Characteristics of Suspicious Breast Lesions Visible Only on MR Imaging: Is It Possible to Classify into Immediate Biopsy and Careful Observation Groups?

PURPOSE

To investigate the characteristics of suspicious MRI-only visible lesions and to explore the validity of subcategorizing these lesions into the following two groups: lesions that would require immediate biopsy (4Bi) and lesions for which careful clinical follow-up could be recommended (4Fo).

METHODS

A retrospective review of 108 MRI-only visible lesions in 106 patients who were diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 between June 2018 and June 2022 at our institution was performed by two radiologists. The breast MR images were evaluated according to BI-RADS and additional MRI descriptors (linear ductal, branching, and apparent diffusion coefficient values). The lesions were categorized by previously reported classification systems, and the positive predictive values (PPVs) for the different categories were determined and compared. Subsequently, a new classification system was developed in this study.

RESULTS

The total malignancy rate was 31% (34/108). No significant differences between benign and malignant lesions were identified for focus and mass lesions. For non-mass lesions, linear ductal and heterogeneous internal enhancement suggested a benign lesion (P = 0.0013 and P = 0.023, respectively), and branching internal enhancement suggested malignancy (P = 0.0066). Segmental distribution suggested malignancy (P = 0.0097). However, the PPV of segmental distribution with heterogeneous enhancement was significantly lower than that of category 4 segmental lesions with other enhancement patterns (11% vs. 59%; P = 0.0198).As a new classification, the distribution of focal, linear, and segmental was given a score of 0, 1, or 2, and the internal enhancement of heterogeneous, linear-ductal, clumped, branching, and clustered-ring enhancement was given a score of 0, 1, 2, 3, and 4, respectively. When categorized using a scoring system, a statistically significant difference in PPV was observed between 4Fo (n = 27) and 4Bi (n = 33) (7% vs. 61%, P = 0.000029).

CONCLUSION

The new classification system was found to be highly capable of subcategorizing BI-RADS category 4 MRI-only visible non-mass lesions into 4Fo and 4Bi.

How Well Does Non-mass Enhancement Correlate With DCIS/Invasive Cancer?

INTRODUCTION

Contiguous non-mass enhancement (NME) often coexists with a solid tumor component on MRI, but it can be challenging to predict whether NME represents invasive breast cancer, ductal carcinoma in situ (DCIS), benign disease, or biopsy site reaction. The purpose of this study was to determine the association between the size/extent of NME and the presence of invasive cancer and/or DCIS on final pathology.

METHODS

This was a single institution retrospective analysis of a prospectively maintained breast cancer registry (2010-2020). Female patients who underwent surgical resection were included if they had a diagnosis of invasive breast cancer (with or without DCIS) and had an MRI showing both a solid mass and contiguous NME. The size of NME on MRI was compared with the size of invasive cancer and/or DCIS on the final pathology.

RESULTS

From a total of 3443 patients, 225 patients were included. 86.2% had invasive ductal carcinoma (IDC), and 12.0% had invasive lobular carcinoma 76.9% were ER+, 16.4% were HER2+, and 13.3% were triple negative breast cancer (TNBC). 18.7% received neoadjuvant chemotherapy (NCT) of whom 31% achieved a complete radiographic/pathologic response. Pearson correlation coefficients (r) between the size of NME and invasive cancer/DCIS showed a strong and positive correlation of MRI NME with DCIS on pathology in patients without NCT. Subgroup analysis showed the strongest correlations for NME and DCIS among non-white (r = .70) and HER2 + patients (r = .74) who did not receive NCT.

CONCLUSIONS

Strong correlations between NME and DCIS were found for HER2 + disease and non-white patients, but only modest correlations were found for other patient/disease characteristics. These correlations may impact decisions in surgical approach.

Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

BACKGROUND

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.

PURPOSE

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.

STUDY TYPE

Retrospective.

SUBJECTS

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.

FIELD STRENGTH/SEQUENCE

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.

ASSESSMENT

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.

STATISTICAL TESTS

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.

RESULTS

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.

