Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.

Purpose: Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels. Approach: A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c-means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings. Results: Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p<0.001). Scores from all breast regions performed significantly better than guessing (p<0.025 from the z-test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points. Conclusions: Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging.

Purpose: Given the dependence of radiomic-based computer-aided diagnosis artificial intelligence on accurate lesion segmentation, we assessed the performances of 2D and 3D U-Nets in breast lesion segmentation on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) relative to fuzzy c-means (FCM) and radiologist segmentations. Approach: Using 994 unique breast lesions imaged with DCE-MRI, three segmentation algorithms (FCM clustering, 2D and 3D U-Net convolutional neural networks) were investigated. Center slice segmentations produced by FCM, 2D U-Net, and 3D U-Net were evaluated using radiologist segmentations as truth, and volumetric segmentations produced by 2D U-Net slices and 3D U-Net were compared using FCM as a surrogate reference standard. Fivefold cross-validation by lesion was conducted on the U-Nets; Dice similarity coefficient (DSC) and Hausdorff distance (HD) served as performance metrics. Segmentation performances were compared across different input image and lesion types. Results: 2D U-Net outperformed 3D U-Net for center slice (DSC, HD p<0.001) and volume segmentations (DSC, HD p<0.001). 2D U-Net outperformed FCM in center slice segmentation (DSC p<0.001). The use of second postcontrast subtraction images showed greater performance than first postcontrast subtraction images using the 2D and 3D U-Net (DSC p<0.05). Additionally, mass segmentation outperformed nonmass segmentation from first and second postcontrast subtraction images using 2D and 3D U-Nets (DSC, HD p<0.001). Conclusions: Results suggest that 2D U-Net is promising in segmenting mass and nonmass enhancing breast lesions from first and second postcontrast subtraction MRIs and thus could be an effective alternative to FCM or 3D U-Net.

Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Comparison of DCE-MRI of murine model cancers with a low dose and high dose of contrast agent.

There are increasing concerns regarding intracellular accumulation of gadolinium (Gd) after multiple dynamic contrast enhanced (DCE) MRI scans. We investigated whether a low dose (LD) of Gd-based contrast agent is as effective as a high dose (HD) for quantitative analysis of DCE-MRI data, and evaluated the use of a split dose protocol to obtain new diagnostic parameters. Female C3H mice (n = 6) were injected with mammary carcinoma cells in the hind leg. MRI experiments were performed on 9.4 T scanner. DCE-MRI data were acquired with 1.5 s temporal resolution before and after a LD (0.04 mmol/kg), then again after 30 min followed by a HD (0.2 mmol/kg) bolus injection of Omniscan. The standard Tofts model was used to extract physiological parameters (Ktrans and ve) with the arterial input function derived from muscle reference tissue. In addition, an empirical mathematical model was used to characterize maximum contrast agent uptake (A), contrast agent uptake rate (α) and washout rate (ß and γ). There were moderate to strong correlations (r = 0.69-0.97, p < 0001) for parameters Ktrans, ve, A, α and ß from LD versus HD data. On average, tumor parameters obtained from LD data were significantly larger (p < 0.05) than those from HD data. The parameter ratios, Ktrans, ve, A and α calculated from the LD data divided by the HD data, were all significantly larger than 1.0 (p < 0.003) for tumor. T2* changes following contrast agent injection affected parameters calculated from HD data, but this was not the case for LD data. The results suggest that quantitative analysis of LD data may be at least as effective for cancer characterization as quantitative analysis of HD data. In addition, the combination of parameters from two different doses may provide useful diagnostic information.

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Dynamic contrast-enhanced magnetic resonance imaging for risk-stratified screening in women with BRCA mutations or high familial risk for breast cancer: are we there yet?

PURPOSE: Women at an elevated lifetime risk for breast cancer (BC), including carriers of pathogenic mutations in BC predisposition genes, are recommended intensified BC screening that includes annual mammography (MG) and annual breast MRI. Controversy exists regarding the clinical utility of MRI as a screening tool in high-risk women. This paper is intended to review recent advances and remaining areas of uncertainty in order to further facilitate the incorporation of breast MRI into an intensified BC screening protocol for women at high familial risk and BRCA carriers. METHODS: A multidisciplinary team of medical oncologists and a radiologist specializing in the treatment of BC and high-risk patients searched PubMed to identify studies deemed to have the highest scientific value. Since none of the initial MRI studies were randomized, meta-analyses examining breast MRI screening in high-risk women were prioritized for inclusion. RESULTS: Breast MRI performs well in high-risk women, including mutation carriers. Breast MRI screening allows for the detection of early stage, likely curable invasive BC. It is mandatory that radiologists receive appropriate MRI training to reduce false positives and unnecessary biopsies. MRI screening is cost-effective in the highest risk patients and new clinical trials are open examining abbreviated and ultra-fast MRI techniques as a tool to drive down costs and improve specificity. CONCLUSIONS: As breast MRI is recommended as part of an intensified screening program in addition to mammography for high-risk women, it important that health care providers understand the benefits and limitations of this screening modality for high-risk women, as well as areas for further investigation.

