease (R1/R2, P = 0.001). IER was higher for human epidermal growth factor receptor two-positive and triple negative cancers than for estrogen receptor-positive / progesterone receptor-positive tumors (R1 P < 0.001-0.002; R2 P = 0.0.001-0.011). IER had higher sensitivity (80.6% vs. 75.5%) and specificity (55.8% vs. 48.1%) than washout curves for positive nodes, higher specificity (48.1% vs. 36.5%) and positive predictive value (70.2% vs. 66.7%) for high ki-67, and excellent interobserver agreement (intraclass correlation coefficient = 0.82). IER, a measurement of early contrast washin, is associated with higher-grade malignancies and tumor aggressiveness and might be potentially incorporated into an AB-MRI protocol. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1692-1700.

Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer?

Background Intrinsic molecular profiling of breast cancer provides clinically relevant information that helps tailor therapy directed to the specific tumor subtype. We hypothesized that dynamic contrast-enhanced MRI (DCE-MRI) derived quantitative kinetic parameters (CD-QKPs) may help predict molecular tumor profiles non-invasively. Purpose To determine the association between DCE-MRI (CD-QKPs) and breast cancer clinicopathological prognostic factors. Material and Methods Clinicopathological factors in consecutive women with biopsy-confirmed invasive breast cancer who underwent breast DCE-MRI were retrospectively reviewed. Analysis of variance was used to examine associations between prognostic factors and CD-QKPs. Fisher's exact test was used to investigate the relationship between kinetic curve type and prognostic factors. Results A total of 198 women with invasive breast cancer were included. High-grade and HER2+ tumors were more likely to have a washout type curve while luminal A tumors were less likely. High-grade was significantly associated with increased peak enhancement (PE; P = 0.01), enhancement maximum slope (MS; P = 0.03), and mean enhancement ( ME, P = 0.03), while high clinical lymph node stage (cN3) was significantly associated with increased MS and time to peak (tP; P = 0.01). HER2+ tumors were associated with a higher PE ( P = 0.03) and ME ( P = 0.06) than HER2- counterparts, and ER-/HER2+ tumors showed higher PE and ME values than ER+/HER2- tumors ( P = 0.06). Conclusion DCE-MRI time-intensity CD-QKPs are associated with high tumor grade, advanced nodal stage, and HER2+ status, indicating their utility as imaging biomarkers.

Complementary Value of Contralateral Parenchymal Enhancement on DCE-MRI to Prognostic Models and Molecular Assays in High-risk ER+/HER2- Breast Cancer.

Purpose: To determine whether markers of healthy breast stroma are able to select a subgroup of patients at low risk of death or metastasis from patients considered at high risk according to routine markers of the tumor.Experimental Design: Patients with ER+/HER2- breast cancer were consecutively included for retrospective analysis. The contralateral parenchyma was segmented automatically on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), where upon the average of the top-10% late enhancement was calculated. This contralateral parenchymal enhancement (CPE) was analyzed with respect to routine prognostic models and molecular assays (Nottingham Prognostic Index, Dutch clinical chemotherapy-selection guidelines, 70-gene signature, and 21-gene recurrence score). CPE was split in tertiles and tested for overall and distant disease-free survival. CPE was adjusted for patient and tumor characteristics, as well as systemic therapy, using inverse probability weighting (IPW). Subanalyses were performed in patients at high risk according to prognostic models and molecular assays.Results: Four-hundred-and-fifteen patients were included, constituting the same group in which the association between CPE and survival was discovered. Median follow-up was 85 months, 34/415(8%) patients succumbed. After IPW-adjustment for patient and tumor characteristics, patients with high CPE had significantly better overall survival than those with low CPE in groups at high risk according to the Nottingham Prognostic Index [HR (95% CI): 0.08 (0.00-0.40), P < 0.001]; Dutch clinical guidelines [HR (95% CI): 0.22 (0.00-0.81), P = 0.021]; and 21-gene recurrence score [HR (95% CI): 0.14 (0.00-0.84), P = 0.030]. One group showed a trend [70-gene signature: HR (95% CI): 0.25 (0.00-1.02), P = 0.054].Conclusions: In patients at high risk based on the tumor, subgroups at relatively low risk were identified using pretreatment enhancement of the stroma on breast DCE-MRI. Clin Cancer Res; 23(21); 6505-15. ©2017 AACR.