DATA CONCLUSION

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.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

BI-RADS 3 Assessment on MRI: A Lesion-Based Review for Breast Radiologists

Unlike mammography and US, limited data exist to establish well-defined criteria for MRI findings that have a ≤2% likelihood of malignancy. Therefore, determining which findings are appropriate for a BI-RADS 3 assessment on MRI remains challenging and variable among breast radiologists. Emerging data suggest that BI-RADS 3 should be limited to baseline MRI examinations (or examinations with less than two years of prior comparisons) performed for high-risk screening and only used for masses with all of the typical morphological and kinetic features suggestive of a fibroadenoma or dominant enhancing T2 hypointense foci that is distinct from background parenchymal enhancement and without suspicious kinetics. This article presents an updated discussion of BI-RADS 3 assessment (probably benign) for breast MRI using current evidence.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions

OBJECTIVES

To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images.

METHODS

This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001).

CONCLUSION

Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance.

KEY POINTS

• Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI.

Nonmass enhancement lesions of the breast on core needle biopsy: outcomes, frequency of malignancy, and pathologic findings

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.

Clumped vs non-clumped internal enhancement patterns in linear non-mass enhancement on breast MRI

OBJECTIVE

To compare positive predictive values (PPVs) of clumped vs non-clumped (homogenous and heterogeneous) internal enhancement on MRI detected linear non-mass enhancement (NME) on MRI-guided vacuum-assisted breast biopsy (MRI-VABB).

METHODS

With IRB (Institutional Review Board) approval, we retrospectively reviewed 598 lesions undergoing MRI-VABB from January 2015 to April 2018 that showed linear NME. We reviewed the electronic medical records for MRI-VABB pathology, any subsequent surgery and clinical follow-up. The X² test was performed for univariate analysis.

RESULTS

There were 120/598 (20%) linear NME MRI-VABB lesions with clumped (52/120, 43%) vs non-clumped (68/120, 57%) internal enhancement, average size 1.8 cm (range 0.6-7.6 cm). On MRI-VABB, cancer was identified in 22/120 (18%) lesions, ductal carcinoma in situ (DCIS) was found in 18/22 (82%) and invasive cancer in 4 (18%). 3/31 (10%) high-risk lesions upgraded to DCIS at surgery, for a total of 25/120 (21%) malignancies. Malignancy was found in 12/52 (23%) clumped lesions and in 13/68 (19%) of non-clumped lesions that showed heterogeneous (5/13, 38%) or homogenous (8/13, 62%) internal enhancement. The PPV of linear NME with clumped internal enhancement (23.1%) was not significantly different from the PPV of non-clumped linear NME (19.1%) (p = 0.597). The PPV of linear NME lesions <1 cm (33.3%) was not significantly different from the PPV of lesions ≥1 cm (18.6%) (p = 0.157).

CONCLUSIONS

Linear NME showed malignancy in 21% of our series. Linear NME with clumped or non-clumped internal enhancement patterns, regardless of lesion size, might need to undergo MRI-VABB in appropriate populations.

ADVANCES IN KNOWLEDGE

Evaluation of linear NME lesions on breast MRI focuses especially on internal enhancement pattern.

Differentiation between Clinically Noninflammatory Granulomatous Lobular Mastitis and Noncalcified Ductal Carcinoma in situ Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

<b><i>Introduction:</i></b> Challenges in differentiation between clinically noninflammatory granulomatous lobular mastitis (GLM) and noncalcified ductal carcinoma in situ (DCIS) remain. <b><i>Objective:</i></b> To identify the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics contributing to their differential diagnosis. <b><i>Methods:</i></b> 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). <b><i>Results:</i></b> An NME lesion was seen more commonly in clinically noninflammatory GLM than in noncalcified DCIS (<i>p</i> = 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 (&#x3e;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). <b><i>Conclusions:</i></b> An NME lesion with a large ring is more highly suggestive of clinically noninflammatory GLM.

The MRI characteristics of non-mass enhancement lesions of the breast: associations with malignancy

OBJECTIVE:

The American College of Radiology updated the terms used for expressing the imaging characteristics of non-mass enhancement (NME) lesions in the fifth edition of the breast imaging-reporting data system (BI-RADS) lexicon. Both the distribution and internal enhancement descriptors were revised for NME lesions. Our aim was to determine the MRI characteristics of NME lesions and to investigate their association with malignancy.

