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.

The verification of the utility of a commercially available phantom combination for quality control in contrast-enhanced mammography

Contrast-enhanced mammography is being increasingly implemented clinically, providing much improved contrast between tumour and background structures, particularly in dense breasts. Although CEM is similar to conventional mammography it differs via an additional exposure with high energy X-rays (≥ 40 kVp) and subsequent image subtraction. Because of its special operational aspects, the CEM aspect of a CEM unit needs to be uniquely characterised and evaluated. This study aims to verify the utility of a commercially available phantom set (BR3D model 020 and CESM model 022 phantoms (CIRS, Norfolk, Virginia, USA)) in performing key CEM performance tests (linearity of system response with iodine concentration and background subtraction) on two models of CEM units in a clinical setting. The tests were successfully performed, yielding results similar to previously published studies. Further, similarities and differences in the two systems from different vendors were highlighted, knowledge of which may potentially facilitate optimisation of the systems.

The role of contrast-enhanced mammography in the preoperative evaluation of invasive lobular carcinoma of the breast

AIM

To assess the performance of contrast-enhanced mammography (CEM) in the preoperative staging of invasive lobular carcinoma (ILC) of the breast.

MATERIALS AND METHODS

The present study was a multicentre, multivendor, multinational retrospective study of women with a histological diagnosis of ILC who had undergone CEM from December 2013 to December 2021. Index lesion size and multifocality were recorded for two-dimensional (2D) mammography, CEM, and when available magnetic resonance imaging (MRI). Comparison with histological data was undertaken for women treated by primary surgical excision. Pearson correlation coefficients and Bland-Altman's analysis of agreement were used to assess differences with a significance level of 0.05.

RESULTS

One hundred and fifteen ILC lesions were included, 46 (40%) presented symptomatically and 69 were screening detected. CEM demonstrated superior sensitivity when compared to standard mammography. The correlation between the histological size measured on the surgical excision specimen size was greater than with standard mammography (r=0.626 and 0.295 respectively, p=0.001), with 19% of lobular carcinomas not visible without a contrast agent. The sensitivity of CEM for multifocal disease was greater than standard mammography (70% and 20% respectively, p<0.0001). CEM overestimated tumour size by an average of 1.5 times, with the size difference increasing for larger tumour. When MRI was performed (n=22), tumour size was also overestimated by an average of 1.3 times. The degree of size overestimation was similar for both techniques, with the tumour size on CEM being on average 0.5 cm larger than MRI.

CONCLUSION

CEM is a useful tool for the local staging of lobular carcinomas and could be an alternative to breast MRI.

ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study

PURPOSE

The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism.

MATERIALS AND METHODS

This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction.

RESULTS

The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization.

CONCLUSIONS

The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.

Role of contrast-enhanced mammography in the preoperative detection of ductal carcinoma in situ of the breasts: a comparison with low-energy image and magnetic resonance imaging

OBJECTIVES

To compare contrast-enhanced mammography (CEM) with low-energy image (LEI) alone and with magnetic resonance imaging (MRI) in the preoperative diagnosis of ductal carcinoma in situ (DCIS).

METHODS

In this single-center retrospective study, we reviewed 98 pure DCIS lesions in 96 patients who underwent CEM and MRI within 2 weeks preoperatively. The diagnostic performances of each imaging modality, lesion morphology, and extent were evaluated.

RESULTS

The sensitivity of CEM to DCIS was similar to that of MRI (92.9% vs. 93.9%, p = 0.77) and was significantly higher than that of LEI alone (76.5%, p = 0.002). The sensitivity of CEM to calcified DCIS (92.4%) was not significantly different from LEI alone (92.4%) and from MRI (93.9%, p = 1.00). However, CEM contributed to the simultaneous comparison of calcifications with enhancements. CEM had considerably higher sensitivity compared with LEI alone (93.8% vs. 43.8%, p < 0.001) and performed similarly to MRI (93.8%, p = 1.00) for noncalcified DCIS. All DCIS lesions were enhanced in MRI, whereas 94.9% (93/98) were enhanced in CEM. Non-mass enhancement was the most common presentation (CEM 63.4% and MRI 66.3%). The difference between the lesion size on each imaging modality and the histopathological size was smallest in MRI, followed by CEM, and largest in LEI.

CONCLUSION

CEM was more sensitive than LEI alone and comparable to MRI in DCIS diagnosis. The enhanced morphology of DCIS in CEM was consistent with that in MRI. CEM was superior to LEI alone in size measurement of DCIS.

CLINICAL RELEVANCE STATEMENT

This study investigated the value of CEM in the diagnosis and evaluation of DCIS, aiming to offer a reference for the selection of examination methods for DCIS and contribute to the early diagnosis and precise treatment of DCIS.

KEY POINTS

• DCIS is an important indication for breast surgery. Early and accurate diagnosis is crucial for DCIS treatment and prognosis. • CEM overcomes the deficiency of mammography in noncalcified DCIS diagnosis, exhibiting similar sensitivity to MRI; and CEM contributes to the comparison of calcification and enhancement of calcified DCIS, thereby outperforming MRI. • CEM is superior to LEI alone and slightly inferior to MRI in the size evaluation of DCIS.

Locoregional staging of breast cancer: contrast-enhanced mammography versus breast magnetic resonance imaging

PURPOSE

Breast cancer diagnosis often involves assessing the locoregional spread of the disease through MRI, as multicentricity, multifocality and/or bilaterality are increasingly common. Contrast-enhanced mammography (CEM) is emerging as a potential alternative method. This study compares the performance of CEM and MRI in preoperative staging of women with confirmed breast carcinoma. Patients were also asked to fill in a satisfaction questionnaire to rate their comfort level with each investigation.

METHODS

From May 1st, 2021 to May 1st, 2022, we enrolled 70 women with confirmed breast carcinoma who were candidates for surgery. For pre-operative locoregional staging, all patients underwent CEM and MRI examination, which two radiologists evaluated blindly. We further investigated all suspicious locations for disease spread, identified by both CEM and MRI, with a second-look ultrasound (US) and eventual histological examination.

RESULTS

In our study cohort, MRI and CEM identified 114 and 102 areas of focal contrast enhancement, respectively. A true discrepancy between MRI and CEM occurred in 9 out of 70 patients examined. MRI reported 8 additional lesions that proved to be false positives on second-look US in 6 patients, while it identified 4 lesions that were not detected by CEM and were pathological (true positives) in 3 patients.

CONCLUSIONS

CEM showed results comparable to MRI in the staging of breast cancer in our study population, with a high rate of patient acceptability.

Breast cancer diagnosis from contrast-enhanced mammography using multi-feature fusion neural network

OBJECTIVES

To develop an end-to-end deep neural network for the classification of contrast-enhanced mammography (CEM) images to facilitate breast cancer diagnosis in the clinic.

METHODS

In this retrospective mono-centric study, patients who underwent CEM examinations from January 2019 to August 2021 were enrolled. A multi-feature fusion network combining low-energy (LE) and dual-energy subtracted (DES) images and dual view, as well as bilateral information, was trained and tested using a large CEM dataset with a diversity of breast tumors for breast lesion classification. Its generalization performance was further evaluated on two external datasets. Results were reported using AUC, accuracy, sensitivity, and specificity.