A novel framework for evaluating the image accuracy of dynamic MRI and the application on accelerated breast DCE MRI.

To develop a novel framework for evaluating the accuracy of quantitative analysis on dynamic contrast-enhanced (DCE) MRI with a specific combination of imaging technique, scanning parameters, and scanner and software performance and to test this framework with breast DCE MRI with Time-resolved angiography WIth Stochastic Trajectories (TWIST). Realistic breast tumor phantoms were 3D printed as cavities and filled with solutions of MR contrast agent. Full k-space raw data of individual tumor phantoms and a uniform background phantom were acquired. DCE raw data were simulated by sorting the raw data according to TWIST view order and scaling the raw data according to the enhancement based on pharmaco-kinetic (PK) models. The measured spatial and temporal characteristics from the images reconstructed using the scanner software were compared with the original PK model (ground truth). Images could be reconstructed using the manufacturer's platform with the modified 'raw data.' Compared with the 'ground truth,' the RMS error in all images was <10% in most cases. With increasing view-sharing acceleration, the error of the initial uptake slope decreased while the error of peak enhancement increased. Deviations of PK parameters varied with the type of enhancement. A new framework has been developed and tested to more realistically evaluate the quantitative measurement errors caused by a combination of the imaging technique, parameters and scanner and software performance in DCE-MRI.

Characterizing and eliminating errors in enhancement and subtraction artifacts in dynamic contrast-enhanced breast MRI: Chemical shift artifact of the third kind.

To characterize errors in enhancement in breast dynamic contrast-enhanced (DCE) MRI studies as a function of echo time and determine the source of dark band artifacts in clinical subtraction images. Computer simulations, oil and water substitute (methylene chloride), as well as an American College of Radiology quality control phantom were tested. Routine clinical DCE breast MRI study was bracketed with (accelerated) in-phase DCE acquisitions in five patients. Simulation results demonstrated up to -160% suppression of the expected enhancement caused by differential enhancement of fat and water. Two-dimensional gradient-recalled echo and fat-suppressed 3D GRE phantom imaging confirmed the simulation results and showed that fat suppression does not eliminate the artifact. In vivo in-phase DCE images showed increased enhancement consistent with predictions and also confirmed increased spatial blurring on in-phase 3D gradient-recalled echo images. Combined multi-dimensional partial Fourier and parallel imaging provided a time-equivalent in-phase DCE MRI acquisition. Errors in expected enhancement occur in DCE breast MRI subtraction images because of differential enhancement of fat and water and incomplete fat signal suppression. These errors can lead to artificial suppression of enhancement as well as dark band artifacts on subtraction images. These artifacts can be eliminated with a time-equivalent in-phase fat-suppressed 3D gradient-recalled echo sequence. Understanding chemical shift artifact of the third kind, a unique artifact of artificial enhancement suppression in the presence of intravoxel fat and water signal, will aid DCE breast MRI image interpretation. In-phase acquisitions (combined with simultaneous minimum echo time or opposed-phase echoes) may facilitate qualitative, quantitative and longitudinal analysis of contrast enhancement. Magn Reson Med 79:2277-2289, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Clinical and Pathologic Features of Clinically Occult Synchronous Bilateral Breast Cancers

