Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

Comparison of Performance in Diagnosis and Characterization of Breast Lesions: Contrast-Enhanced Mammography Versus Breast Magnetic Resonance Imaging

INTRODUCTION

In contemporary medical practice, magnetic resonance imaging (MRI) is the most sensitive modality for detecting breast cancer. Contrast-enhanced mammography (CEM), a relatively recent technology, represents another contrast-enhanced imaging technique that has the potential to serve as an alternative to breast MRI. Our main goal is to compare the diagnostic accuracy including assessment of sensitivity and specificity of these 2 contrast-enhanced breast imaging methods, CEM and MRI, in the diagnosis and characterization of breast lesions.

MATERIAL AND METHODS

Our prospective study included patients who were clinically suspected of malignancy and/or had suspicious findings detected by mammography or ultrasound. A total of 116 patients were included, and both CEM and MRI examinations were performed on all patients. All CEM examinations were conducted at our institution, while 56.89% of all MRI examinations were carried out at external centers. While histopathological results were accessible for all malignant lesions, the final diagnosis for 80.5% of benign lesions was established through typical imaging findings and adequate follow-up.

RESULTS

This study encompassed a total of 219 lesions, with 125 out of 219 (57.07%) malignant lesions and 94 out of 219 (42.92%) benign lesions. The sensitivity and specificity values were 98.40% and 81.91%, respectively, for CEM, and 100% and 75.33%, respectively, for MRI. Moreover, CEM showcased comparable performance to MRI in evaluating women with dense breasts.

CONCLUSION

CEM and MRI were compared for breast lesion diagnosis, with MRI showing higher sensitivity and CEM higher specificity; however, the differences were not statistically significant.

Contrast-enhanced Mammography versus MR Imaging of the Breast

Breast MR imaging and contrast-enhanced mammography (CEM) are both techniques that employ intravenously injected contrast agent to assess breast lesions. This approach is associated with a very high sensitivity for malignant lesions that typically exhibit rapid enhancement due to the leakiness of neovasculature. CEM may be readily available at the breast imaging department and can be performed on the spot. Breast MR imaging provides stronger enhancement than the x-ray-based techniques and offers higher sensitivity. From a patient perspective, both modalities have their benefits and downsides; thus, patient preference could also play a role in the selection of the imaging technique.

Breast masses with rim enhancement on contrast-enhanced mammography: morphological and enhancement features for diagnosis and differentiation of benign and malignant

OBJECTIVES

To investigate the imaging characteristics and clinicopathological features of rim enhancement of breast masses demonstrated on contrast-enhanced mammography (CEM).

METHODS

67 cases of breast lesions confirmed by pathology and showing rim enhancement on CEM examinations were analyzed. The lesions were divided into benign and malignant groups, and the morphological and enhanced features were described. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated separately for each morphology descriptor to evaluate the diagnostic ability of each indicator.

RESULTS

There were 35 (52.2%) malignant and 32 (47.8%) benign lesions. There are significant differences in the morphological and enhanced features between benign and malignant lesions. 29/35 (82.9%) malignant lesions exhibited irregular shapes, and 31/35 (88.6%) showed indistinct margins. 28/35 (80%) malignant lesions displayed strong enhancement on CEM, while 12/32 (37.5%) benign lesions exhibited weak enhancement (P = 0.001). Malignant lesions showed a higher incidence of unsmooth inner walls than benign lesions (28/35 vs 7/32; P <.001). Lesion margins showed high sensitivity of 88.57% and NPV of 81.8%. The presence of suspicious calcifications had the highest specificity of 100% and PPV of 100%. The diagnostic sensitivity, specificity, PPV, and NPV of the combined parameters were 97.14%, 93.15%, 94.44%, and 96.77%, respectively.

CONCLUSIONS

The assessment of morphological and enhanced features of breast lesions exhibiting rim enhancement on CEM can improve the differentiation between benign and malignant breast lesions.

ADVANCES IN KNOWLEDGE

This article provides a reference for the differential diagnosis of ring enhanced lesions on CEM.

Evaluation of architectural distortion with contrast-enhanced mammography

Architectural distortion (AD) is the third most common abnormality detected on mammograms. In the absence of an accurate non-invasive tool to evaluate ADs, clinical management often requires surgical excision for histological diagnosis. This problem is expected to worsen with the growing use of digital breast tomosynthesis (DBT) and the resultant increasing detection of ADs. There is therefore a great clinical need for a diagnostic imaging tool to complement non-enhanced mammography for the evaluation of AD. Contrast-enhanced mammography (CEM) is an emerging breast imaging method that uses contrast media and the principle of dual-energy subtraction to evaluate vascularity of suspicious breast lesions. CEM, a cost-effective alternative to breast magnetic resonance imaging (MRI), can be used to evaluate AD by juxtaposing CEM images with non-enhanced mammograms for comparison. In this review, the authors aim to provide readers with an overview of the interpretation of AD on CEM using imaging examples. Relevant imaging features of CEM and their respective significance will be matched with information from a literature review. Finally, the authors would like to highlight the added value of CEM in relevant clinical applications in the assessment of AD.