RESULTS

A total of 2496 patients (mean age, 53 years ± 12 (standard deviation)) were included and divided into a training set (1718), a validation set (255), and a testing set (523). The proposed CEM-based multi-feature fusion network achieved the best diagnosis performance with an AUC of 0.96 (95% confidence interval (CI): 0.95, 0.97), compared with the no-fusion model, the left-right fusion model, and the multi-feature fusion network with only LE image inputs. Our models reached an AUC of 0.90 (95% CI: 0.85, 0.94) on a full-field digital mammograph (FFDM) external dataset (86 patients), and an AUC of 0.92 (95% CI: 0.89, 0.95) on a CEM external dataset (193 patients).

CONCLUSION

The developed multi-feature fusion neural network achieved high performance in CEM image classification and was able to facilitate CEM-based breast cancer diagnosis.

CLINICAL RELEVANCE STATEMENT

Compared with low-energy images, CEM images have greater sensitivity and similar specificity in malignant breast lesion detection. The multi-feature fusion neural network is a promising computer-aided diagnostic tool for the clinical diagnosis of breast cancer.

KEY POINTS

• Deep convolutional neural networks have the potential to facilitate contrast-enhanced mammography-based breast cancer diagnosis. • The multi-feature fusion neural network reaches high accuracies in the classification of contrast-enhanced mammography images. • The developed model is a promising diagnostic tool to facilitate clinical breast cancer diagnosis.

The Role of Contrast-Enhanced Mammography After Cryoablation of Breast Cancer

Image-guided cryoablation is an emerging therapeutic technique for the treatment of breast cancer and is a treatment strategy that is an effective alternate to surgery in select patients. Tumor features impacting the efficacy of cryoablation include size, location in relation to skin, and histology (e.g., extent of intraductal component), underscoring the importance of imaging for staging and workup in this patient population. Contrast-enhanced mammography (CEM) utilization is increasing in both the screening and diagnostic settings and may be useful for follow-up imaging after breast cancer cryoablation, given its high sensitivity for cancer detection and its advantages in terms of PPV, time, cost, eligibility, and accessibility compared with contrast-enhanced MRI. This Clinical Perspective describes the novel use of CEM after breast cancer cryoablation, highlighting the advantages and disadvantages of CEM compared with alternate imaging modalities, expected benign postablation CEM findings, and CEM findings suggestive of residual or recurrent tumor.

Performance of contrast-enhanced mammography for detecting multifocal and multicentric breast cancer and evaluating tumour size, and implications for surgical management: Early experience in a tertiary centre

INTRODUCTION

To compare diagnostic accuracy of contrast-enhanced mammography (CEM) with standard 2D digital mammography (equivalent to low-energy image; LEM) for detection of multifocal and multicentric breast cancer and evaluation of tumour size and disease extent for preoperative planning.

METHODS

Biopsy proven breast cancer patients who underwent CEM preoperatively between January 2021 and January 2023 were included in this study. CEM and LEM images were independently reviewed by at least two blinded readers. Lesion location, number, size (maximal diameter) and extension across the midline and/or nipple invasion were recorded. Tumour number and size estimated on imaging were compared with final operative histology, which served as the gold standard.

RESULTS

Forty-nine patients (48 females and 1 male) and 50 cases (one patient had bilateral breast lesions) were included in the analysis. Median patient age was 60 (IQR 51, 69). CEM had significantly higher lesion detection rate compared with LEM, with sensitivities of 78% for LEM and 92% for CEM for the index tumour and 15% for LEM and 100% for CEM for multicentric and multifocal cancer. We found no statistically significant difference in median tumour size measurements on CEM and final surgical specimen (P value = 0.97); however, a significant difference was identified in the tumour size measured on LEM and surgical specimen (P value < 0.001).

CONCLUSION

CEM is superior to standard 2D digital mammography for detection of multifocal and multicentric breast cancer and is a reliable and more accurate method for estimating tumour size.

[Use of contrast-enhanced mammography for diagnosis of breast cancer]

BACKGROUND

Contrast-enhanced mammography (CEM) is an imaging method that is able to improve visualization of intramammary tumors after peripheral venous administration of an iodine-containing contrast medium (ICM).

OBJECTIVES AND METHODS

The current significance of CEM is discussed.

RESULTS

Studies were able to show an advantage of CEM in the diagnosis of breast cancer compared to mammography, especially for women with dense breasts. Indications for CEM currently depend on the availability of magnetic resonance imaging (MRI). If MRI is available, CEM is indicated in those cases when MRI cannot be performed. Use of CEM for breast cancer screening is currently viewed critically. This view can change when results and updated assessments of large CEM studies in Europe and USA become available. Patients must be informed about the use of an ICM. As ICM administration for CEM is carried out in a similar manner to established imaging methods, the authors expect the use of ICM for CEM to be unproblematic as long as general contraindications are adhered to.

CONCLUSIONS

In the future, CEM could have greater importance for the diagnosis of breast cancer, as this imaging method has diagnostic advantages compared to conventional mammography. A great advantage of CEM is its availability. For those who use breast MRI, CEM is helpful when MRI is not feasible due to contraindications or other reasons.

Contrast-enhanced mammography versus breast MRI in the assessment of multifocal and multicentric breast cancer: a retrospective study

BACKGROUND

Breast cancer multifocality and multicentricity diagnosis influences the surgeon's choice between applying breast conservative therapy or performing mastectomy.

PURPOSE

To assess the role of contrast enhanced mammography (CEM) and breast magnetic resonance imaging (MRI) in the assessment of preoperative breast cancer multifocality and multicentricity and to assess their accuracy, agreement and impact on the surgical management.

MATERIAL AND METHODS

The study retrospectively included cases over a 5-year period. After analysis and interpretation of suspicious breast lesions, a comparative evaluation of CEM and MRI was conducted with the assessment of diagnostic indices, including sensitivity, specificity and diagnostic accuracy. The kappa (κ) measure of agreement between both modalities was measured. The postoperative specimen pathology was the reference standard.

RESULTS

One hundred and twenty-two female cases with 126 breast lesions were evaluated. Specimen pathology, MRI and CEM showed a single neoplastic lesion in 67.5%, 35% and 48.5% of cases, respectively, and multiple neoplastic lesions in 32.5%, 65% and 51.6% of cases, respectively. The sensitivity, specificity and accuracy of MRI were 95.12%, 49.41%,and 64.29%, and the CEM values were 85.37%, 64.71% and 71.43%, respectively. The κ value was 0.592 with an intermediate agreement between both modalities. When comparing between both modalities, enhancing foci showed a statistically significant difference, although there were no statistically significant difference in terms of high breast density or molecular subtype.

CONCLUSION

In terms of breast cancer multifocality and multicentricity evaluation, MRI showed a higher sensitivity, while CEM showed a higher specificity, and there was moderate agreement between the two modalities.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Head-to-head comparison of contrast-enhanced mammography and contrast-enhanced MRI for assessing pathological complete response to neoadjuvant therapy in patients with breast cancer: a meta-analysis

OBJECTIVE

Breast cancer patients receiving neoadjuvant therapy (NAT) are in need of a more patient-friendly imaging modality such as contrast-enhanced mammography (CEM) for monitoring therapy response. The purpose of this study was to conduct a meta-analysis to compare the diagnostic performances of CEM and contrast-enhanced magnetic resonance imaging (CE-MRI) for assessing pathological complete response (pCR) in these patients.

