BACKGROUND: Nonmass enhancement (NME) on breast MRI impacts surgical planning.
OBJECTIVE: To evaluate positive predictive values (PPVs) and identify malignancy discriminators of NME ipsilateral to breast cancer on initial staging MRI.
METHODS: Retrospective.
METHODS: Eighty-six women (median age, 48 years; range, 26-75 years) with 101 NME lesions (BI-RADS 4 and 5) ipsilateral to known cancers and confirmed histopathology.
UNASSIGNED: 1.5 T and 3.0 T dynamic contrast-enhanced fat-suppressed T1-weighted fast spoiled gradient-echo.
RESULTS: Three radiologists blinded to pathology independently reviewed MRI features (distribution, internal enhancement pattern, and enhancement kinetics) of NME, locations relative to index cancers (contiguous, non-contiguous, and different quadrants), associated mammographic calcifications, lymphovascular invasion (LVI), axillary node metastasis, and radiology-pathology correlations. Clinical factors, NME features, and cancer characteristics were analyzed for associations with NME malignancy.
METHODS: Fisher\'s exact, Chi-square, Wilcoxon rank sum tests, and mixed-effect multivariable logistic regression were used. Significance threshold was set at P < 0.05.
RESULTS: Overall NME malignancy rate was 48.5% (49/101). Contiguous NME had a significantly higher malignancy rate (86.7%) than non-contiguous NME (25.0%) and NME in different quadrants (10.7%), but no significant difference was observed by distance from cancer for non-contiguous NME, P = 0.68. All calcified NME lesions contiguous to the calcified index cancer were malignant. NME was significantly more likely malignant when index cancers were masses compared to NME (52.9% vs. 21.4%), had mammographic calcifications (63.2% vs. 39.7%), LVI (81.8% vs. 44.4%), and axillary node metastasis (70.8% vs. 41.6%). NME features with highest PPVs were segmental distribution (85.7%), clumped enhancement (66.7%), and nonpersistent kinetics (77.1%). On multivariable analysis, contiguous NME, segmental distribution, and nonpersistent kinetics were associated with malignancy.
CONCLUSIONS: Malignancy discriminators of ipsilateral NME on staging MRI included contiguous location to index cancers, segmental distribution, and nonpersistent kinetics.
METHODS: 3 TECHNICAL EFFICACY: Stage 2.

Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable.
To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images.
Retrospective.
A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results.
A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence.
In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort.
Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant.
For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635.
Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions.
4.
Stage 2.

Nonmass enhancement (NME) on breast magnetic resonance imaging (MRI) is defined as an area whose internal enhancement characteristics can be distinguished from the normal surrounding breast parenchyma, without an associated mass in the Breast Imaging Reporting and Data System lexicon. In this study, we evaluated the pathologic correlates of NME lesions of the breast identified on MRI at our institution, including the frequency of atypical or malignant lesions in the core needle biopsies (CNBs), performed after such a radiologic finding. A retrospective study was performed on all CNBs performed for NME on breast MRI between 2010 and 2019. A total of 443 biopsies from 411 patients were identified, comprising 5.5% of all CNBs over the study period. The pathologic diagnoses were benign in the majority of the biopsies (68.0%), whereas 11.5% and 20.5% of the cases were atypical and malignant lesions, respectively. Of the malignant cases, 69.2% were ductal carcinoma in situ (DCIS) and 30.8% were invasive carcinomas. The most common invasive cancer was invasive ductal carcinoma (50%), followed by invasive lobular carcinoma (39.3%). NME identified on breast MRI carried a significant (32%) risk of atypia and malignancy in our cohort, which confirms that biopsy evaluation of these lesions is warranted. DCIS was the most commonly identified malignancy. Notably, among invasive cancers, invasive lobular carcinoma was identified at a substantially higher frequency that would be expected for that histotype.