Contrast-Enhanced Mammography-Guided Biopsy: Preliminary Results of a Single-Center Retrospective Experience

Background: CEM-guided breast biopsy is an advanced diagnostic procedure that takes advantage of the ability of CEM to enhance suspicious breast lesions. The aim pf this paper is to describe a single-center retrospective experience on CEM-guided breast biopsy in terms of procedural features and histological outcomes. Methods: 69 patients underwent the procedure. Patient age, breast density, presentation, dimensions, and lesion target enhancement were recorded. All the biopsy procedures were performed using a 7- or 10-gauge (G) vacuum-assisted biopsy needle. The procedural approach (horizontal or vertical) and the decubitus of the patient (lateral or in a sitting position) were noted. Results: A total of 69 patients underwent a CEM-guided biopsy. Suspicious lesions presented as mass enhancement in 35% of cases and non-mass enhancement in 65% of cases. The median size of the target lesions was 20 mm. The median procedural time for each biopsy was 10 ± 4 min. The patients were placed in a lateral decubitus position in 52% of cases and seated in 48% of cases. The most common approach was horizontal (57%). The mean AGD was 14.8 mGy. At histology, cancer detection rate was 28% (20/71). Conclusions: CEM-guided biopsy was feasible, with high procedure success rates and high tolerance by the patients.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study

OBJECTIVE

To construct and test a nomogram based on intra- and peritumoral radiomics and clinical factors for predicting malignant BiRADS 4 lesions on contrast-enhanced spectral mammography.

METHODS

A total of 884 patients with BiRADS 4 lesions were enrolled from two centers. For each lesion, five ROIs were defined using the intratumoral region (ITR), peritumoral regions (PTRs) of 5 and 10 mm around the tumor, and ITR plus PTRs of 5 mm and 10 mm. Five radiomics signatures were established by LASSO after selecting features. A nomogram was built using selected signatures and clinical factors by multivariable logistic regression analysis. The performance of the nomogram was assessed with the AUC, decision curve analysis, and calibration curves, and also compared with the radiomics model, clinical model, and radiologists.

RESULTS

The nomogram built by three radiomics signatures (constructed from ITR, 5 mm PTR, and ITR + 10 mm PTR) and two clinical factors (age and BiRADS category) showed powerful predictive ability in internal and external test sets with AUCs of 0.907 and 0.904, respectively. The calibration curves, decision curve analysis, showed favorable predictive performance of the nomogram. In addition, radiologists improved the diagnostic performance with the help of nomogram.

CONCLUSION

The nomogram established via intratumoral and peritumoral radiomics features and clinical risk factors had the best performance in distinguishing benign and malignant BiRADS 4 lesions, which could help radiologists improve diagnostic capabilities.

KEY POINTS

• Radiomics features from peritumoral regions in contrast-enhanced spectral mammography images may provide valuable information for the diagnosis of benign and malignant breast imaging reporting and data system category 4 breast lesions. • The nomogram incorporated intra- and peritumoral radiomics features and clinical variables have good application prospects in assisting clinical decision-makers.

Contrast Enhanced Mammography (CEM) Enhancing Asymmetry: Single-Center First Case Analysis

(1) Purpose: The latest Breast Imaging Reporting and Data System (BI-RADS) lexicon for CEM introduced a new descriptor, enhancing asymmetries (EAs). The purpose of this study was to determine which types of lesions were correlated with EAs. (2) Methods: A total of 3359 CEM exams, executed at AOUC Careggi in Florence, Italy between 2019 and 2021 were retrospectively assessed by two radiologists. For each of the EAs found, the size, the enhancing conspicuity (degree of enhancement relative to background described as low, moderate, or high), whether there was a corresponding finding in the traditional radiology images (US or mammography), the biopsy results when performed including any follow-up exams, and the presence of background parenchymal enhancement (BPE) of the normal breast tissue (minimal, mild, moderate, marked) were described. (3) Results: A total of 64 women were included, 36 of them underwent CEM for a preoperative staging assessment, and 28 for a problem-solving examination. Among the 64 EAs, 19/64 (29.69%) resulted in being category B5 (B5) lesions, 5/64 (7.81%) as category B3 (B3) lesions, and 40/64(62.50%) were negative or benign either after biopsy or second-look exams or follow-up. We assessed that EAs with higher enhancing conspicuity correlated significantly with a higher risk of B5 lesions (p: 0.0071), especially bigger ones (p: 0.0274). Conclusions: EAs can relate both with benign and tumoral lesions, and they need to be assessed as the other CEM descriptors, with re-evaluation of low-energy images and second-look exams, particularly larger EAs with higher enhancing conspicuity.

Accuracy and precision of contrast enhanced mammography versus MRI for predicting breast cancer size: how "good" are they really?

OBJECTIVE

To evaluate and compare the accuracy and precision of contrast-enhanced mammography (CEM) vs MRI to predict the size of biopsy-proven invasive breast cancer.

METHODS

Prospective study, 59 women with invasive breast cancer on needle biopsy underwent CEM and breast MRI. Two breast radiologists read each patient's study, with access limited to one modality. CEM lesion size was measured using low-energy and recombined images and on MRI, the first post-contrast series. Extent of abnormality per quadrant was measured for multifocal lesions. Reference standards were size of largest invasive malignant lesion, invasive (PathInvasive) and whole (PathTotal). Pre-defined clinical concordance ±10 mm.

RESULTS

Mean patient age 56 years, 42 (71%) asymptomatic. Lesions were invasive ductal carcinoma 40 (68%) with ductal carcinoma in situ (31/40) in 78%, multifocal in 12 (20%). Median lesion size was 17 mm (invasive) and 27 mm (total), range (5-125 mm). Lin's concordance correlation coefficients for PathTotal 0.75 (95% CI 0.6, 0.84) and 0.71 (95% CI 0.56, 0.82) for MRI and contrast-enhanced spectral mammography (CESM) respectively. Mean difference for total size, 3% underestimated and 4% overestimated, and for invasive 41% and 50% overestimate on MRI and CESM respectively. LOAs for PathTotal varied from 60% under to a 2.4 or almost threefold over estimation. MRI was concordant with PathTotal in 36 (64%) cases compared with 32 (57%) for CESM. Both modalities concordant in 26 (46%) cases respectively.

CONCLUSION

Neither CEM nor MRI have sufficient accuracy to direct changes in planned treatment without needle biopsy confirmation.