Nonmass Enhancement Breast Lesions: Diagnostic Performance of Kinetic Assessment on Ultrafast and Standard Dynamic Contrast-Enhanced MRI in Comparison With Morphologic Evaluation.

OBJECTIVE. The purpose of this article was to evaluate the diagnostic performance of the kinetic parameters of ultrafast and standard dynamic contrast-enhanced MRI (DCE-MRI) compared with morphologic evaluation in differentiating benign from malignant nonmass enhancement (NME) breast lesions. MATERIALS AND METHODS. A total of 77 consecutive patients with 77 NMEs (23 benign and 54 malignant) underwent 3-T MRI, including one unenhanced and eight contrast-enhanced ultrafast DCE-MRI scans (7-second scans) and standard DCE-MRI scans. The two readers evaluated the lesions' likelihood of malignancy on a continuous scale from 0 to 100% as the morphologic score using standard DCE-MRI. The kinetic curves of ultrafast DCE-MRI were fitted using an empirical mathematical model, ΔS(t) = A × (1 - e-αt), where A is the upper limit of signal intensity, e is the Euler number, and alpha (s-1) is the rate of signal increase. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC30, which is the integration of the kinetic curve from 0 to 30 seconds) were calculated. From standard DCE-MRI, initial enhancement rate and signal enhancement ratio (SER) were calculated. These parameters were compared between benign and malignant NMEs. RESULTS. The morphologic score of malignant NME was statistically significantly higher than that of benign NME (p < 0.0001). The upper limit of signal intensity, rate of signal increase, initial slope of the kinetic curve, and AUC30 of ultrafast DCE-MRI, initial enhancement rate, SER of standard DCE-MRI of malignant NMEs were statistically significantly higher than those of benign NMEs (p = 0.0011, 0.0045, < 0.0001, < 0.0001, 0.0017, and < 0.0001, respectively). AUC ROC analysis found no statistically significant difference between morphologic score, AUC30 of ultrafast DCE-MRI, or SER of standard DCE-MRI. CONCLUSION. The kinetic parameters of ultrafast and standard DCE-MRI were as effective as morphologic evaluation for differentiation between benign and malignant NMEs.

Artificial intelligence in the interpretation of breast cancer on MRI.

Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI-based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient-specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1310-1324.

Low-dose imaging technique (LITE) MRI: initial experience in breast imaging.

OBJECTIVES: To compare a low-dose dynamic contrast-enhanced breast MRI protocol (LITE MRI) to standard-dosage using a dual-dose injection technique. METHODS: 8 females with a total of 10 lesions with imaging features compatible with fibroadenoma were imaged using a dual-dose dynamic contrast-enhanced-MRI (DCE-MRI) technique. After pre-contrast scans, 15% of a standard dose of contrast was administered; approximately 10 min later, the remaining 85% of the standard dose was administered. Enhancement kinetic parameters, conspicuity and signal-to-noise ratio were measured quantitatively. RESULTS: One lesion showed no enhancement in either DCE series. All nine of the enhancing lesions were visualized in both the low-dose and standard-dose images. While the (low-to-standard) ratio of contrast doses was roughly 0.18, this did not match the ratios of kinetic parameters. Lesion conspicuity and enhancement rate were both higher in the low-dose images, with (low-to-standard) ratios 1.5 ± 0.1 and 1.2 ± 0.4, respectively. The upper limit of enhancement (ratio 0.3 ± 0.1) and signal-to-noise ratio (ratio 0.5 ± 0.1) were higher in the standard-dose images, but less than expected based on the ratio of the doses. CONCLUSIONS: This preliminary study demonstrates that LITE MRI has the potential to match standard DCE-MRI in the detection of enhancing lesions. Additionally, LITE MRI may enhance sensitivity to contrast media dynamics. ADVANCES IN KNOWLEDGE: Lower doses of MRI contrast media may be equally effective in the detection of breast lesions, and increase sensitivity to contrast media dynamics. LITE MRI may help increase screening compliance and long-term patient safety.

Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma.

Background Previous studies have suggested that breast parenchymal texture features may reflect the biologic risk factors associated with breast cancer development. Therefore, combining the characteristics of normal parenchyma from the contralateral breast with radiomic features of breast tumors may improve the accuracy of digital mammography in the diagnosis of breast cancer. Purpose To determine whether the addition of radiomic analysis of contralateral breast parenchyma to the characterization of breast lesions with digital mammography improves lesion classification over that with radiomic tumor features alone. Materials and Methods This HIPAA-compliant, retrospective study included 182 patients (age range, 25-90 years; mean age, 55.9 years ± 14.9) who underwent mammography between June 2002 and July 2009. There were 106 malignant and 76 benign lesions. Automatic lesion segmentation and radiomic analysis were performed for each breast lesion. Radiomic texture analysis was applied in the normal regions of interest in the contralateral breast parenchyma to assess the mammographic parenchymal patterns. The classification performance of both individual features and the output from a Bayesian artificial neural network classifier was evaluated with the leave-one-patient-out method by using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of differentiating between malignant and benign lesions. Results The performance of the combined lesion and parenchyma classifier in the differentiation between malignant and benign mammographic lesions was better than that with the lesion features alone (AUC = 0.84 ± 0.03 vs 0.79 ± 0.03, respectively; P = .047). Overall, six radiomic features-spiculation, margin sharpness, size, circularity from the tumor feature set, and skewness and power law beta from the parenchymal feature set-were selected more than 50% of the time during the feature selection process on the combined feature set. Conclusion Combining quantitative radiomic data from tumors with contralateral parenchyma characterizations may improve diagnostic accuracy for breast cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Shaffer in this issue.

Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.

RATIONALE AND OBJECTIVES: With the growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening, we compare the performance of deep learning computer-aided diagnosis on DBT images to that of conventional full-field digital mammography (FFDM). MATERIALS AND METHODS: In this study, we retrospectively collected FFDM and DBT images of 78 biopsy-proven lesions from 76 patients. A region of interest was selected for each lesion on FFDM, synthesized 2D, and DBT key slice images. Features were extracted from each lesion using a pretrained convolutional neural network (CNN) and served as input to a support vector machine classifier trained in the task of predicting likelihood of malignancy. RESULTS: From receiver operating characteristic (ROC) analysis of all 78 lesions, the synthesized 2D image performed best in both the cradiocaudal view (area under the ROC curve [AUC] = 0.81, SE = 0.05) and mediolateral oblique view (AUC = 0.88, SE = 0.04) in the task of lesion characterization. When cradiocaudal and mediolateral oblique data of each lesion were merged through soft voting, DBT key slice image performed best (AUC = 0.89, SE = 0.04). When only masses and architectural distortions (ARDs) were considered, DBT performed significantly better than FFDM (p = 0.024). CONCLUSION: DBT performed significantly better than FFDM in the merged view classification of mass and ARD lesions. The increased performance suggests that the information extracted by the CNN from DBT images may be more relevant to lesion malignancy status than the information extracted from FFDM images. Therefore, this study provides supporting evidence for the efficacy of computer-aided diagnosis on DBT in the evaluation of mass and ARD lesions.

Intensive Surveillance with Biannual Dynamic Contrast-Enhanced Magnetic Resonance Imaging Downstages Breast Cancer in BRCA1 Mutation Carriers.

PURPOSE: To establish a cohort of high-risk women undergoing intensive surveillance for breast cancer.Experimental Design: We performed dynamic contrast-enhanced MRI every 6 months in conjunction with annual mammography (MG). Eligible participants had a cumulative lifetime breast cancer risk ≥20% and/or tested positive for a pathogenic mutation in a known breast cancer susceptibility gene. RESULTS: Between 2004 and 2016, we prospectively enrolled 295 women, including 157 mutation carriers (75 BRCA1, 61 BRCA2); participants' mean age at entry was 43.3 years. Seventeen cancers were later diagnosed: 4 ductal carcinoma in situ (DCIS) and 13 early-stage invasive breast cancers. Fifteen cancers occurred in mutation carriers (11 BRCA1, 3 BRCA2, 1 CDH1). Median size of the invasive cancers was 0.61 cm. No patients had lymph node metastasis at time of diagnosis, and no interval invasive cancers occurred. The sensitivity of biannual MRI alone was 88.2% and annual MG plus biannual MRI was 94.1%. The cancer detection rate of biannual MRI alone was 0.7% per 100 screening episodes, which is similar to the cancer detection rate of 0.7% per 100 screening episodes for annual MG plus biannual MRI. The number of recalls and biopsies needed to detect one cancer by biannual MRI were 2.8 and 1.7 in BRCA1 carriers, 12.0 and 8.0 in BRCA2 carriers, and 11.7 and 5.0 in non-BRCA1/2 carriers, respectively. CONCLUSIONS: Biannual MRI performed well for early detection of invasive breast cancer in genomically stratified high-risk women. No benefit was associated with annual MG screening plus biannual MRI screening.See related commentary by Kuhl and Schrading, p. 1693.