PurposeTo investigate clinicopathologic breast cancer characteristics associated with the identification of synchronous bilateral breast cancer (SBBC) on dynamic contrast-enhanced breast magnetic resonance imaging. MethodsWe performed a retrospective review of 311 consecutive patients newly diagnosed with breast cancer who underwent diagnostic mammography, ultrasonography, and dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) at our institution. Tumor histology, grade, biomarker status (estrogen receptor, progesterone receptor, and HER2), TNM staging, and unifocal or multifocal/multicentric status were compared between the index tumors in the unilateral disease and the SBBC groups, as well as between the index tumors in the unilateral disease group and the synchronous contralateral tumors (SCT) in the SBBC group, using the Fisher exact test. ResultsA total of 326 cancers in 311 patients were reviewed. Fifteen (4.8%) patients were diagnosed with SBBC. The index tumors in patients with SBBC were more frequently lower T stage (p = 0.007), lower grade (p = 0.04), and multifocal/multicentric (p = 0.004) compared with the index tumors in the unilateral group. Biomarkers, N status, and M status did not significantly differ between the index tumors in both groups. ConclusionsSBBC was more likely to be identified in women with lower T stage, lower tumor grade and multifocal/multicentric tumors. Multimodality imaging including DCE-MRI is essential in detection and diagnosis of SBBC.

Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks

This work addresses a novel computer-aided diagnosis (CAD) system in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The CAD system is designed based on a mixture ensemble of convolutional neural networks (ME-CNN) to discriminate between benign and malignant breast tumors. The ME-CNN is a modular and image-based ensemble, which can stochastically partition the high-dimensional image space through simultaneous and competitive learning of its modules. The proposed system was assessed on our database of 112 DCE-MRI studies including solid breast masses, using a wide range of classification measures. The ME-CNN model composed of three CNN experts and one convolutional gating network achieves an accuracy of 96.39%, a sensitivity of 97.73% and a specificity of 94.87%. The experimental results also show that it has competitive classification performances compared to three existing single-classifier methods and two convolutional ensemble methods. The proposed ME-CNN model could provide an effective tool for radiologists to analyse breast DCE-MRI images.

Breast DCE-MRI: lesion classification using dynamic and morphological features by means of a multiple classifier system

BackgroundIn breast magnetic resonance imaging (MRI) analysis for lesion detection and classification, radiologists agree that both morphological and dynamic features are important to differentiate benign from malignant lesions. We propose a multiple classifier system (MCS) to classify breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) combining morphological features and dynamic information.MethodsThe proposed MCS combines the results of two classifiers trained with dynamic and morphological features separately. Twenty-six malignant and 22 benign breast lesions, histologically proven, were analysed. The lesions were subdivided into two groups: training set (14 benign and 18 malignant) and testing set (8 benign and 8 malignant). Volumes of interest were extracted both manually and automatically. We initially considered a feature set including 54 morphological features and 98 dynamic features. These were reduced by means of a selection procedure to delete redundant parameters. The performance of each of the two classifiers and of the overall MCS was compared with pathological classification.ResultsWe obtained an accuracy of 91.7% on the testing set using automatic segmentation and combining the best classifier for morphological features (decision tree) and for dynamic information (Bayesian classifier). With implementation of the MCS, an increase in accuracy of 12.5% and of 31.3% was obtained compared with the accuracy of the Bayesian classifier tested with dynamic features and with that of the decision tree tested with morphological parameters, respectively.ConclusionsAn MCS can optimise the accuracy for breast lesion classification combining morphological features and dynamic information.

Application of whole-lesion histogram analysis of pharmacokinetic parameters in dynamic contrast-enhanced MRI of breast lesions with the CAIPIRINHA-Dixon-TWIST-VIBE technique.

To investigate the application of whole-lesion histogram analysis of pharmacokinetic parameters for differentiating malignant from benign breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In all, 92 women with 97 breast lesions (26 benign and 71 malignant lesions) were enrolled in this study. Patients underwent dynamic breast MRI at 3T using a prototypical CAIPIRINHA-Dixon-TWIST-VIBE (CDT-VIBE) sequence and a subsequent surgery or biopsy. Inflow rate of the agent between plasma and interstitium (Ktrans ), outflow rate of agent between interstitium and plasma (Kep ), extravascular space volume per unit volume of tissue (ve ) including mean value, 25th/50th/75th/90th percentiles, skewness, and kurtosis were then calculated based on the whole lesion. A single-sample Kolmogorov-Smirnov test, paired t-test, and receiver operating characteristic curve (ROC) analysis were used for statistical analysis. Malignant breast lesions had significantly higher Ktrans , Kep , and lower ve in mean values, 25th/50th/75th/90th percentiles, and significantly higher skewness of ve than benign breast lesions (all P < 0.05). There was no significant difference in kurtosis values between malignant and benign breast lesions (all P > 0.05). The 90th percentile of Ktrans , the 90th percentile of Kep , and the 50th percentile of ve showed the greatest areas under the ROC curve (AUC) for each pharmacokinetic parameter derived from DCE-MRI. The 90th percentile of Kep achieved the highest AUC value (0.927) among all histogram-derived values. The whole-lesion histogram analysis of pharmacokinetic parameters can improve the diagnostic accuracy of breast DCE-MRI with the CDT-VIBE technique. The 90th percentile of Kep may be the best indicator in differentiation between malignant and benign breast lesions. 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2018;47:91-96.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