METHODS

The PubMed, Embase, and Cochrane Library databases were searched through March 2023 to identify studies reporting a head-to-head comparison of CEM and CE-MRI in detecting pCR in breast cancer patients receiving NAT. Pooled diagnostic performance was calculated using a bivariate random-effects model, and an AUC was derived for each test from hierarchic summary ROC analysis.

RESULTS

Six studies with 328 patients were included. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were 93% (95% CI 84-97%), 68% (95% CI 60-76%), and 29.29 (95% CI 11.41-75.18) for CEM versus 84% (95% CI 62-95%), 80% (95% CI 71-87%), and 21.39 (95% CI 5.94-77.13) for CE-MRI. The AUC was 0.85 (95% CI 0.82-0.88) for CEM and 0.85 (95% CI 0.82-0.88) for CE-MRI.

CONCLUSION

This meta-analysis of head-to-head comparison studies showed that CEM provides an equivalent diagnostic accuracy to CE-MRI in identification of pCR in breast cancer patients receiving NAT. The results support the increasing use of CEM in this setting and would encourage future studies to validate CEM as a suitable replacement for MRI.

Interpreting contrast imaging to plan breast surgery

BACKGROUND

Contrast enhanced mammography (CEM) and magnetic resonance imaging (MRI) are more accurate than conventional imaging (CI) for breast cancer staging. How adding CEM and MRI to CI might change the surgical plan is understudied.

METHODS

Surgical plans (breast conserving surgery (BCS), wider BCS, BCS with diagnostic excision (>1BCS), mastectomy) were devised by mock-MDT (radiologist, surgeon and pathology reports) according to disease extent on CI, CI + CEM and CI + MRI. Differences in the mock-MDT's surgical plans following the addition of CEM or MRI were investigated. Using pre-defined criteria, the appropriateness of the modified plans was assessed by comparing estimated disease extent on imaging with final pathology. Surgery performed was recorded from patient records.

RESULTS

Contrast imaging modified mock-MDT plans for 20 of 61(32.8%) breasts. The addition of CEM changed the plan in 16/20 (80%) and MRI in 17/20 breasts (85%). Identical changes were proposed by both CEM and MRI in 13/20 (65%) breasts. The modified surgical plan based on CI + CEM was possibly appropriate for 6/16 (37.5%), and CI + MRI in 9/17, (52.9%) breasts. The surgery performed was concordant with the mock-MDT plan for all 10 patients where the plans could be compared (BCS 1, >1 BCS 2 and mastectomy 7).

CONCLUSION

Adding CEM or MRI to CI changed mock-MDT plans in up to one third of women, but not all were appropriate. Changing surgical plans following addition of contrast imaging to CI without biopsy confirmation could lead to over or under-treatment.

Vaccines in Breast Cancer: Challenges and Breakthroughs

Breast cancer is a problem for women's health globally. Early detection techniques come in a variety of forms ranging from local to systemic and from non-invasive to invasive. The treatment of cancer has always been challenging despite the availability of a wide range of therapeutics. This is either due to the variable behaviour and heterogeneity of the proliferating cells and/or the individual's response towards the treatment applied. However, advancements in cancer biology and scientific technology have changed the course of the cancer treatment approach. This current review briefly encompasses the diagnostics, the latest and most recent breakthrough strategies and challenges, and the limitations in fighting breast cancer, emphasising the development of breast cancer vaccines. It also includes the filed/granted patents referring to the same aspects.

Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study

Background

Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow.

Methods

A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists' reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444).

Findings

The FAPS-based segmentation task achieved DSCs of 0.888 ± 0.101, 0.820 ± 0.148 and 0.837 ± 0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916-0.978), 0.940 (95% [CI]: 0.894-0.987) and 0.891 (95% [CI]: 0.816-0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6 s vs 3 min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies.

Interpretation

The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability.

Funding

This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).

Contrast-enhanced mammography in the assessment of residual disease after neoadjuvant treatment

PURPOSE

To investigate the utility of contrast-enhanced mammography (CEM) as an alternative to breast MRI for the evaluation of residual disease after neoadjuvant treatment (NAT).

METHODS

This prospective study enrolled consecutive women undergoing NAT for breast cancer from July 2017-July 2019. Breast MRI and CEM exams performed after completion of NAT were read independently by two breast radiologists. Residual disease and lesion size on MRI and CEM recombined (RI) and low-energy images (LEI) were compared. Histopathology was considered the reference standard. Statistical analysis was performed using McNemar's and Leisenring's tests. Multiple comparison adjustment was made using Bonferroni procedure. Lesion sizes were correlated using Kendall's tau coefficient.

RESULTS

There were 110 participants with 115 breast cancers. Residual disease (invasive cancer or ductal carcinoma in situ) was detected in 83/115 (72%) lesions on pathology, 71/115 (62%) on MRI, 55/115 (48%) on CEM RI, and 75/115 (65%) on CEM LEI. When using multiple comparison adjustment, no significant differences were detected between MRI combined with CEM LEI and CEM RI combined with CEM LEI, in terms of accuracy (MRI: 77%, CEM: 72%; p ≥ 0.99), sensitivity (MRI: 88%, CEM: 81%; p ≥ 0.99), specificity (MRI: 47%, CEM: 50%; p ≥ 0.99), PPV (MRI: 81%, CEM: 81%; p ≥ 0.99), or NPV (MRI: 60%, CEM: 50%; p ≥ 0.99). Size correlation between pathology and both MRI combined with CEM LEI and CEM RI combined with CEM LEI was moderate: τ = 0. 36 vs 0.33 (p ≥ 0.99).

CONCLUSION

Contrast-enhanced mammography is an acceptable alternative to breast MRI for the detection of residual disease after neoadjuvant treatment.

Intratumoral and peritumoral radiomics for preoperatively predicting the axillary non-sentinel lymph node metastasis in breast cancer on the basis of contrast-enhanced mammography: a multicenter study

OBJECTIVE

To develop and test a contrast-enhanced mammography (CEM)-based radiomics model using intratumoral and peritumoral regions to predict non-sentinel lymph node (NSLN) metastasis in breast cancer before surgery.

METHODS

This multicenter study included 365 breast cancer patients with sentinel lymph node metastasis. Intratumoral regions of interest (ROIs) were manually delineated, and peritumoral ROIs (5 and 10 mm) were automatically obtained. Five models, including intratumoral model, peritumoral (5 and 10 mm) models, and intratumoral+peritumoral (5 and 10 mm) models, were constructed by support vector machine classifier on the basis of optimal features selected by variance threshold, SelectKbest, and least absolute shrinkage and selection operator algorithms. The predictive performance of radiomics models was evaluated by receiver operating characteristic curves. An external testing set was used to test the model. The Memorial Sloan Kettering Cancer Center (MSKCC) model was used to compare the predictive performance with radiomics model.

RESULTS

The intratumoral ROI and intratumoral+peritumoral 10-mm ROI-based radiomics model achieved the best performance with an area under the curve (AUC) of 0.8000 (95% confidence interval [CI]: 0.6871-0.8266) in the internal testing set. In the external testing set, the AUC of radiomics model was 0.7567 (95% CI: 0.6717-0.8678), higher than that of MSKCC model (AUC = 0.6681, 95% CI: 0.5148-0.8213) (p = 0.361).