UNASSIGNED: Challenges in differentiation between clinically noninflammatory granulomatous lobular mastitis (GLM) and noncalcified ductal carcinoma in situ (DCIS) remain.
UNASSIGNED: To identify the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics contributing to their differential diagnosis.
UNASSIGNED: A total of 33 clinically noninflammatory GLM and 36 noncalcified DCIS were retrospectively analyzed in the study. Internal enhancement of a nonmass enhancement (NME) lesion was divided into clustered enhanced ring (absence/presence), and clustered enhanced ring (presence) was further classified as small and large ring based on the optimal cutoff value. The 5th Breast Imaging and Data System MRI descriptors were used for assessing the other DCE-MRI characteristics. Multivariate analysis including variables with significant differences in univariate analyses was conducted to identify the independent predictors. The discriminative abilities of different predictors and their combination were compared by area under the receiver-operating characteristic curves (AUCs).
UNASSIGNED: An NME lesion was seen more commonly in clinically noninflammatory GLM than in noncalcified DCIS (p = 0.003). DCE-MRI characteristics with significant differences in univariate analyses included NME size, clustered enhanced ring (absence/presence), ring size, initial increase and kinetic characteristics for the differentiation between these two entities presenting as NME lesion. Clustered enhanced ring (presence) was further classified as small (≤7 mm) or large ring (>7 mm). Multivariate analysis revealed that internal enhancement and initial increase were identified as significant independent predictors, and the AUC of their combination achieved the highest value of 0.867 (95% CI, 0.748-0.943).
UNASSIGNED: An NME lesion with a large ring is more highly suggestive of clinically noninflammatory GLM.

Pseudoangiomatous stromal hyperplasia (PASH), a rare, noncancerous lesion, is often an incidental finding on magnetic resonance imaging (MRI)-guided biopsy analysis of other breast lesions. We sought to describe the characteristics of PASH on MRI and identify the extent to which these characteristics are correlated with the amount of PASH in the pathology specimens. We identified 69 patients who underwent MRI-guided biopsies yielding a final pathological diagnosis of PASH between 2008 and 2015. We analyzed pre-biopsy MRI scans to document the appearance of the lesions of interest. All biopsy samples were classified as having ≤50% PASH or ≥51% PASH present on the pathological specimen. On MRI, 9 lesions (13%) appeared as foci, 19 (28%) appeared as masses with either washout or persistent kinetics, and 41 (59%) appeared as regions of nonmass enhancement. Of this latter group, 33 lesions (80%) showed persistent kinetic features. Masses, foci, and regions of nonmass enhancement did not significantly correlate with the percentage of PASH present in the biopsy specimens (P ≥ .05). Our findings suggest that PASH has a wide-ranging appearance on MRI but most commonly appears as a region of nonmass enhancement with persistent kinetic features. Our finding that most specimens had ≤50% PASH supports the notion that PASH is usually an incidental finding. We did not identify a definitive imaging characteristic that reliably identifies PASH.

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.

Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spatially and temporally inter-connected regions of enhancements, so they can be modeled as networks and their properties characterized via network-based connectivity. In this work, we represented nonmass lesions as graphs using a link formation energy model that favors linkages between regions of similar enhancement and closer spatial proximity. However, adding graph features to an existing computer-aided diagnosis (CAD) pipeline incurs an increase of feature space dimensionality, which poses additional challenges to traditional supervised machine learning techniques due to the inability to increase accordingly the number of training datasets. We propose the combination of unsupervised dimensionality reduction and embedded space clustering followed by a supervised classifier to improve the performance of a CAD system for nonmass-like lesions in breast MRI. Our work extends a previoulsy proposed framework for deep embedded unsupervised clustering (DEC) to embedding space classification, with the joint optimization of objective functions for DEC and supervised multi-layered perceptron (MLP) classification. The strength of the method lies in the ability to learn and further optimize an embedded feature representation of lower dimensionality that maximizes the diagnostic accuracy of a CAD lesion classifier to discriminate between benign and malignant lesions. We identified 792 nonmass-like enhancements (267 benign, 110 malignant and 415 unknown) in 411 patients undergoing breast MRI at our institution. The diagnostic performance of the proposed method was evaluated and compared to the performance of a conventional supervised MLP classifier in original feature space. A statistically significant increase in diagnostic area under the ROC curve (AUC) was achieved. Generalization AUC increased from 0.67 ± 0.08 to 0.81 ± 0.10 (21% increase, p-value=4.2×10-8) with the proposed graph-based lesion characterization and deep embedding framework.