ADVANCES IN KNOWLEDGE

Despite small mean differences in lesion size estimates using CEM or MRI, the 95% limits of agreement do not meet clinically acceptable levels.

[Breast Imaging Reporting and Data System (BI-RADS): Advantages and Limitations]

Breast Imaging Reporting and Data System (BI-RADS) is a communication and data tracking system that standardizes and controls the quality of reporting by presenting lexicon descriptors, assessment categories, and recommendations for managing breast lesions. Using standardized terminology recommended by BI-RADS, radiologists can concisely and reproducibly communicate breast imaging results to clinicians. They can also provide the estimated malignant probability of the lesions found and guide management for them by determining the final assessment category. The limitations of BI-RADS 5th edition currently in use are that there are some areas for which standardized terminologies still need to be established, and that the diagnostic criteria of MRI assessment categories 3 and 4 are ambiguous compared to those for mammography or ultrasound. The next revision of BI-RADS is expected to include solutions for overcoming current limitations.

Unilateral primary breast edema: Can T2-weighted images meet the diagnostic challenge?

Background

Unilateral primary breast edema may pose a diagnostic challenge. Excluding malignant etiologies is of utmost importance and may require the use of dynamic MRI examination as a problem solver. Yet, the enhancement pattern of benign and malignant disorders associated with edematous breasts may overlap, and this may add to the dilemma. So, our aim in the current study was to assess the role of T2-weighted MR imaging as a problem-solving sequence in differentiating benign from malignant causes of the edematous breast.

Results

In the current prospective study, 65/96 cases were benign and 31/96 cases were malignant. By the individual analysis of the signal intensity in T2-weighted imaging of MRI examination, there was a significant correlation between low T2 signal intensity lesion and malignant etiology of breast edema with a resultant higher sensitivity of 83.87% and a higher specificity of 98.46% as compared to the contrast-enhanced series, which achieved a sensitivity of 80.65% and a specificity of 20.00%. The combined assessment of T2 WI and the contrast-enhanced series yielded a higher sensitivity of 100% and a specificity of 98.46%.

Conclusions

T2WI is a problem-solving sequence in the evaluation of the primary edematous breast, yielding a significant added value in the diagnostic approach and improving the overall diagnostic performance of dynamic contrast-enhanced MRI.

Breast Lesions Screening of Mammographic Images with 2D Spatial and 1D Convolutional Neural Network-Based Classifier

Mammography is a first-line imaging examination that employs low-dose X-rays to rapidly screen breast tumors, cysts, and calcifications. This study proposes a two-dimensional (2D) spatial and one-dimensional (1D) convolutional neural network (CNN) to early detect possible breast lesions (tumors) to reduce patients’ mortality rates and to develop a classifier for use in mammographic images on regions of interest where breast lesions (tumors) may likely occur. The 2D spatial fractional-order convolutional processes are used to strengthen and sharpen the lesions’ features, denoise, and improve the feature extraction processes. Then, an automatic extraction task is performed using a specific bounding box to sequentially pick out feature patterns from each mammographic image. The multi-round 1D kernel convolutional processes can also strengthen and denoise 1D feature signals and assist in the identification of the differentiation levels of normality and abnormality signals. In the classification layer, a gray relational analysis-based classifier is used to screen the possible lesions, including normal (Nor), benign (B), and malignant (M) classes. The classifier development for clinical applications can reduce classifier’s training time, computational complexity level, computational time, and achieve a more accurate rate for meeting clinical/medical purpose. Mammographic images were selected from the mammographic image analysis society image database for experimental tests on breast lesions screening and K-fold cross-validations were performed. The experimental results showed promising performance in quantifying the classifier’s outcome for medical purpose evaluation in terms of recall (%), precision (%), accuracy (%), and F1 score.

How Dual-Energy Contrast-Enhanced Spectral Mammography Can Provide Useful Clinical Information About Prognostic Factors in Breast Cancer Patients: A Systematic Review of Literature

Introduction

In the past decade, a new technique derived from full-field digital mammography has been developed, named contrast-enhanced spectral mammography (CESM). The aim of this study was to define the association between CESM findings and usual prognostic factors, such as estrogen receptors, progesterone receptors, HER2, and Ki67, in order to offer an updated overview of the state of the art for the early differential diagnosis of breast cancer and following personalized treatments.

Materials and Methods

According to the PRISMA guidelines, two electronic databases (PubMed and Scopus) were investigated, using the following keywords: breast cancer AND (CESM OR contrast enhanced spectral mammography OR contrast enhanced dual energy mammography) AND (receptors OR prognostic factors OR HER2 OR progesterone OR estrogen OR Ki67). The search was concluded in August 2021. No restriction was applied to publication dates.

Results

We obtained 28 articles from the research in PubMed and 114 articles from Scopus. After the removal of six replicas that were counted only once, out of 136 articles, 37 articles were reviews. Eight articles alone have tackled the relation between CESM imaging and ER, PR, HER2, and Ki67. When comparing radiological characterization of the lesions obtained by either CESM or contrast-enhanced MRI, they have a similar association with the proliferation of tumoral cells, as expressed by Ki-67. In CESM-enhanced lesions, the expression was found to be 100% for ER and 77.4% for PR, while moderate or high HER2 positivity was found in lesions with non-mass enhancement and with mass closely associated with a non-mass enhancement component. Conversely, the non-enhancing breast cancer lesions were not associated with any prognostic factor, such as ER, PR, HER2, and Ki67, which may be associated with the probability of showing enhancement. Radiomics on CESM images has the potential for non-invasive characterization of potentially heterogeneous tumors with different hormone receptor status.

Conclusions

CESM enhancement is associated with the proliferation of tumoral cells, as well as to the expression of estrogen and progesterone receptors. As CESM is a relatively young imaging technique, a few related works were found; this may be due to the “off-label” modality. In the next few years, the role of CESM in breast cancer diagnostics will be more thoroughly investigated.