METHODS:

The MRI results of 129 NME lesions were retrospectively evaluated. The medical files, biopsy results and follow-up findings of lesions were recorded. Patients who had benign biopsy and those who had stable or regressed lesions during follow-up were classified as benign. All MRI results had been obtained with a 1.5 Tesla Signa HDx MR system (GE Healthcare).

RESULTS:

Segmental and diffuse distribution along with clustered-ring internal enhancement were significantly associated with malignancy, while linear distribution and homogeneous enhancement pattern were associated with benignancy. Additionally, the plateau type (Type II) curve was significantly more frequent in malignant lesions. There was no association between the presence of cystic structures and the benign/malignant nature of the lesion. However, multivariate logistic regression showed that only segmental distribution and diffusion restriction were associated with malignancy.

CONCLUSION:

In the current study, segmental distribution, clustered-ring enhancement, Type II dynamic curve and the presence of diffusion restriction were found to be associated with malignancy. There is a requirement for multicenter studies which include higher numbers of patients in order to better evaluate lesions with rarer characteristics for distribution and enhancement pattern.

ADVANCES IN KNOWLEDGE:

Our aim in this study was to investigate the MRI characteristics of NME lesions. We have reported the MRI findings of NME lesions and have found that segmental distribution and clustered-ring enhancement patterns are significantly more frequent in malignant lesions.

A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions

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.

Grading system to categorize breast MRI using BI-RADS 5th edition: a statistical study of non-mass enhancement descriptors in terms of probability of malignancy

PURPOSE

To analyze the association of breast non-mass enhancement descriptors in the BI-RADS 5th edition with malignancy, and to establish a grading system and categorization of descriptors.

MATERIALS AND METHODS

This study was approved by our institutional review board. A total of 213 patients were enrolled. Breast MRI was performed with a 1.5-T MRI scanner using a 16-channel breast radiofrequency coil. Two radiologists determined internal enhancement and distribution of non-mass enhancement by consensus. Corresponding pathologic diagnoses were obtained by either biopsy or surgery. The probability of malignancy by descriptor was analyzed using Fisher's exact test and multivariate logistic regression analysis. The probability of malignancy by category was analyzed using Fisher's exact and multi-group comparison tests.

RESULTS

One hundred seventy-eight lesions were malignant. Multivariate model analysis showed that internal enhancement (homogeneous vs others, p < 0.001, heterogeneous and clumped vs clustered ring, p = 0.003) and distribution (focal and linear vs segmental, p < 0.001) were the significant explanatory variables. The descriptors were classified into three grades of suspicion, and the categorization (3, 4A, 4B, 4C, and 5) by sum-up grades showed an incremental increase in the probability of malignancy (p < 0.0001).

CONCLUSION

The three-grade criteria and categorization by sum-up grades of descriptors appear valid for non-mass enhancement.

Magnetic Resonance Imaging-Directed Ultrasound Imaging of Non-Mass Enhancement in the Breast: Outcomes and Frequency of Malignancy

OBJECTIVES

This study was performed to determine the frequency, predictors, and outcomes of ultrasound (US) correlates for non-mass enhancement.

METHODS

From January 2005 to December 2011, a retrospective review of 5837 consecutive breast magnetic resonance imaging examinations at our institution identified 918 non-mass enhancing lesions for which follow-up or biopsy was recommended. Retrospective review of the images identified 879 of 918 lesions (96%) meeting criteria for non-mass enhancement. Patient demographics, pathologic results, and the presence of an adjacent landmark were recorded. Targeted US examinations were recommended for 331 of 879 cases (38%), and 284 of 331 women (86%) underwent US evaluations.

RESULTS

The US correlate rate for non-mass enhancement was 23% (64 of 284). An adjacent landmark was significantly associated with a US correlate (P < .001). Biopsy was recommended for 43 of 64 correlates (67%). Ultrasound-guided biopsy was performed on 39 of 43 (91%); 7 of 39 (18%) were malignant. No correlate was seen for 220 of 284 lesions (77%). At magnetic resonance imaging-guided biopsy, 14 of 117 (12%) were malignancies. For all biopsied non-mass enhancements, the malignancy rate was 18% (55 of 308) and was significantly more prevalent in the setting of a known index cancer (P < .001), older age (P < .001), the presence of a landmark (P = .002), and larger lesion size (P = .019).