OBJECTIVE: The purpose of this study is to assess the frequency of reclassification of nonmass enhancement (NME) as background parenchymal enhancement (BPE) and to determine positive predictive values (PPVs) of NME descriptors using the revised BI-RADS atlas.
METHODS: A retrospective review of our institution\'s MRI database from January 1, 2009, through March 30, 2012, identified 6220 contrast-enhanced breast MRI examinations. All findings prospectively assessed as NME and rated as BI-RADS categories 3, 4, and 5 (n = 386) were rereviewed in consensus by two radiologists who were blinded to pathologic findings with the fifth edition of the BI-RADS lexicon. Findings considered as postsurgical, associated with known cancers, NME given a BI-RADS category 3 assessment before 2009, previously biopsied, and those reclassified as BPE, focus, or mass were excluded (n = 181). Medical records were reviewed for demographics and outcomes.
RESULTS: Two hundred five women were included (mean age, 48.8 years; range, 21-84 years). Seventy-seven of 386 findings (20.0%) were reclassified as BPE, and patients with BPE were younger than those with NME (mean age, 43.9 years; range, 31-62 years) (p = 0.003). Pathology results for 144 of 205 (70.2%) patients included 52 malignant, 11 high-risk, and 81 benign lesions. The highest PPVs for distribution patterns were 34.5% (10/29) for segmental and 100.0% (3/3) for diffuse distribution. The highest PPVs for internal enhancement patterns were 36.7% (11/30) for clustered ring enhancement and 27.5% (11/40) for clumped enhancement. No difference for NME malignancy rate was noted according to BPE (10/52 [19.2%] marked or moderate; 42/153 [27.5%] mild or minimal, p = 0.24). Thirty-two percent (17/52) of malignant NMEs had high T2 signal.
CONCLUSIONS: Careful assessment of findings as BPE versus NME can improve PPVs, particularly in younger women. Although clustered ring enhancement had one of the study\'s highest PPVs, this number falls below previously published rates. Reliance on T2 signal as a benign feature may be misleading, because one-third of malignancies had T2 signal.

The purpose of this study was to verify the utility of second-look ultrasonography (US) in evaluating nonmass enhancement (NME) lesions detected on breast magnetic resonance imaging (MRI) by analysing its correlation and imaging features. From July 2008 to June 2012, 102 consecutive MRI-detected NME lesions were subsequently evaluated with US. Lesions were evaluated according to the established Breast Imaging Reporting and Data System (BI-RADS) lexicon. The correlation between MRI-detected NME lesion characteristics, lesion size, histopathological findings and features at second-look US were analysed. Second-look US identified 44/102 (43%) of the NME lesions revealed by MRI. A US correlate was seen in 34/45 (76%) malignant lesions compared with 10/57 (18%) benign lesions (p < 0.0001). The likelihood of malignancy was significantly higher for NME lesions with a US correlate than lesions without: 34/44 (77%) versus 11/58 (19%) (p < 0.0001). The malignancy of the 44 (43%) MRI-detected NME lesions with a US correlate was significantly associated with US lesion margins and BI-RADS categories (p = 0.001 and 0.002 respectively). Second-look US of MRI-detected NME lesions is useful for decision-making as part of the diagnostic workup. Familiarity with the US features associated with malignancy improves the utility of US in the workup of these NME abnormalities.

OBJECTIVE: The purpose of this article is to review the varied appearances and associated diagnoses of nonmass enhancement on breast MRI with radiologic-pathologic correlation.
CONCLUSIONS: Knowledge of the distribution and internal characteristics of these findings is helpful to determine when core needle biopsy is indicated. Correlating imaging with pathologic findings is critical in making appropriate recommendations regarding clinical management.