Enhancement Type at Contrast-enhanced Mammography and Association with Malignancy

Background Despite the increasing use of contrast-enhanced mammography (CEM), there are limited data on the evaluation of findings on recombined images and the association with malignancy. Purpose To determine the rates of malignancy of enhancement findings on CEM images in the presence or absence of low-energy findings using the Breast Imaging Reporting and Data System (BI-RADS) lexicon developed for mammography and MRI. Materials and Methods All diagnostic CEM examinations performed at one academic institution between December 2015 and December 2019 had low-energy and recombined images retrospectively. Data were independently reviewed by three breast imaging radiologists with 5-25 years of experience using the BI-RADS mammography and MRI lexicon. Outcome was determined with pathologic analysis or 1-year imaging or clinical follow-up. The χ² and Fisher exact tests were used for analysis. Results A total of 371 diagnostic CEM studies were performed in 371 women (mean age, 54 years ± 11[SD]). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value of enhancement on CEM images was 95% (104 of 109 [95% CI: 90, 98]), 67% (176 of 262 [95% CI: 61, 73]), 55% (104 of 190 [95% CI: 47, 62]), and 97% (176 of 181 [95% CI: 94, 99]), respectively. Among 190 CEM studies with enhancing findings, enhancing lesions were more likely to be malignant when associated with low-energy findings (26% vs 59%, P < .001). Among enhancement types, mass enhancement composed 71% (99 of 140) of all malignancies with PPV of 63% when associated with low-energy findings. Foci, non-mass enhancement, and mass enhancement without low-energy findings had PPV of 6%, 24%, and 38%, respectively. Neither background parenchymal enhancement nor density was associated with enhancement type (P = .19 and P = .28, respectively). Conclusion Mass enhancement on recombined images using CEM was most commonly associated with malignancy, especially when associated with low-energy findings. Enhancement types were more likely to be benign when not associated with low-energy findings; however, they should still be viewed with suspicion, given the high association with malignancy. © RSNA, 2022 Online supplemental material is available for this article.

Comparison of Contrast-Enhanced Spectral Mammography and Contrast-Enhanced MRI in Screening Multifocal and Multicentric Lesions in Breast Cancer Patients

Objectives

We aimed to determine the difference between contrast-enhanced spectral mammography (CESM) and contrast-enhanced magnetic resonance imaging (CE-MRI) in detecting multifocal and multicentric breast cancer (MMBC).

Methods

: This study was conducted among breast cancer patients between July 1, 2017, and May 30, 2021. The sensitivity, specificity, and accuracy of CESM and CE-MRI in the diagnosis of MMBC were evaluated with pathological results as the gold standard.

Results

A total of 188 lesions were detected in 54 patients with MMBC, including 177 breast cancer and 11 benign lesions. Based on CESM and CE-MRI, 4 false-positive cases and 3 false-negative cases and 7 false-positive cases and 1 false-negative case, respectively, were found. The accuracy of CESM was higher than that of MRI (96.3% vs 95.7%), and the specificity was higher than that of MRI (63.6% vs 36.4%). There were no significant differences in the sensitivity, specificity, and accuracy for the detection of MMBC between CESM and CE-MRI (p = 0.500; p = 0.250; p = 0.792).

Conclusion

CESM is an effective method for the detection of MMBC, which is consistent with the sensitivity and accuracy of CE-MRI.

Association between quantitative and qualitative image features of contrast-enhanced mammography and molecular subtypes of breast cancer

Background

The molecular subtype of breast cancer is one of the most important factors affecting patient prognosis. The study aimed to analyze the association between quantitative and qualitative features of contrast-enhanced mammography (CEM) images and breast cancer molecular subtypes.

Methods

This retrospective double-center study included women who underwent CEM between November 2017 and April 2020. Each patient had at least 1 malignant lesion confirmed by pathology. The CEM images were evaluated by 2 radiologists to obtain quantitative and qualitative image features. The molecular subtypes were studied as dichotomous outcomes, including luminal versus non-luminal, human epidermal growth factor receptor (HER2)-enriched versus non-HER2-enriched, and triple-negative breast cancer (TNBC) versus non-TNBC subtypes. The association between the image features and molecular subtypes was analyzed by multivariate logistic regression, with odds ratios (ORs) and 95% confidence intervals (CIs) provided.

Results

A total of 151 patients with 160 malignant lesions were included in the study. For quantitative features, a higher standard deviation of lesion density was associated with non-luminal (OR =0.88, 95% CI: 0.81 to 0.96, P=0.004) and HER2-enriched breast cancers (OR =1.16, 95% CI: 1.04 to 1.28, P=0.006). The relative degree of enhancement (RDE) and contrast-to-noise ratio (CNR) were not associated with molecular subtypes. However, a higher CNR/lesion size (OR =1.06, 95% CI: 1.01 to 1.12, P=0.012) was associated with luminal subtype cancers, and a higher RDE/lesion size (OR =0.94, 95% CI: 0.88 to 1.00, P=0.035) or a higher CNR/lesion size (OR =0.94, 95% CI: 0.88-1.00, P=0.038) was associated with non-TNBCs. For qualitative features, the presence of calcification was associated with HER2-enriched breast cancers (OR =2.91, 95% CI: 1.10 to 7.67, P=0.031). The presence of architectural distortion was associated with luminal cancer (OR =14.50, 95% CI: 1.91 to 110.14, P=0.010) and non-TNBC (OR =0.05, 95% CI: 0.00 to 0.43, P=0.022). Non-mass enhancement (OR =2.78, 95% CI: 1.08 to 7.14, P=0.033) was associated with HER2-enriched breast cancers. An association remained after adjustments for age, breast thickness, and breast density (all adjusted P<0.050).