CONCLUSIONS

Non-mass enhancement with an adjacent landmark is more likely to have a US correlate compared to non-mass enhancement without an adjacent landmark. Non-mass enhancement in the setting of a known index cancer, older age, a landmark, and larger lesion size is more likely to be malignant. However, no statistical difference was detected in the rate of malignancy between non-mass enhancement with (18%) or without (12%) a correlate. Absence of a correlate does not obviate the need to biopsy suspicious non-mass enhancement.

Descriptors of Malignant Non-mass Enhancement of Breast MRI: Their Correlation to the Presence of Invasion

RATIONALE AND OBJECTIVES

This study aims to investigate the clinical significance of malignant non-mass enhancement (NME) descriptors in breast magnetic resonance images by assessing their correlation to the presence of invasion or lymph node metastasis.

MATERIALS AND METHODS

Three radiologists independently reviewed magnetic resonance images with malignant NMEs between January 2008 and December 2009. Distribution was assessed first, and then each of four internal enhancement patterns-clumped, clustered ring, branching, and hypointense area-was evaluated dichotomously (yes or no). Because clustered rings and hypointense areas were thought to be major structural elements of heterogeneous NMEs, they were also evaluated by integrating them into one collective descriptor we called the "heterogeneous structures." Chi-square test, Fisher exact test, or Student t test was used to analyze differences of variables by each reviewer. Positive predictive values (PPVs) of descriptors in predicting presence of invasion or lymph node metastasis were calculated. P < 0.05 was considered significant.

RESULTS

We included 131 malignant NMEs (76 in situ and 55 invasive) in 129 patients (two bilateral). All three observers' results showed clustered rings (PPVs 54.5%, 54.5%, 50.0%) (P = 0.0005, 0.038, 0.029) and hypointense areas (PPVs 63.6%, 61.5%, 73.9%) (P = 0.004, 0.024, 0.0006) to be significantly associated with invasion. When clustered rings and hypointense areas were integrated into heterogeneous structures, they were significantly associated with invasion (PPVs 54.3%, 53.3%, 51.8%) (P = 0.0003, 0.016, 0.003).

CONCLUSIONS

The NME descriptors clustered rings, hypoechoic areas, and heterogeneous structures, assessed collectively, were associated with invasive breast cancer.

Morphology evaluation of nonmass enhancement on breast MRI: Effect of a three-step interpretation model for readers' performances and biopsy recommendations

OBJECTIVE

To evaluate and compare the use of a newly introduced interpretation model for breast nonmass enhancement (NME, defined as an area of enhancement without a three-dimensional, space-occupying lesion) with the use of the standard interpretation method based on BI-RADS.

MATERIALS AND METHODS

Two expert and two less-experienced breast imaging radiologists performed reading sessions of 86 malignant and 64 benign NME lesions twice. First, radiologists characterized NME using BI-RADS descriptors and assessed the likelihood of malignancy and need for a biopsy. Second, the likelihood of malignancy and need for a biopsy were assessed with the use of the model, in which three-step characterization of morphological features were performed: (1) selection of distribution modifiers, (2) homogeneous vs. heterogeneous internal enhancement (IE) pattern, and (3) evaluation of presence of "clumped", "clustered ring enhancement (CRE)", and "branching" IE signs. Multireader-multicase receiver operating characteristic analysis was used to evaluate observers' performances. Univariate and multivariate logistic regression analyses were performed for morphology descriptors.

RESULTS

With use of the model, average Az of less-experienced radiologists (0.77-0.83; p=0.013) and average sensitivity of all radiologists (96.2-98.2%; p=0.007) improved significantly. NPV also improved but nonsignificantly (81.1-91.9%; p=0.055). Multivariate analyses of the second reading showed branching, clumped, and CRE signs to be significant predictors of malignancy in the results of 3, 2, and 2 readers, respectively.

CONCLUSION

The three-step interpretation model for NME has the potential to improve less-experienced radiologists' performances, making them comparable to expert breast imagers.