CONCLUSIONS

The intratumoral and peritumoral radiomics based on CEM had an acceptable predictive performance in predicting NSLN metastasis in breast cancer, which could be seen as a supplementary predicting tool to help clinicians make appropriate surgical plans.

ADVANCES IN KNOWLEDGE

The intratumoral and peritumoral CEM-based radiomics model could noninvasively predict NSLN metastasis in breast cancer patients before surgery.

Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study

BACKGROUND

This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM.

METHODS

Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, accuracy, the positive predictive value (PPV), the negative predictive value (NPV), the F1 score, the precision recall curve (PRC), and heat maps. The final models were compared with the diagnostic performance of conventional CNNs, radiomics models, and two radiologists with specialised breast imaging experience.

RESULTS

The best-performing deep learning model, that is, the CBAM-based Xception, achieved an area under the ROC curve (AUC) of 0.970, a sensitivity of 0.848, a specificity of 1.000, and an accuracy of 0.891 on the external test set, which was higher than those of other CNNs, radiomics models, and radiologists. The PRC and the heat maps also indicated the favourable predictive performance of the attention-based CNN model. The diagnostic performance of two radiologists improved with deep learning assistance.

CONCLUSIONS

Using an attention-based deep learning model based on CESM images can help to distinguishing benign from malignant breast lesions, and the diagnostic performance of radiologists improved with deep learning assistance.

Contrast-enhanced mammography (CEM) versus MRI for breast cancer staging: detection of additional malignant lesions not seen on conventional imaging

BACKGROUND

Contrast-enhanced mammography (CEM) is more available than MRI for breast cancer staging but may not be as sensitive in assessing disease extent. We compared CEM and MRI in this setting.

METHODS

Fifty-nine women with invasive breast cancer underwent preoperative CEM and MRI. Independent pairs of radiologists read CEM studies (after reviewing a 9-case set prior to study commencement) and MRI studies (with between 5 and 25 years of experience in breast imaging). Additional lesions were assigned National Breast Cancer Centre (NBCC) scores. Positive lesions (graded NBCC ≥ 3) likely to influence surgical management underwent ultrasound and/or needle biopsy. True-positive lesions were positive on imaging and pathology (invasive or in situ). False-positive lesions were positive on imaging but negative on pathology (high-risk or benign) or follow-up. False-negative lesions were negative on imaging (NBCC < 3 or not identified) but positive on pathology.

RESULTS

The 59 women had 68 biopsy-proven malignant lesions detected on mammography/ultrasound, of which MRI demonstrated 66 (97%) and CEM 67 (99%) (p = 1.000). Forty-one additional lesions were detected in 29 patients: six of 41 (15%) on CEM only, 23/41 (56%) on MRI only, 12/41 (29%) on both; CEM detected 1/6 and MRI 6/6 malignant additional lesions (p = 0.063), with a positive predictive value (PPV) of 1/13 (8%) and 6/26 (23%) (p = 0.276).

CONCLUSIONS

While MRI and CEM were both highly sensitive for lesions detected at mammography/ultrasound, CEM may not be as sensitive as MRI in detecting additional otherwise occult foci of malignancy.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trials Registry: ACTRN 12613000684729.

Quantitative Analysis of Contrast-enhanced Mammography for Risk Stratification of Benign Versus Malignant Disease and Molecular Subtype

OBJECTIVE

To assess quantitative enhancement of benign, high-risk, and malignant lesions and differences in molecular subtype and grade of malignant lesions on contrast-enhanced mammography (CEM).

METHODS

This IRB-approved retrospective study included women who underwent CEM for diagnostic work-up of a breast lesion between 2014 and 2020. Inclusion criteria were women who had diagnostic work-up with CEM and had BI-RADS 1 or 2 with one year follow-up, BI-RADS 3 with tissue diagnosis or stability for 2 years, or BI-RADS 4 or 5 with tissue diagnosis. An enhancement ratio was calculated for all lesions. This was obtained by drawing a region of interest within the lesion and a second region of interest in the nonenhancing background tissue using a program developed with MATLAB. Descriptive statistics were evaluated using chi-squared tests, Fisher exact tests, and analysis of variance. A logistic regression model was used to predict cancer outcome using the enhancement ratio. Statistical significance was defined as P < 0.05.

RESULTS

There were 332 lesions in 210 women that met study criteria. Of the 332 lesions, 50.9% (169/332) were malignant, 5.7% (19/332) were high-risk, and 43.4% (144/332) were benign. Enhancement intensity of malignant lesions was higher than benign lesions. Odds ratio for quantitative enhancement of malignant lesions was 30.15 (P < 0.0001). Enhancement ratio above 1.49 had an 84.0% sensitivity and 84.0% specificity for malignancy. HER2-enriched breast cancers had significantly higher mean enhancement ratios (P = 0.0062).

CONCLUSION

Quantitative enhancement on CEM demonstrated that malignant breast lesions had higher mean enhancement intensity than benign lesions.

Contrast Media-Enhanced Breast Computed Tomography With a Photon-Counting Detector: Initial Experiences on In Vivo Image Quality and Correlation to Histology

OBJECTIVES

The aim of this study was to investigate the feasibility, the image quality, and the correlation with histology of dedicated spiral breast computed tomography (B-CT) equipped with a photon-counting detector in patients with suspicious breast lesions after application of iodinated contrast media.

MATERIALS AND METHODS

The local ethics committee approved this prospective study. Twelve women with suspicious breast lesions found in mammography or B-CT underwent contrast-enhanced spiral B-CT and supplementary ultrasound. For all lesions, biopsy-proven diagnosis and histological workup after surgical resection were obtained including the size of cancer/ductal carcinoma in situ, which were correlated to sizes measured in B-CT. Signal-to-noise ratio and contrast-to-noise ratio were evaluated for tumor, glandular tissue, and fatty tissue.

RESULTS

Of the 12 patients, 15 suspicious lesions were found, 14 were malignant, and 1 benign lesion corresponded to a chronic inflammation. All lesions showed strong contrast media uptake with a signal-to-noise ratio of 119.7 ± 52.5 with a contrast-to-noise ratio between glandular tissue and breast cancer lesion of 12.6 ± 5.9. The correlation of the size of invasive tumors measured in B-CT compared with histological size was significant and strong R = 0.77 ( P < 0.05), whereas the correlation with the size of the peritumoral ductal carcinoma in situ was not significant R = 0.80 ( P = 0.11).

CONCLUSIONS

Contrast-enhanced B-CT shows high contrast between breast cancer and surrounding glandular tissue; therefore, it is a promising technique for cancer detection and staging depicting both soft tissue lesions and microcalcifications, which might be a substantial advantage over breast MRI.