Diagnostic value of electric properties tomography (EPT) for differentiating benign from malignant breast lesions: comparison with standard dynamic contrast-enhanced MRI.

OBJECTIVES: To evaluate the diagnostic utility of electric properties tomography (EPT) in differentiating benign from malignant breast lesions in comparison with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: In this institutional review board-approved retrospective study, 116 consecutive patients with 141 breast lesions (50 benign and 91 malignant) underwent 3-T MRI, including 3D turbo-spin echo (TSE) sequence and standard DCE-MRI scans between January 2014 and January 2017. The lesions were segmented semi-automatically using subtraction DCE-MR images, and they were registered to the phase images from 3D TSE. The mean conductivity of the lesion was obtained from phase-based reconstruction of lesions. From the DCE-MRI, initial enhancement rate (IER) and signal enhancement ratio (SER) were calculated from signal intensity (SI) as follows: IER = (SIearly - SIpre)/SIpre, SER = (SIearly - SIpre)/(SIdelayed - SIpre). The parameters from EPT and the DCE-MRI were compared between benign and malignant lesions. RESULTS: There was significant difference in mean conductivity (0.14 ± 1.77 vs 1.14 ± 1.36 S/m, p < 0.0001) and SER (0.77 ± 0.28 vs 1.04 ± 0.25, p < 0.0001) between benign and malignant lesions, but not in IER (p = 0.06). Receiver operating curve (ROC) analysis revealed that the area under the curve (AUC) of the mean conductivity and SER was 0.71 and 0.80, respectively, without significant difference (p = 0.15). CONCLUSIONS: The mean conductivity of EPT was significantly different between benign and malignant breast lesions as well as kinetic parameter or SER from DCE-MRI. KEY POINTS: • The conductivity of malignant lesions was higher than that of benign lesions. • EPT helps differentiatie benign from malignant lesions. • Diagnostic ability of EPT was not significantly different from that of DCE-MRI.

Lymph node wire localization post-chemotherapy: Towards improving the false negative sentinel lymph node biopsy rate in breast cancer patients.

PURPOSE: To evaluate whether the disease status of the pre-neoadjuvant chemotherapy (NAC) core biopsied lymph node (preNACBxLN) in patients with node positive breast cancer corresponds to nodal status of all surgically retrieved lymph nodes (LNs) post-NAC and whether wire localization of this LN is feasible. MATERIALS AND METHODS: HIPPA compliant IRB approved retrospective study including breast cancer patients (a.) with preNACBxLN confirmed metastases, (b.) who received NAC, and (c.) underwent wire localization of the preNACBxLN. Electronic medical records were reviewed. Fisher's exact test was used to compare differences in residual disease post-NAC among breast cancer subtypes. RESULTS: 28 women with node positive breast cancer underwent ultrasound guided wire localization of the preNACBxLN, without complication. There was no evidence of residual nodal disease for 16 patients, with mean 4.4 (median 4) LNs resected. 12 patients had residual nodal metastases, with mean 9.2 (median 7) LNs resected and mean 2.3 (median 2) LNs with tumor involvement. 11 patients had metastases detected within the localized LN. One patient had micrometastasis in a sentinel LN, despite no residual disease in the preNACBxLN. Patients with luminal A/B breast cancer more often had residual nodal metastases (86%) at pathology, as compared to patients with HER2+ (20%) and Triple Negative breast cancer (50%), though not quite achieving statistical significance (p=0.055). CONCLUSION: Ultrasound guided wire localization of the preNACBxLN is feasible and may improve detection of residual tumor in patients post-NAC.

Abbreviated MRI and Accelerated MRI for Screening and Diagnosis of Breast Cancer.