Conclusions

The quantitative and qualitative imaging features of CEM could contribute to distinguishing breast cancer molecular subtypes.

Does contrast-enhanced mammography have an impact on the detection of cancer in patients with risk of developing breast cancer?

Background

Contrast-enhanced mammography (CEM) has been discovered to be more sensitive and specific than two-dimensional full-field digital mammography (FFDM) in both screening and diagnostic settings. The aim of the study was to assess the additive role of CEM in the detection and characterization of breast lesions in women with increased risk of developing breast cancer. This prospective study included 283 female patients with increased risk of developing breast cancer (i.e., positive family history of breast cancer, personal history of breast cancer, and heterogeneously dense mammary parenchyma) coming for either screening (n = 127/283 (49.1%)) or diagnostic (n = 156/283 (55.1%)) purpose. All patients had FFDM and CEM done, and the findings were evaluated independently; final Breast Imaging Reporting And Data System (BIRADS) classification was given for each modality. Results were then compared with histopathology or ultrasound findings with routine follow-up for normal and typically benign findings.

Results

In this study, 283 women with mean age of 48 were enrolled. Among the studied cases regardless to a specific risk factor, 15/283 (5.3%) were diagnosed as normal, 13/283 (4.6%) as inflammatory lesions, 72/283(25.4%) as benign lesions, 6/283 (2.1%) as benign precancerous lesions, and 177/283 (62.5%) as malignant. The overall sensitivity and specificity of the CEM were 92.7 and 71.43 %, respectively, while FFDM were 80.90 and 59.05%, respectively.

Conclusion

Contrast-enhanced mammography is a valuable screening and diagnostic imaging modality in patients with increased risk of developing breast cancer with diagnostic indices higher than mammography resulting in a significantly higher cancer detection rate.

Added value of contrast-enhanced spectral mammography in symptomatic patients with dense breasts

Background

Breast cancer is the most common malignancy in females around the world representing 25.1% of all cancers.

The high prevalence and need for early treatment of breast malignancy highlight the importance of early and accurate diagnosis. In order to achieve this target, it is necessary to select the most appropriate modality for investigation.

Early detection of breast cancer by conventional mammography tends to reduce mortality; however, it has a low sensitivity and specificity in young females with dense breasts owing to reduced contrast between a possible tumor and the surrounding breast tissue with superimposition of the glandular tissue obscuring underlying lesions.

Our study included 25 patients with dense breasts presented with different breast symptoms, yet the breast lump was the most common complaint. The aim of our study is to evaluate the supplementary value of contrast-enhanced spectral mammography in the assessment of symptomatic patients with dense breasts.

Results

In our study, the enrolled subjects underwent both contrast-enhanced spectral mammography (CESM) and conventional full-field digital mammography (FFDM). CESM was shown to be better than FFDM in terms of sensitivity, positive predictive value, negative predictive value, and accuracy, measuring 100%, 77.8%, 100%, and 84%, compared to 56%, 75%, 46%, and 60%, respectively, yet both modalities showed low specificity, measuring 63.6% and 66.6% for CESM and FFDM, respectively.

The added value of CESM was assessed in terms of ability to detect and correctly characterize the lesions in correlation to histopathological results where CESM could detect 88% of the lesions included in our study and correctly characterized 84% of the lesions; on the other side, FFDM detected only 20% of the lesions and correctly characterized 60% of the lesions. CESM changed the treatment plan to a more extensive surgery +/− neoadjuvant chemotherapy in 57% out of fourteen cases diagnosed with breast cancer emphasizing the role of CESM in assessing the extent of the disease, multicentricity, and multifocality and consequently tailoring the most appropriate treatment plan suitable for each patient.

Conclusion

Contrast-enhanced spectral mammography is superior to full-field digital mammography in patients with dense breasts with a significant supplementary value in detection, characterization of lesions, and tailoring the appropriate treatment plan.

Contrast-enhanced digital mammography and magnetic resonance imaging: reproducibility compared to pathologic anatomy

PURPOSE

To compare the reproducibility between contrast-enhanced digital mammography (CEDM) and magnetic resonance imaging (MRI) with the postsurgical pathologic examination. In addition, the applicability of the Breast Imaging-Reporting and Data System (BI-RADS) lexicon of MRI to CEDM was evaluated for mass lesions.

METHODS

A total of 62 patients with a histologically proven diagnosis of breast cancer were included in this study, for a total of 67 lesions. Fifty-nine patients underwent both methods. The reproducibility between MRI vs CEDM and the reference standard (postoperative pathology) was assessed by considering the lesion and breast size as pivotal variables. Reproducibility was evaluated by computing the concordance correlation coefficient (CCC). Bland-Altman plots were used to depict the observed pattern of agreement as well as to estimate the associated bias. Furthermore, the pattern of agreement between the investigated methods with regard to the breast lesion characterization (i.e. mass/nonmass; shape; margins; internal enhanced characteristics) was assessed by computing the Cohen kappa and its 95% confidence interval (CI).

RESULTS

The reproducibility between MRI and the reference standard and between CEDM and the reference standard showed substantial agreement, with a CCC value of 0.956 (95% CI, 0.931-0.972) and 0.950 (95% CI, 0.920-0.969), respectively. By looking at the Bland-Altman analysis, bias values of 2.344 and 1.875 mm were observed for MRI and CEDM vs reference evaluation, respectively. The agreement between MRI and CEDM is substantial with a CCC value of 0.969 (95% CI, 0.949-0.981). The Bland-Altman analysis showed bias values of -0.469 mm when comparing CEDM vs MRI. Following the Landis and Koch classification criteria, moderate agreement was observed between the two methods in describing BI-RADS descriptors of mass lesions.