A Score to Predict the Malignancy of a Breast Lesion Based on Different Contrast Enhancement Patterns in Contrast-Enhanced Spectral Mammography

Background: To create a predictive score of malignancy of a breast lesion based on the main contrast enhancement features ascertained by contrast-enhanced spectral mammography (CESM). Methods: In this single-centre prospective study, patients with suspicious breast lesions (BIRADS > 3) were enrolled between January 2013 and February 2022. All participants underwent CESM prior to breast biopsy, and eventually surgery. A radiologist with 20 years’ experience in breast imaging evaluated the presence or absence of enhancement and the following enhancement descriptors: intensity, pattern, margin, and ground glass. A score of 0 or 1 was given for each descriptor, depending on whether the enhancement characteristic was predictive of benignity or malignancy (both in situ and invasive). Then, an overall enhancement score ranging from 0 to 4 was obtained. The histological results were considered the gold standard in the evaluation of the relationship between enhancement patterns and malignancy. Results: A total of 321 women (median age: 51 years; range: 22−83) with 377 suspicious breast lesions were evaluated. Two hundred forty-nine lesions (66%) have malignant histological results (217 invasive and 32 in situ). Considering an overall enhancement score ≥ 2 as predictive of malignancy, we obtain an overall sensitivity of 92.4%; specificity of 89.8%; positive predictive value of 94.7%; and negative predictive value of 85.8%. Conclusions: Our proposed predictive score on the enhancement descriptors of CESM to predict the malignancy of a breast lesion shows excellent results and can help in early breast cancer diagnosis and in avoiding unnecessary biopsies.

Contrast-Enhanced Mammography versus Breast Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

BACKGROUND

Contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CE-MRI) are commonly used in the screening of breast cancer. The present systematic review aimed to summarize, critically analyse, and meta-analyse the available evidence regarding the role of CE-MRI and CEM in the early detection, diagnosis, and preoperative assessment of breast cancer.

METHODS

The search was performed on PubMed, Google Scholar, and Web of Science on 28 July 2021 using the following terms "breast cancer", "preoperative staging", "contrast-enhanced mammography", "contrast-enhanced spectral mammography", "contrast enhanced digital mammography", "contrast-enhanced breast magnetic resonance imaging" "CEM", "CESM", "CEDM", and "CE-MRI". We selected only those papers comparing the clinical efficacy of CEM and CE-MRI. The study quality was assessed using the QUADAS-2 criteria. The pooled sensitivities and specificity of CEM and CE-MRI were computed using a random-effects model directly from the STATA "metaprop" command. The between-study statistical heterogeneity was tested (I2-statistics).

RESULTS

Nineteen studies were selected for this systematic review. Fifteen studies (1315 patients) were included in the metanalysis. Both CEM and CE-MRI detect breast lesions with a high sensitivity, without a significant difference in performance (97% and 96%, respectively).

CONCLUSIONS

Our findings confirm the potential of CEM as a supplemental screening imaging modality, even for intermediate-risk women, including females with dense breasts and a history of breast cancer.

Impact of contrast-enhanced mammography in surgical management of breast cancers for women with dense breasts: a dual-center, multi-disciplinary study in Asia

OBJECTIVE

To evaluate the impact of pre-operative contrast-enhanced mammography (CEM) in breast cancer patients with dense breasts.

METHODS

We conducted a retrospective review of 232 histologically proven breast cancers in 200 women (mean age: 53.4 years ± 10.2) who underwent pre-surgical CEM imaging across two Asian institutions (Singapore and Taiwan). Majority (95.5%) of patients had dense breast tissue (BI-RADS category C or D). Surgical decision was recorded in a simulated blinded multi-disciplinary team setting on two separate scenarios: (i) pre-CEM setting with standard imaging, and clinical and histopathological results; and (ii) post-CEM setting with new imaging and corresponding histological findings from CEM. Alterations in surgical plan (if any) because of CEM imaging were recorded. Predictors CEM of patients who benefitted from surgical plan alterations were evaluated using logistic regression.

RESULTS

CEM resulted in altered surgical plans in 36 (18%) of 200 patients in this study. CEM discovered clinically significant larger tumor size or extent in 24 (12%) patients and additional tumors in 12 (6%) patients. CEM also detected additional benign/false-positive lesions in 13 (6.5%) of the 200 patients. Significant predictors of patients who benefitted from surgical alterations found on multivariate analysis were pre-CEM surgical decision for upfront breast conservation (OR, 7.7; 95% CI, 1.9-32.1; p = 0.005), architectural distortion on mammograms (OR, 7.6; 95% CI, 1.3-42.9; p = .022), and tumor size of ≥ 1.5 cm (OR, 1.5; 95% CI, 1.0-2.2; p = .034).

CONCLUSION

CEM is an effective imaging technique for pre-surgical planning for Asian breast cancer patients with dense breasts.

KEY POINTS

• CEM significantly altered surgical plans in 18% (nearly 1 in 5) of this Asian study cohort with dense breasts. • Significant patient and imaging predictors for surgical plan alteration include (i) patients considered for upfront breast-conserving surgery; (ii) architectural distortion lesions; and (iii) tumor size of ≥ 1.5 cm. • Additional false-positive/benign lesions detected through CEM were uncommon, affecting only 6.5% of the study cohort.

Preoperative evaluation of breast cancer: Contrast-enhanced mammography versus contrast-enhanced magnetic resonance imaging: A systematic review and meta-analysis

INTRODUCTION

Breast cancer is the most common cancer in women worldwide. It is responsible for about 23% of cancer in females in both developed and developing countries. This study aimed to compare the diagnostic performance of contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CEMRI) in preoperative evaluations of breast lesions.

METHODS

We searched for published literature in the English language in MEDLINE via PubMed and EMBASETM via Ovid, The Cochrane Library, and Trip database. For literature published in other languages, we searched national databases (Magiran and SID), KoreaMed, and LILACS. Metadisc1.4 software was used for statistical analysisRESULTS:A total of 1225 patients were included. The pooled sensitivity of CEM and CEMRI was 0.946 (95% CI, 0.931-0.958) and 0.935 (95% CI, 0.920-0.949), respectively. The pooled specificity of CEM and CEMRI was 0.783 (95% CI, 0.758-0.807) and 0.715 (95% CI, 0.688-0.741), respectively. The sensitivity of CEM was the most in the United States (97%) and the specificity of CEM was the most in Brazil (88%). MRI sensitivity was the most in USA and Egypt (99%) and China had the most MRI specificity (81%) in diagnosis of breast lesions.

CONCLUSION

Contrast-enhanced mammography, a combination of high energy image and low energy image, can well display breast lesions and has the diagnostic efficacy equivalent to MRI. Importantly, CEM imaging shows higher specificity, positive predictive value, and diagnostic conformance rate than MRI. Despite some drawbacks such as higher irradiation and iodine usage, CEM has such advantages as convenient and fast examination, strong applicability, and low costs; thus, it can be popularized as a useful tool in breast disease.

Impact of preoperative staging with contrast-enhanced mammography for localized breast cancer management

OBJECTIVE

A precise evaluation of the disease extent is mandatory before surgery for early breast cancer (EBC). Contrast-enhanced mammography (CEDM) is a recent technique that may help define adequate surgery.

METHODS

This retrospective study included consecutive patients referred to a cancer center between November 2016 and July 2017 for biopsy-confirmed invasive EBC management. The primary objective was to evaluate the rate of surgical changes after incorporating the results of the preoperative staging examination, including CEDM.

RESULTS

A total of 231 patients were screened for inclusion, and 132 patients were included, corresponding to 134 lesions. The first surgical plan was modified for 33 patients (25%), which represented 34 lesions. For 8 patients (6%), the surgery was cancelled in preference for neoadjuvant chemotherapy; for 16 patients (12.1%), the primary tumor procedure was enlarged; and for 23 patients (17.4%) the lymph node management was modified. Surgery was changed only due to the CEDM results for 24 patients (18.5%) and consisted of a more invasive procedure due to a more extended, multifocal or multicentric lesion than seen on the standard imaging. Anatomopathological surgery piece findings were well correlated with contrast-enhanced mammography results. Overall, there was no increase in the delay between the planned date of surgery and the effective surgical procedure (median 0 days).