BackgroundIn this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).MethodsA total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2−) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.ResultsAmong all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2− breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.ConclusionsThrough a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence

We investigated automated quantitative measures of background parenchymal enhancement (BPE) derived from an early versus delayed post-contrast sequence in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for association with breast cancer presence in a case-control study. DCE-MRIs were retrospectively analyzed for 51 cancer cases and 51 controls with biopsy-proven benign lesions, matched by age and year-of-MRI. BPE was quantified using fully-automated validated computer algorithms, separately from three sequential DCE-MRI post-contrast-subtracted sequences (SUB1, SUB2, and SUB3). The association of BPE computed from the three SUBs and other known factors with breast cancer were assessed in terms of odds ratio (OR) and area under the receiver operating characteristic curve (AUC). The OR of breast cancer for the percentage BPE measure (BPE%) quantified from SUB1 was 3.5 (95% Confidence Interval: 1.3, 9.8; p = 0.015) for 20% increments. Slightly lower and statistically significant ORs were also obtained for BPE quantified from SUB2 and SUB3. There was no significant difference (p > 0.2) in AUC for BPE quantified from the three post-contrast sequences and their combination. Our study showed that quantitative measures of BPE are associated with breast cancer presence and the association was similar across three breast DCE-MRI post-contrast sequences.

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.

Purpose Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. Methods We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGVα2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. Results NN produced the lowest image error (SER: 29.1), while TV/TGVα2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). Conclusion TV/TGVα2 should be used as temporal constraints for CS DCE-MRI of the breast.

Dynamic Contrast-Enhanced MRI Perfusion Parameters as Imaging Biomarkers of Angiogenesis.

Hypoxia in the tumor microenvironment is the leading factor in angiogenesis. Angiogenesis can be identified by dynamic contrast-enhanced breast MRI (DCE MRI). Here we investigate the relationship between perfusion parameters on DCE MRI and angiogenic and prognostic factors in patients with invasive ductal carcinoma (IDC). Perfusion parameters (Ktrans, kep and ve) of 81 IDC were obtained using histogram analysis. Twenty-fifth, 50th and 75th percentile values were calculated and were analyzed for association with microvessel density (MVD), vascular endothelial growth factor (VEGF) and conventional prognostic factors. Correlation between MVD and ve50 was positive (r = 0.33). Ktrans50 was higher in tumors larger than 2 cm than in tumors smaller than 2 cm. In multivariate analysis, Ktrans50 was affected by tumor size and MVD with 12.8% explanation. There was significant association between Ktrans50 and tumor size and MVD. Therefore we conclude that DCE MRI perfusion parameters are potential imaging biomarkers for prediction of tumor angiogenesis and aggressiveness.

Perfusion Parameters on Breast Dynamic Contrast-Enhanced MRI Are Associated With Disease-Specific Survival in Patients With Triple-Negative Breast Cancer.