CONCLUSION

CEDM is able to measure and describe tumor masses comparably to MRI and can be used for surgical planning.

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Background

Contrast-enhanced mammography (CEM) is a promising breast imaging technique. A limited number of studies have focused on the radiomics analysis of CEM. We intended to explore whether a model constructed with both clinical and radiomics features of CEM can better classify benign and malignant breast lesions.

Methods

This retrospective, double-center study included women who underwent CEM between August 2017 and February 2020. The data from Center 1 were used as training set and the data from Center 2 were used as external testing set (training: testing =2:1). Models were constructed with the clinical, radiomics, and clinical + radiomics features of CEM. The clinical features included patient age and clinical image features interpreted by the radiologists. The radiomics features were extracted from high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images of CEM. The Mann-Whitney U test, Pearson correlation and Boruta's approach were used to select the radiomics features. Random Forest (RF) and logistic regression were used to establish the models. For the testing set, the areas under the curve (AUCs) and 95% confidence intervals (CIs) were employed to evaluate the performance of the models. For the training set, the mean AUCs were obtained by performing internal validation for 100 iterations and then compared by the Kruskal-Wallis and Mann-Whitney U tests.

Results

A total of 226 women (mean age: 47.4±10.1 years) with 226 pathologically proven breast lesions (101 benign; 125 malignant) were included. For the external testing set, the AUCs were 0.964 (95% CI: 0.918-1.000) for the combined model, 0.947 (95% CI: 0.891-0.997) for the radiomics model, and 0.882 (95% CI: 0.803-0.962) for the clinical model. In the internal validation process, the combined model achieved a mean AUC of 0.934±0.030, which was significantly higher than those of the radiomics (mean AUC =0.921±0.031, adjusted P<0.050) and clinical models (mean AUC =0.907±0.036; adjusted P<0.050).

Conclusions

Incorporating both clinical and radiomics features of CEM may achieve better classification results for breast lesions.

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

Current Status of Contrast Enhanced Mammography: A Comprehensive Review

OBJECTIVES

The purpose of this article is to provide a detailed and updated review of the physics, techniques, indications, limitations, reporting, implementation and management of contrast enhanced mammography.

BACKGROUND

Contrast enhanced mammography (CEM), is an emerging iodine-based modified dual energy mammography technique. In addition to having the same advantages as standard full-field digital mammography (FFDM), CEM provides information regarding tumor enhancement, relying on tumor angiogenesis, similar to dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). This article reviews current literature on CEM and highlights considerations that are critical to the successful use of this modality.

CONCLUSION

Multiple studies point to the advantage of using CEM in the diagnostic setting of breast imaging, which approaches that of DCE-MRI.

Primary breast edema on contrast-enhanced digital mammography: a preliminary experience

Background

Primary breast edema can cause marked increase in skin thickness, breast density and echogenicity due to dense breast tissue filled with fluid and so causes subsequent significant attenuation of both the x-ray and ultrasound beams. The study aim is to assess the value of contrast-enhanced digital mammography (CEDM) in assessment and characterization of the obscured underlying breast lesions in cases of primary breast edema.

Results

Fifty five female participants were evaluated, of median age 51 years old and IQR 21. CEDM shows high sensitivity and specificity in the lesion detection as well as local extension delineation in cases associated with primary breast edema. It was accurate in detection of multifocal/multi-centric disease. CEDM is considered as a good negative test in cases of metastatic axillary lymph nodes to exclude and assess any associated obscured breast lesions, as it is good in delineating breast masses obscured by condensed parenchymal tissue. The calculated sensitivity of DM & CEDM was 87.5%, 95.8%, specificity was 55.5%, 72%, the PPV and NPV were 91, 93.6% and 45%, 77.8%, respectively.

Conclusions

CEDM has an important additional diagnostic value in the assessment, characterization and better delineation of breast lesions in primary edematous breast cases.

Can the delayed phase of quantitative contrast-enhanced mammography improve the diagnostic performance on breast masses?

Background

Contrast-enhanced mammography (CEM) is an imaging tool for breast cancer detection. Most quantitative analyses of CEM involve two phases, and it is unknown whether an added delayed phase can improve its diagnostic performance compared to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This study aimed to evaluate whether the delayed phase improves the diagnostic performance of CEM in distinguishing malignant and benign masses.

Methods

This prospective study enrolled 111 women with 111 pathologically confirmed breast masses. CEM was performed after the injection of contrast agent between 2-3 minutes (T1, early phase), 4-5 minutes (T2, second phase), and 7-9 minutes (T3, delayed phase). The quantitative enhanced gray value of lesions (LGV) and the lesion to background grey value ratio (LBR) were measured within each phase's corresponding region of interest (ROI). Based on their changes, the kinetic enhancement pattern was assessed among the three phases, and the diagnostic performance was subsequently measured.

Results

The LGV and LBR of malignant masses were significantly greater than those of benign lesions. The diagnostic performance of LGV and LBR at the delayed phase was consistent with that of the second phase but poorer than that of the early phase. The sensitivity of LGV_T1 + LGV_T2 + LGV_T3 was less than that of LGV_T1 + LGV_T2 (86.5% vs. 95.1%) with a similar area under the curve (AUC), specificity, positive-predictive value (PPV), negative-predictive value (NPV), and accuracy. The sensitivity of LBR_T1 + LBR_T2 + LBR_T3 increased by 19.6%, and specificity decreased by 20.7% compared with LBR_T1 + LBR_T2. The LGV_T1 + LGV_T2 + LGV_T3 + kinetic enhancement (T1-T3) had the lowest sensitivity (67.0%), but the highest specificity (75.8%), and the sensitivity of LBR_T1 + LBR_T2 + LBR_T3 + kinetic enhancement (T1-T3) was higher than that of LBR_T1 + LBR_T2 + kinetic enhancement (T1-T2) (90.2% vs. 63.4%, respectively).