Although published studies have revealed that magnetic resonance imaging (MRI) is by far the most effective imaging modality for cancer detection, it is currently considered cost-ineffective for screening women at an intermediate risk for breast cancer. The concept of an "abbreviated MRI" protocol has recently emerged as a possible solution for reducing the cost of MRI. The abbreviated MRI is a shortened version of the standard MRI, consisting of a single early phase dynamic contrast enhanced (DCE) series. Several clinical studies have shown that this MRI protocol would not affect sensitivity or specificity for breast MRI screening purposes. In clinical practice, morphologic evaluation and kinetic assessment are 2 major components of the interpretation process. However, kinetic assessment cannot be performed with the abbreviated protocol, because multiple sets of post-contrast images are necessary for the generation of kinetic curves. "Accelerated MRI" is a collective term for imaging techniques that acquire DCE-MR images in a very short time. Published studies suggest that the kinetic assessment during the very early post-contrast phase obtained with the accelerated MRI techniques is comparable to that with the standard MRI techniques. Applying accelerated MR techniques could potentially enhance the abbreviated MRI protocol in terms of diagnostic potential, while maintaining the shorter study time. Thus, the abbreviated MRI protocol associated with accelerated MRI techniques may provide value for screening and for diagnostic purposes.

Value of breast MRI for patients with a biopsy showing atypical ductal hyperplasia (ADH).

PURPOSE: To evaluate the diagnostic value of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for patients with atypical ductal hyperplasia (ADH) in predicting malignant upgrade. MATERIALS AND METHODS: 3T DCE-MRI was performed for 17 patients with ADH (median age 52, range 42-76) proven by stereotactic biopsy (n = 15), and ultrasound-guided biopsy (n = 2) from January 2011 to April 2015. All patients underwent surgical excision after the MRI. Two radiologists prospectively reviewed the MRI to determine the presence or absence of suspicious findings at the site of biopsy, and evaluated the MR features of any lesion present according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon. MRI findings and clinical information were correlated with the final surgical pathology by multivariate analysis. RESULTS: Nine of 17 lesions were upgraded to malignancy. MRI demonstrated suspicious nonmass enhancement (NME) at the site of biopsy in all upgraded patients. The median size was 19.5 mm (range, 9-44 mm). In the eight patients without upgrade, no enhancement (n = 2), linear enhancement along the biopsy track (n = 4), thin rim enhancement around hematoma (n = 1), and a focal NME (n = 1) were seen. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MRI findings were 100, 87.5, 90, and 100%, respectively. Multivariate analysis revealed that the presence of suspicious enhancement on MRI was the most significant predictor of upgrade to malignancy (P = 0.0006) CONCLUSION: Our study revealed a high NPV of DCE-MRI for patients with ADH in terms of malignant upgrade at subsequent surgery. This suggests that patients with ADH without suspicious enhancement on DCE-MRI might be followed with DCE-MRI rather than undergoing surgical excision. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1738-1747.

Fast bilateral breast coverage with high spectral and spatial resolution (HiSS) MRI at 3T.

PURPOSE: To develop and assess a full-coverage, sensitivity encoding (SENSE)-accelerated breast high spatial and spectral resolution (HiSS) magnetic resonance imaging (MRI) within clinically reasonable times as a potential nonenhanced MRI protocol for breast density measurement or breast cancer screening. MATERIALS AND METHODS: Sixteen women with biopsy-proven cancer or suspicious lesions, and 13 women who were healthy volunteers or were screened for breast cancer, received 3T breast MRI exams, including SENSE-accelerated HiSS MRI, which was implemented as a submillimeter spatial resolution echo-planar spectroscopic imaging (EPSI) sequence. In postprocessing, fat and water resonance peak height and integral images were generated from EPSI data. The postprocessing software was custom-designed, and new algorithms were developed to enable processing of whole-coverage axial HiSS datasets. Water peak height HiSS images were compared to pre- and postcontrast T1 -weighted images. Fat suppression was quantified as parenchymal-to-suppressed-fat signal ratio in HiSS water peak height and nonenhanced T1 -weighted images, and artifact levels were scored. RESULTS: Approximately a 4-fold decrease in acquisition speed, with a concurrent 2.5-fold decrease in voxel size, was achieved, with low artifact levels, and with spectral signal-to-noise ratio (SNR) of 45:1. Fat suppression was 1.9 times more effective (P < 0.001) in HiSS images than in T1 -weighted images (SPAIR), and HiSS images showed higher SNR in the axilla. HiSS MRI visualized 10 of 13 malignant lesions identified on dynamic contrast-enhanced (DCE)-MRI, and did not require skin removal in postprocessing to generate maximum intensity projection images. CONCLUSION: We demonstrate full-coverage, SENSE-accelerated breast HiSS MRI within clinically reasonable times, as a potential protocol for breast density measurement or breast cancer screening. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1341-1348.