CONCLUSION

CEDM added to preoperative staging helped define better surgical management without increasing delay in the surgical procedure.

ADVANCES IN KNOWLEDGE

CEDM is a reliable technique that should be considered as part of preoperative staging for EBC.

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.

Can Contrast-Enhanced Spectral Mammography (CESM) Reduce Benign Breast Biopsy?

Objectives

To evaluate the potential of contrast-enhanced spectral mammography (CESM) in reducing benign breast biopsy rate, thereby improving resource utilization. To explore its potential as a value-adding modality in the management of BI-RADS 4/5 lesions.

Materials and Methods

This was a prospective study conducted between July 2016 and September 2018. Patients with BI-RADS 4/5 lesions detected on conventional imaging (mammogram, digital breast tomosynthesis, and ultrasound) were enrolled for adjunct CESM. Histopathologic correlation was done for all lesions. Additional suspicious lesions detected on CESM were all identified on second-look ultrasound and subsequently biopsied. Images were evaluated independently by two radiologists trained in breast imaging using BI-RADS classification. Presence of enhancement on CESM, BI-RADS score, and histopathology of each lesion were analyzed and tested with the chi-square/fisher-exact test for statistical significance.

Results

The study included 105 lesions in 63 participants—1 man and 62 women, an average age of 53.7 ± 10.8 years. On CESM, 22 (20.9%) of the lesions did not show enhancement. All 22 lesions had been classified as BI-RADS 4A and were subsequently proven to be benign. Of the remaining 83 enhancing lesions, 54 (65.1%) were malignant and 29 (34.9%) were benign (p < 0.05). CESM detected 6 additional lesions which were not identified on initial conventional imaging. Four of these were proven malignant and were in a different quadrant than the primary lesion investigated.

Conclusion

There is evidence that the absence of enhancement in CESM strongly favors benignity. It may provide the reporting radiologist with greater confidence in imaging assessment, especially in BI-RADS 4A cases, where a proportion of them are in actuality BI-RADS 3. Greater accuracy of BI-RADS grading can reduce nearly half of benign biopsies and allow better resource allocation. CESM also increases the detection rate of potentially malignant lesions, thereby changing the treatment strategies.

Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours

Objective

To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM).

Methods

This retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour.

Results

In total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower.

Conclusion

Deep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status.

Key Points

• A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography.

• The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors.

• Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08538-4.

Single Center Evaluation of Comparative Breast Radiation dose of Contrast Enhanced Digital Mammography (CEDM), Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT)

RATIONALE AND OBJECTIVES

The aim of this retrospective study is to compare the radiation dose received during CEDM, short and long protocol (CEDM SP and CEDM LP), with dose received during DM and DBT on patients with varying breast thickness, age and density.

MATERIALS AND METHODS

Between January 2019 and December 2019, patients having 6214 DM, 3662 DBT and 173 CEDM examinations in our department were analyzed. Protocol total single breast AGD has been evaluated for all clinical imaging protocols, extracting AGD values and exposure data from the dose DICOM Structured Report (SR) information stored in the hospital PACS system. Protocol AGD was calculated as the sum of single projection AGDs carried out in every exam for each clinical protocol. A total amount of 23,383 exams for each breast were analyzed. Protocol AGDs, stratified as a function of patient breast compression thickness, age, and breast density were assessed.

RESULTS

The total protocol AGD median values for each protocol are: 2.8 mGy for DM, 3.2 mGy for DBT, 6.0 mGy for DM+DBT, 4.5 mGy for CEDM SP, 7.4 mGy for CEDM SP_DBT (CEDM SP protocol with DBT), 8.4 mGy for CEDM LP and 11.6 mGy for CEDM LP_DBT (CEDM LP protocol with DBT). CEDM SP AGD median value is 59% higher than DM AGD median value and 40% lesser than DM+DBT AGD median; this last difference was statistically confirmed with a p-value <0.001. AGD value for each standard breast CEDM SP projection results to be below 3-mGy limit. AGD value for each standard breast CEDM SP projection results to be below 3 mGy, as required by international legislation. For dense breasts, the AGD median value is 4.2 mGy, with the first and third quartile of 3.3 mGy and 6.0 mGy respectively; for non-dense breasts, the AGD median value is 4.7 mGy, with first and third quartile of 3.5 mGy and 6.3 mGy respectively. The difference between the two groups was statistically tested and confirmed, with a p-value of 0.039.

CONCLUSION

CEDM SP results in higher radiation exposure compared with conventional DM and DBT but lower than the Combo mode. The dose administered during the CEDM SP is lower in patients with dense breasts regardless of their size. An interesting outcome, considering the ongoing studies on CEDM screening in patients with dense breasts.

Contrast-enhanced Mammography: A Systematic Review and Meta-Analysis of Diagnostic Performance

Background Contrast-enhanced mammography (CEM) is a promising technique for breast cancer detection, but conflicting results have been reported in previous meta-analyses. Purpose To perform a systematic review and meta-analysis of CEM diagnostic performance considering different interpretation methods and clinical settings. Materials and Methods The MEDLINE, EMBASE, Web of Science, and Cochrane Library databases were systematically searched up to July 15, 2021. Prospective and retrospective studies evaluating CEM diagnostic performance with histopathology and/or follow-up as the reference standard were included. Study quality was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Summary diagnostic odds ratio and area under the receiver operating characteristic curve were estimated with the hierarchical summary receiver operating characteristic (HSROC) model. Summary estimates of sensitivity and specificity were obtained with the hierarchical bivariate model, pooling studies with the same image interpretation approach or focused on the same findings. Heterogeneity was investigated through meta-regression and subgroup analysis. Results Sixty studies (67 study parts, 11 049 CEM examinations in 10 605 patients) were included. The overall area under the HSROC curve was 0.94 (95% CI: 0.91, 0.96). Pooled diagnostic odds ratio was 55.7 (95% CI: 42.7, 72.7) with high heterogeneity (τ² = 0.3). At meta-regression, CEM interpretation with both low-energy and recombined images had higher sensitivity (95% vs 94%, P < .001) and specificity (81% vs 71%, P = .03) compared with recombined images alone. At subgroup analysis, CEM showed a 95% pooled sensitivity (95% CI: 92, 97) and a 78% pooled specificity (95% CI: 66, 87) from nine studies in patients with dense breasts, while in 10 studies on mammography-detected suspicious findings, CEM had a 92% pooled sensitivity (95% CI: 89, 94) and an 84% pooled specificity (95% CI: 73, 91). Conclusion Contrast-enhanced mammography demonstrated high performance in breast cancer detection, especially with joint interpretation of low-energy and recombined images. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Bahl in this issue.

Association between lesion enhancement and breast cancer in contrast-enhanced spectral mammography

BACKGROUND

Contrast-enhanced spectral mammography (CESM) may help to determine the malignancy potential of lesions according to the degree of enhancement.

PURPOSE

To investigate the correlation between the degree of contrast enhancement of the lesions in contrast-enhanced spectral mammography (CESM) and the final histopathological diagnosis in patients with BI-RADS 4 and 5 lesions.