Conclusions

The addition of a delayed CEM phase for breast cancer diagnosis yielded limited performance improvement. The quantitative analysis combined with enhancement patterns between the two consecutive phases has great potential to distinguish between malignant and benign lesions.

Training Radiologists to Interpret Contrast-enhanced Mammography: Toward a Standardized Lexicon

OBJECTIVE

Using terms adapted from the BI-RADS Mammography and MRI lexicons, we trained radiologists to interpret contrast-enhanced mammography (CEM) and assessed reliability of their description and assessment.

METHODS

A 60-minute presentation on CEM and terminology was reviewed independently by 21 breast imaging radiologist observers. For 21 CEM exams with 31 marked findings, observers recorded background parenchymal enhancement (BPE) (minimal, mild, moderate, marked), lesion type (oval/round or irregular mass, or non-mass enhancement), intensity of enhancement (none, weak, medium, strong), enhancement quality (none, homogeneous, heterogeneous, rim), and BI-RADS assessment category (2, 3, 4A, 4B, 4C, 5). "Expert" consensus of 3 other radiologists experienced in CEM was developed. Kappa statistic was used to assess agreement between radiologists and expert consensus, and between radiologists themselves, on imaging feature categories and final assessments. Reproducibility of specific feature descriptors was assessed as fraction of consensus-concordant responses.

RESULTS

Radiologists demonstrated moderate agreement for BPE, (mean kappa, 0.43; range, 0.05-0.69), and lowest reproducibility for "minimal." Agreement was substantial for lesion type (mean kappa, 0.70; range, 0.47-0.93), moderate for intensity of enhancement (mean kappa, 0.57; range, 0.44-0.76), and moderate for enhancement quality (mean kappa, 0.59; range, 0.20-0.78). Agreement on final assessment was fair (mean kappa, 0.26; range, 0.09-0.44), with BI-RADS category 3 the least reproducible. Decision to biopsy (BI-RADS 2-3 vs 4-5) showed moderate agreement with consensus (mean kappa, 0.54; range, -0.06-0.87).

CONCLUSION

With minimal training, agreement for description of CEM findings by breast imaging radiologists was comparable to other BI-RADS lexicons.

Current Status and Future of BI-RADS in Multimodality Imaging, From the AJR Special Series on Radiology Reporting and Data Systems

BI-RADS is a communication and data tracking system that has evolved since its inception as a brief mammography lexicon and reporting guide into a robust structured reporting platform and comprehensive quality assurance tool for mammography, ultrasound, and MRI. Consistent and appropriate use of the BI-RADS lexicon terminology and assessment categories effectively communicates findings, estimates the risk of malignancy, and provides management recommendations to patients and referring clinicians. The impact of BI-RADS currently extends internationally through six language translations. A condensed version has been proposed to facilitate a phased implementation of BI-RADS in resource-constrained regions. The primary advance of the 5th edition of BI-RADS is harmonization of the lexicon terms across mammography, ultrasound, and MRI. Harmonization has also been achieved across these modalities for the reporting structure, assessment categories, management recommendations, and data tracking system. Areas for improvement relate to certain common findings that lack lexicon descriptors and a need for further clarification of proper use of category 3. BI-RADS is anticipated to continue to evolve for application to a range of emerging breast imaging modalities.

Diagnostic accuracy of contrast-enhanced digital mammography in comparison with sonomammography for characterization of focal asymmetries

Background

Mammographic focal asymmetry represents normal breast tissue, benign, or malignant lesions. Accurate characterization is important for better management. The study evaluates diagnostic accuracy of contrast-enhanced digital mammography (CEDM) for characterization of focal asymmetries seen in 2D mammography.

Results

The study was done prospectively on 38 females among 360 patients who underwent baseline sonomammographic assessment for diagnostic and screening purposes. Complementary ultrasound was performed only when a finding was detected in cases of screening mammograms. Focal asymmetries were evaluated according to Breast Imaging Reporting and Data System (BI-RADS) lexicon 2013. CEDM was performed and followed by ultrasound (US) guided core biopsy for solid lesions or aspiration for cystic lesions. CEDM processing resulted in recombined image showing enhancing abnormality. Low energy image and recombined image findings were analyzed blindly and classified into focus enhancement, mass enhancement, non-mass enhancement, and non-enhanced lesions. CEDM and sonomammography findings were compared regarding pathological probability and multiplicity. Histopathology was the reference standard.

Mass enhancement showed strong correlation with malignant pathology. Non-mass enhancement showed no correlation with particular pathology. All non-enhanced focal asymmetries were benign in pathology or normal tissue. Rim enhancement needed second look ultrasound evaluation. CEDM was superior to sonomammography with higher sensitivity (77.8%, 65.7% respectively), NPV (0.8, 0.6), accuracy (0.6, 0.2) but lower specificity (81.8% vs. 100%). Multiplicity detection by CEDM was 26.3% and by sonomammography was 10.5%.

Conclusion

CEDM is more accurate than sonomammography in determination of normal tissue, benign, or malignant lesions in cases of mammographic focal asymmetry. CEDM is more accurate in detection of multiplicity. Undesired biopsies were avoidable with proper management of suspicious and malignant lesions.

Contrast-Enhanced Mammography Implementation, Performance, and Use for Supplemental Breast Cancer Screening

Contrast-enhanced mammography (CEM) is an emerging breast imaging technology that provides recombined contrast-enhanced images of the breast in addition to low-energy images analogous to a 2-dimensional full-field digital mammogram. Because most breast imaging centers do not use CEM at this time, a detailed overview of CEM implementation and performance is presented. Thereafter, the potential use of CEM for supplemental screening is discussed in detail, given the importance of this topic for the future of the CEM community. Diagnostic performance, safety, and cost considerations of CEM for dense breast tissue supplemental screening are discussed.