The aim of this study was to investigate the association between perfusion parameters on MRI performed before treatment and survival outcome (disease-free survival [DFS], disease-specific survival [DSS]) in patients with triple-negative breast cancer (TNBC). Sixty-one patients (median age, 50 years; age range, 27-77 years) with TNBC (tumor size on MRI: median, 25.5 mm; range, 11.0-142.0 mm) were included. We analyzed clinical and pathologic variables and MRI parameters. Cox proportional hazards models were used to determine associations with survival outcome. The median follow-up time was 46.1 months (range, 13.9-58.4 months). Eleven of 61 (18.0%) patients had events (i.e., local, regional, or distant recurrence or contralateral breast cancer) and seven (11.5%) died of breast cancer. Among the pretreatment variables, a larger tumor size on MR images (hazard ratio [HR] = 1.024, p = 0.003) was associated with worse DFS at univariate analysis. In multivariate pretreatment models for DSS, a higher fractional volume of extravascular extracellular space per unit volume of tissue (ve) value (HR = 1.658, p = 0.038), higher peak enhancement (HR = 1.843, p = 0.018), and a larger tumor size on MR images (HR = 1.060, p = 0.001) were associated with worse DSS. In multivariate posttreatment models, a larger pathologic tumor size (HR for DFS, 1.074 [p = 0.005]; HR for DSS, 1.050 [p = 0.042]) and metastasis in surgically resected axillary lymph nodes (HR for DFS, 5.789 [p = 0.017]; HR for DSS, 23.717 [p = 0.005]) were associated with worse survival outcome. A higher ve value, higher peak enhancement, and larger tumor size of the primary tumor on pretreatment MRI were independent predictors of worse DSS in patients with TNBC.

Dynamic contrast-enhanced breast magnetic resonance imaging for the prediction of early and late recurrences in breast cancer.

The aim of the study was to evaluate dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) features for the prediction of early and late recurrences in patients with breast cancer.Of 1030 breast cancer patients who underwent surgery at our hospital from January 2007 to July 2011, 83 recurrent breast cancer patients were enrolled in this study. We compared MRI features (background parenchymal enhancement [BPE], internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, and quantitative kinetic parameters) and clinico-pathologic variables (age, stage, histologic grade, nuclear grade, existence of lymphovascular invasion and extensive intraductal carcinoma component, and immunohistochemical profiles) between patients with early (≤2.5 years after surgery) and late recurrence (>2.5 years after surgery). Cox proportional hazard regression analysis was performed to evaluate independent risk factors for early and late recurrence.On breast MRI, prominent ipsilateral whole-breast vascularity was independently associated with early recurrence (hazard ratio [HR], 2.86; 95% confidence intervals [CI], 1.39–5.88) and moderate or marked BPE (HR, 2.08; 95% CI, 1.04–4.18) and rim enhancement (HR, 2.14; 95% CI, 1.00–4.59) were independently associated with late recurrence. Clinico-pathologic variables independently associated with early recurrence included negative estrogen receptor (HR, 0.53; 95% CI, 0.29–0.96), whereas T2 stage (HR, 2.08; 95% CI, 1.04–4.16) and nuclear grade III (HR, 2.54; 95% CI, 1.29–4.98) were associated with late recurrence.In DCE-MRI, prominent ipsilateral whole-breast vascularity, moderate or marked BPE, and rim enhancement could be useful for predicting recurrence timing in patients with breast cancer.

Advanced Breast MRI Techniques: Helpful for Screening Breast Cancer?

Breast magnetic resonance imaging (MRI) is increasingly used in conjunction with mammography as a screening tool to detect breast cancers in asymptomatic high-risk women. Conventional dynamic contrast-enhanced (DCE) breast MRI has a high sensitivity but only moderate specificity for the detection of breast cancer. The primary goal of developing and applying advanced breast MRI techniques that can assess tissue biology is to improve lesion specificity. This review provides a summary of the advances in DCE-MRI techniques and the use of diffusion-weighted imaging and magnetic resonance spectroscopy for breast cancer detection. Publications on the use of these advanced MRI techniques are largely single-institution studies with small numbers of patients, which limits the generalization of this data to a wider screening population. In their current forms, these adjunctive techniques require further research, incorporating an expanded patient population, to validate their utility for breast cancer screening.

Diagnostic Performance of Breast Magnetic Resonance Imaging in Non-Calcified Equivocal Breast Findings: Results from a Systematic Review and Meta-Analysis.