MATERIAL AND METHODS

CESM was performed in 128 patients who had BI-RADS 4 and 5 lesions on mammography and underwent histopathological examination. A total of 128 index lesions were scored using a 4-point scale regarding the degree of contrast enhancement (0 = no contrast enhancement, 1 = minimal, 2 = moderate, 3 = marked), a score of 2 and 3 was accepted as suggestive of malignancy. The study was approved in our institutional scientific committee.

RESULTS

In total, 76 (59.4%) of the lesions had benign histopathological results, whereas 52 of them had malignant results. Contrast enhancement was not observed in 22.7% of the lesions while 24.2% had minimal enhancement, 18.8% had moderate enhancement, and 34.4% had marked enhancement in CESM. The sensitivity of the degree of contrast enhancement in CESM was 98.1%, when the specificity was 77.6%, positive predictive value was 75%, negative predictive value was 98.3%, and accuracy was 85.9%.

CONCLUSION

This study demonstrated that the degree of contrast enhancement of the lesions in CESM may be used in daily practice with easily performing a visual scale in predicting the malignancy potential of the lesions.

Contrast-Enhanced Mammography: Technique, Indications, and Review of Current Literature

Purpose of Review

To provide an update on contrast-enhanced mammography (CEM) regarding current technique and interpretation, the performance of this modality versus conventional breast imaging modalities (mammography, ultrasound, and MRI), existing clinical applications, potential challenges, and pitfalls.

Recent Findings

Multiple studies have shown that the low-energy, non-contrast-enhanced images obtained when performing CEM are non-inferior to full-field digital mammography with the added benefit of recombined post-contrast images, which have been shown to provide comparable information compared to MRI without sacrificing sensitivity and negative predictive values. While CEMs' usefulness for further diagnostic characterization of indeterminate breast findings is apparent, additional studies have provided strong evidence of potential roles in screening intermediate to high-risk populations, evaluation of disease extent, and monitoring response to therapy, particularly in patients in whom MRI is either unavailable or contraindicated. Others have shown that some patients prefer CEM over MRI given the ease of performance and patient comfort. Additionally, some health systems may find significantly reduced costs compared to MRI. Currently, CEM is hindered by the limited availability of CEM-guided tissue sampling and issues of intravenous contrast administration. However, commercially available CEM-guided biopsy systems are on the horizon, and small changes in practice workflow can be quickly adopted. As of now, MRI remains a mainstay of high-risk screening, evaluation of the extent of disease, and monitoring response to therapy, but smaller studies have suggested that CEM may be equivalent to MRI for these indications, and larger confirmatory studies are needed.

Summary

CEM is an emerging problem-solving breast imaging modality that provides complementary information to conventional imaging modalities and may potentially be used in place of MRI for specific indications and/or patient populations.

Prediction of Underestimation Using Contrast-Enhanced Spectral Mammography in Patients Diagnosed as Ductal Carcinoma In Situ on Preoperative Core Biopsy

BACKGROUND

To assess the performance of contrast-enhanced spectral mammography (CESM) for the prediction of DCIS underestimation in comparison with mammography, breast US, and breast MRI.

PATIENTS AND METHODS

We prospectively enrolled patients diagnosed with DCIS on preoperative core biopsy. Visibility, lesion type, and extent on each imaging modality, CESM gray values (CGV) were evaluated. Pathologic features of core biopsy and surgery were recorded. Chi-square or Fisher's exact test were used for univariate analysis. Multivariate logistic regression analysis was used to find independent predictors for DCIS underestimation and receiver operating characteristic (ROC) curve analysis was performed.

RESULTS

A total of 113 lesions in 108 patients were analyzed (50 pure DCIS; 63 underestimated DCIS). Visibility on mammography, breast US, CESM, and breast MRI were 44%, 76%, 58%, and 80% for pure DCIS, and 73%, 81%, 86%, and 92% for underestimated DCIS. Tumor extents on surgical pathology of pure and underestimated DCIS were 1.11 ± 1.35 cm and 2.61 ± 2.09 cm. On multivariate analysis, nuclear grade and suspected invasion on core biopsy, visibility on mammography, and extent on breast MRI were independent factors for the model 1, whereas nuclear grade on core biopsy, extent on CESM, and mean CGV on MLO-recombined image were independent factors for the model 2. Area under ROC curve (AUC) was 0.843 for model 1 including breast MRI, whereas AUC was 0.823 for model 2 including CESM, which didn't show a significant difference (P = .968).

CONCLUSION

For detecting underestimated DCIS, CESM was superior to mammography and breast US, and comparable to breast MRI.

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.

Multifocality and Multicentrality in Breast Cancer: Comparison of the Efficiency of Mammography, Contrast-Enhanced Spectral Mammography, and Magnetic Resonance Imaging in a Group of Patients with Primarily Operable Breast Cancer

BACKGROUND

The multifocality and multicentrality of breast cancer (MFMCC) are the significant aspects that determine a specialist's choice between applying breast-conserving therapy (BCT) or performing a mastectomy. This study aimed to assess the usefulness of mammography (MG), contrast-enhanced spectral mammography (CESM), and magnetic resonance imaging (MRI) in women diagnosed with breast cancer before qualifying for surgical intervention to visualize other (additional) cancer foci.

METHODS

The study included 60 breast cancer cases out of 630 patients initially who underwent surgery due to breast cancer from January 2015 to April 2019. MG, CESM, and MRI were compared with each other in terms of the presence of MFMCC and assessed for compliance with the postoperative histopathological examination (HP).

RESULTS

Histopathological examination confirmed the presence of MFMCC in 33/60 (55%) patients. The sensitivity of MG in detecting MFMCC was 50%, and its specificity was 95.83%. For CESM, the sensitivity was 85.29%, and the specificity was 96.15%. For MRI, all the above-mentioned parameters were higher as follows: sensitivity-91.18%; specificity-92.31%.

CONCLUSIONS

In patients with MFMCC, both CESM and MRI are highly sensitive in the detection of additional cancer foci. Both CESM and MRI change the extent of surgical intervention in every fourth patient.

Opportunities in cancer imaging: risk-adapted breast imaging in screening

In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.

Accuracy of contrast-enhanced spectral mammography compared with MRI for invasive breast cancers: Prospective study in population of predominantly underrepresented minorities

OBJECTIVES

This prospective study compares contrast-enhanced spectral mammography (CESM) with contrast-enhanced breast MRI in assessing the extent of newly diagnosed breast cancer in a multiethnic cohort.

METHODS

This study includes 41 patients with invasive breast cancer detected by mammography or conventional ultrasound imaging from May 2017 to March 2020. CESM and MRI scans were performed prior to any treatment. Results are compared with each other and to histopathology. Detection of the malignant lesion was assessed by sensitivity, specificity, PPV, NPV. Consistency of malignant tumor size measurement was compared between modalities using Intraclass Correlation Coefficient (ICC).

RESULTS

In a multiethnic cohort with over 65% Hispanic and African-American women, the sensitivity of detecting malignant lesions for CESM is 93.1% (77.23%, 99.15%) and MRI is 96.55% (82.24%, 99.91%). The PPV for CESM 96.43% (81.65%, 99.91%) is better compared to MRI 82.35% (65.47%, 93.24%). CESM is as effective as MRI in evaluating index cancers and multifocal/multicentric/contralateral disease. CESM has greater specificity and PPV since MRI tends to overcall benign lesions. There is a good agreement of tumor size between CESM to surgery and MRI to surgery with ICC of 0.85 (95% CI 0.69, 0.93) and 0.87 (95% CI 0.74, 0.94), respectively. There is good agreement of malignancy detection between CESM and MRI with Kappa of 0.74 (95% CI 0.52, 0.95).