Contrast-enhanced mammography: past, present, and future

Contrast-enhanced mammography (CEM) combines conventional mammography with iodinated contrast material to improve cancer detection. CEM has comparable performance to breast MRI without the added cost or time of conventional MRI protocols. Thus, this technique may be useful for indications previously reserved for MRI, such as problem-solving, determining disease extent in patients with newly diagnosed cancer, monitoring response to neoadjuvant therapy, evaluating the posttreatment breast for residual or recurrent disease, and potentially screening in women at intermediate- or high-risk for breast cancer. This article will provide a comprehensive overview on the past, present, and future of CEM, including its evolving role in the diagnostic and screening settings.

Technique, protocols and adverse reactions for contrast-enhanced spectral mammography (CESM): a systematic review

We reviewed technical parameters, acquisition protocols and adverse reactions (ARs) for contrast-enhanced spectral mammography (CESM). A systematic search in databases, including MEDLINE/EMBASE, was performed to extract publication year, country of origin, study design; patients; mammography unit/vendor, radiation dose, low-/high-energy tube voltage; contrast molecule, concentration and dose; injection modality, ARs and acquisition delay; order of views; examination time. Of 120 retrieved articles, 84 were included from 22 countries (September 2003-January 2019), totalling 14012 patients. Design was prospective in 44/84 studies (52%); in 70/84 articles (83%), a General Electric unit with factory-set kVp was used. Per-view average glandular dose, reported in 12/84 studies (14%), ranged 0.43-2.65 mGy. Contrast type/concentration was reported in 79/84 studies (94%), with Iohexol 350 mgI/mL mostly used (25/79, 32%), dose and flow rate in 72/84 (86%), with 1.5 mL/kg dose at 3 mL/s in 62/72 studies (86%). Injection was described in 69/84 articles (82%), automated in 59/69 (85%), manual in 10/69 (15%) and flush in 35/84 (42%), with 10-30 mL dose in 19/35 (54%). An examination time < 10 min was reported in 65/84 studies (77%), 120 s acquisition delay in 65/84 (77%) and order of views in 42/84 (50%) studies, beginning with the craniocaudal view of the non-suspected breast in 7/42 (17%). Thirty ARs were reported by 14/84 (17%) studies (26 mild, 3 moderate, 1 severe non-fatal) with a pooled rate of 0.82% (fixed-effect model). Only half of CESM studies were prospective; factory-set kVp, contrast 1.5 mL/kg at 3 mL/s and 120 s acquisition delay were mostly used; only 1 severe AR was reported. CESM protocol standardisation is advisable.

Diagnostic performance of contrast-enhanced dual-energy spectral mammography (CESM): a retrospective study involving 644 breast lesions

OBJECTIVE

To evaluate the diagnostic performance of contrast-enhanced dual-energy spectral mammography (CESM) in comparison with that of full-field digital mammography (FFDM), either alone or accompanied with breast ultrasound (BUS) in a large series of patients/breast lesions (n = 644).

PATIENTS AND METHODS

In this retrospective study, five radiologists evaluated the lesions by three imaging modalities: FFDM, FFDM + BUS, and CESM and compared the imaging to the gold standard (histopathology or clinical follow-up). Diagnostic performance parameters and receiver operating characteristic (ROC) curves of CESM were calculated and compared to those of FFDM or FFDM + BUS (McNemar's test). Additionally, the reliability of tumor size measurement by CESM was compared with the histopathological measurement.

RESULTS

The study included 218 benign and 426 malignant lesions. 85% of benign and 93% of malignant lesions were adequately identified using CESM. With respect to FFDM and FFDM + BUS, CESM significantly increased sensitivity to 93.2% (+ 10.7% and + 3.4%, respectively); specificity to 84.4% (+ 15.8% and + 1.7%, respectively); PPV to 92.3% (+ 26.8% and + 3.6%, respectively); NPV to 86.0% (+ 1.6% and + 1.8%, respectively); and accuracy to 90.2% (+ 15.8% and + 3.2%, respectively). In the ROC curves analyses, the comparison among the three AUC values was also statistically significant (p < 0.001). Good agreement between tumor diameters measured using CESM and histopathology was observed (Spearman's rank correlation, r = 0.891, p < 0.0001), although this technique tended to produce an overestimation of the size (+ 7 mm).

CONCLUSIONS

CESM has high diagnostic accuracy and can be considered as a useful technique for the assessment of breast lesions.

Diagnostic Value of Contrast-Enhanced Spectral Mammography for Screening Breast Cancer: Systematic Review and Meta-analysis

BACKGROUND

Contrast-enhanced spectral mammography (CESM) is a new image examination technology that has developed over the past few years. As CESM technology keeps improving, a current meta-analysis review is needed to systematically evaluate the potential diagnostic value of CESM.

METHODS

A total of 18 studies were included in the review. Sensitivity, specificity, and other important parameters of CESM accuracy for breast cancer diagnosis were pooled and analyzed using random-effects models. Summary receiver operating characteristic curves were calculated for overall accuracy estimation.

RESULTS

The summary estimates for CESM in the diagnosis of breast cancer were as follows: the pooled sensitivity and specificity were 0.89 (95% confidence interval [CI], 0.88-0.91) and 0.84 (95% CI, 0.82-0.85), respectively. Positive likelihood ratio was 3.73 (95% CI, 2.68-5.20), negative likelihood ratio was 0.10 (95% CI, 0.06-0.15), and diagnostic odds ratio was 71.36 (95% CI, 36.28-140.39). The area under the curve was 0.96 (standard error = 0.011).

CONCLUSION

CESM has a high diagnostic accuracy for evaluating breast cancer and can be considered as a useful test for initial assessment of breast lesions.