ObjectivesTo evaluate the performance of MRI for diagnosis of breast cancer in non-calcified equivocal breast findings.Materials and MethodsWe performed a systematic review and meta-analysis of peer-reviewed studies in PubMed from 01/01/1986 until 06/15/2015. Eligible were studies applying dynamic contrast-enhanced breast MRI as an adjunct to conventional imaging (mammography, ultrasound) to clarify equivocal findings without microcalcifications. Reference standard for MRI findings had to be established by histopathological sampling or imaging follow-up of at least 12 months. Number of true or false positives and negatives and other characteristics were extracted, and possible bias was determined using the QUADAS-2 applet. Statistical analyses included data pooling and heterogeneity testing.ResultsFourteen out of 514 studies comprising 2,316 lesions met our inclusion criteria. Pooled diagnostic parameters were: sensitivity (99%, 95%-CI: 93–100%), specificity (89%, 95%-CI: 85–92%), PPV (56%, 95%-CI: 42–70%) and NPV (100%, 95%-CI: 99–100%). These estimates displayed significant heterogeneity (P<0.001).ConclusionsBreast MRI demonstrates an excellent diagnostic performance in case of non-calcified equivocal breast findings detected in conventional imaging. However, considering the substantial heterogeneity with regard to prevalence of malignancy, problem solving criteria need to be better defined.

B1 Field Correction of T1 Estimation Should Be Considered for Breast Dynamic Contrast-enhanced MR Imaging Even at 1.5 T.

Purpose To prospectively quantify the effect of T1 estimation in fat by B1 correction in breast magnetic resonance (MR) imaging at 1.5 T and to examine the subsequent quantitative dynamic contrast material-enhanced parameters in breast cancer with and without B1 correction. Materials and Methods This study had institutional review board approval, and informed consent was obtained from 72 patients with breast cancer before breast MR imaging studies were performed between January and July 2015. B1+ field and variable flip angle (FA) mapping were included in the dynamic contrast-enhanced breast MR imaging protocol with a 1.5-T MR imaging system. Precontrast T1 relaxation in fat and breast tumors was computed with and without B1 correction. The pharmacokinetic parameters of breast cancer were calculated by using the Tofts model with T1 values before and after B1 correction. The Mann-Whitney U test and linear regression model were used for statistical analysis. Results The FA was 19% higher in the left breast and 3% lower in the right breast than the prescribed value. This 22% average FA difference created a 43% T1 estimation bias in fat between the breasts. The T1 variation in fat was reduced to 0.96% after B1 correction. There was a 50% overestimation and a 7% underestimation of tumor T1 in the left breast and the right, respectively, associated with B1 error. Assuming T1 after B1 correction represents the true tumor T1, 41% underestimation in the left breast and 10% overestimation in the right without B1 correction were seen in the dynamic contrast-enhanced parameters (including the volume transfer constant, or Ktrans, fraction of extracellular extravascular space, or ve, and blood normalized initial area under the gadolinium concentration curve to 90 seconds, or IAUGCBN90). Conclusion B1 correction for more accurate T1 values should be considered for quantitative dynamic contrast-enhanced breast MR imaging, even at 1.5 T, to offset significant systemic error. © RSNA, 2016.

Invasive Breast Cancer Preferably and Predominantly Occurs at the Interface Between Fibroglandular and Adipose Tissue

BackgroundIncreasing evidence suggests adipocyte involvement in malignant breast tumor invasive front or margin. The aim of this study was to evaluate the location of invasive breast tumors in relation to fibroglandular and adipose tissue by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Patients and MethodsPretreatment breast DCE-MRI images of 294 patients with biopsy-proven invasive breast cancer from 2008 to 2014 were studied. Invasive breast tumors were visualized as enhanced lesions in the postcontrast subtraction images. Positive identification of biopsy-confirmed invasive breast tumors on DCE-MRI images was achieved by correlation of findings from breast MRI and pathology reports. Tumor location in relation to fibroglandular and adipose tissue was investigated using precontrast T1-weighted MRI images. ResultsOf 294 patients, 291 had DCE-MRI discernable invasive breast tumors located at the interface between fibroglandular and adipose tissues, regardless of the tumor size, type, receptor status, or breast composition. ConclusionInvasive breast cancer preferably and predominantly occurs adjacent to breast adipose tissue.