CONCLUSIONS

CESM is an effective imaging modality for evaluating the extent of disease in newly diagnosed invasive breast cancers and a good alternative to MRI in a multiethnic population.

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.

Diagnostic Performance of Contrast-enhanced Mammography: Comparison With MRI and Mammography

OBJECTIVE

To compare the diagnostic performance of contrast-enhanced mammography (CEM) with MRI and mammography (MG) based on histopathological results.

METHODS

In this IRB-approved study, written informed consent was obtained from all patients. Images from 40 patients (62 lesions) with suspicious findings on US between March 2018 and August 2018 were evaluated. Sensitivity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of CEM, MRI, and MG were evaluated and compared within a 95% confidence interval. Maximum dimensions of lesions were measured and correlations of results were evaluated with Spearman's Rho test.

RESULTS

In the histopathological analysis, 66% (41/62) of lesions were malignant and 34% (21/62) of lesions were benign. Contrast-enhanced mammography, MRI, and MG had sensitivities of 100% (41/41), 100% (41/41), and 80% (33/41), respectively. The sensitivity of CEM and MRI was significantly better than that of MG (P = 0.03). The NPVs of CEM (100%, 7/7) and MRI (100%, 14/14) were statistically higher than the NPV of MG (60%, 12/20) (P = 0.03). The false-positive rates for CEM, MRI, and MG were 33% (7/21), 66% (14/21), and 42% (9/21), respectively. Contrast-enhanced mammography had a significantly lower false-positive rate than MRI (P < 0.001). Mammography had the highest false-negative rate, missing 19% (8/41) of malignant lesions.

CONCLUSION

Contrast-enhanced mammography has similar performance characteristics to MRI and improved performance characteristics relative to MG. In particular, CEM and MRI have similar sensitivity and NPVs and both are superior in each of these metrics to MG.

Contrast-Enhanced Mammography for Newly Diagnosed Breast Cancer in Women With Breast Augmentation: Preliminary Findings

In 17 women with newly diagnosed breast cancer who underwent contrast-enhanced mammography (CEM) and MRI, both modalities were found to be concordant for the index cancer. In six of the 17 women, CEM showed an additional lesion that was confirmed by MRI. Of these six additional lesions, three were multifocal, one was multicentric, and two were contralateral; two of the six were malignant. MRI did not identify any additional cancers that were not identified on CEM. CEM may have a role in women with breast augmentation and either a contraindication or limited access to MRI.

Multireader comparison of contrast-enhanced mammography versus the combination of digital mammography and digital breast tomosynthesis in the preoperative assessment of breast cancer

PURPOSE

To compare preoperative contrast-enhanced spectral mammography (CEM) versus digital mammography plus digital breast tomosynthesis (DM + DBT) in detecting breast cancer (BC) and assessing its size.

MATERIAL AND METHODS

We retrospectively included 78 patients with histological diagnosis of BC who underwent preoperative DM, DBT, and CEM over one year. Four readers, blinded to pathology and clinical information, independently evaluated DM + DBT versus CEM to detect BC and measure its size. Readers' experience ranged 3-10 years. We calculated the per-lesion cancer detection rate (CDR) and the complement of positive predictive value (1-PPV) of both methods, stratifying analysis on the total of lesions, index lesions, and additional lesions. The agreement in assessing cancer size versus pathology was assessed with Bland-Altman analysis.

RESULTS

100 invasive BCs (78 index lesions and 22 additional lesions) were analyzed. Compared to DM + DBT, CEM showed higher overall CDR in less experienced readers (range 0.85-0.90 vs. 0.95-0.96), and higher CDR for additional lesions, regardless of the reader (range 0.54-0.68 vs. 0.77-0.86). CEM increased the detection of additional disease in dense breasts in all readers and non-dense breasts in less experienced readers only. The 1-PPV of CEM (range 0.10-0.18) was comparable to that of DM + DBT (range 0.09-0.19). At Bland-Altman analysis, DM + DBT and CEM showed comparable mean differences and limits of agreement in respect of pathologic cancer size.

CONCLUSION

Preoperative CEM improved the detection of additional cancer lesions compared to DM + DBT, particularly in dense breasts. CEM and DM + DBT achieved comparable performance in cancer size assessment.

The Usefulness of Spectral Mammography in Surgical Planning of Breast Cancer Treatment-Analysis of 999 Patients with Primary Operable Breast Cancer

Contrast-enhanced spectral mammography (CESM) is a promising, digital breast imaging method for planning surgeries. The study aimed at comparing digital mammography (MG) with CESM as predictive factors in visualizing multifocal-multicentric cancers (MFMCC) before determining the surgery extent. We analyzed 999 patients after breast cancer surgery to compare MG and CESM in terms of detecting MFMCC. Moreover, these procedures were assessed for their conformity with postoperative histopathology (HP), calculating their sensitivity and specificity. The question was which histopathological types of breast cancer were more frequently characterized by multifocality–multicentrality in comparable techniques as regards the general number of HP-identified cancers. The analysis involved the frequency of post-CESM changes in the extent of planned surgeries. In the present study, MG revealed 48 (4.80%) while CESM 170 (17.02%) MFMCC lesions, subsequently confirmed in HP. MG had MFMCC detecting sensitivity of 38.51%, specificity 99.01%, PPV (positive predictive value) 85.71%, and NPV (negative predictive value) 84.52%. The respective values for CESM were 87.63%, 94.90%, 80.57% and 96.95%. Moreover, no statistically significant differences were found between lobular and NST cancers (27.78% vs. 21.24%) regarding MFMCC. A treatment change was required by 20.00% of the patients from breast-conserving to mastectomy, upon visualizing MFMCC in CESM. In conclusion, mammography offers insufficient diagnostic sensitivity for detecting additional cancer foci. The high diagnostic sensitivity of CESM effectively assesses breast cancer multifocality/multicentrality and significantly changes the extent of planned surgeries. The multifocality/multicentrality concerned carcinoma, lobular and invasive carcinoma of no special type (NST) cancers with similar incidence rates, which requires further confirmation.

Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer

Background

There is a demand for additional alternative methods that can allow the differentiation of the breast tumor into molecular subtypes precisely and conveniently.

Purpose

The present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to associate between the breast cancer molecular subtype and the extracted MR imaging features.

Methods

We analyzed a total of 264 patients (mean age: 47.9 ± 9.7 years; range: 19–81 years) with 264 masses (mean size: 28.6 ± 15.86 mm; range: 5–91 mm) using a Unet model and Gradient Tree Boosting for segmentation and classification.

Results

The tumors were segmented clearly by the Unet model automatically. All the extracted features which including the shape features,the texture features of the tumors and the clinical features were input into the classifiers for classification, and the results showed that the GTB classifier is superior to other classifiers, which achieved F1-Score 0.72, AUC 0.81 and score 0.71. Analyzed the different features combinations, we founded that the texture features associated with the clinical features are the optimal features to different the breast cancer subtypes.

Conclusion

CAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype.