Contrast mammography in clinical practice: Current uses and potential diagnostic dilemmas

A deep learning approach for virtual contrast enhancement in Contrast Enhanced Spectral Mammography

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first requires intravenously administering an iodinated contrast medium. Then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations, this work proposes using deep generative models for virtual contrast enhancement on CESM, aiming to make CESM contrast-free and reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model's performance, also exploiting radiologists' assessments, on a novel CESM dataset that includes 1138 images. As a further contribution to this work, we make the dataset publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.

Contrast-Enhanced Mammography (CEM) compared to Breast Magnetic Resonance (MRI) in the evaluation of breast lobular neoplasia

PURPOSE

To compare the diagnostic performance (detection, assessment of correct disease extent and multifocality/centricity) of Contrast-Enhanced Mammography (CEM) Versus Breast Magnetic Resonance (MRI) in the study of lobular neoplasms.

METHODS

We retrospectively selected all the patients who underwent surgery for a lobular breast neoplasm, either an in situ or an invasive tumor, and had undergone both breast CEM and MRI examinations during the pre-surgical planning. Wilcoxon Signed Rank test was performed to assess the differences between size measurements using the different methods and the post-surgical pathological measurements, considered the gold standard. The agreement in identifying multifocality/multicentricity among the different methods and the pathology was assessed using the Kappa statistics.

RESULTS

We selected 19 patients, of which one presented a bilateral neoplasm. Then, the images of these 19 patients were analyzed, for a total of 52 malignant breast lesions. We found no significant differences between the post-surgical pathological size of the lesions and the calculated size with CEM and MRI (p-value of the difference respectively 0.71 and 0.47). In all 20 cases, neoplasm detection was possible both with CEM and MRI. CEM and MRI showed an excellent ability to identify multifocal and multicentric cases (K statistic equal to 0.93 for both the procedures), while K statistic was 0.11 and 0.59 for FFDM and US, respectively.

CONCLUSION

The findings of this study suggest that CEM is a reliable imaging technique in the preoperative setting of patients with lobular neoplasm, with comparable results to breast MRI.

False-Positive and False-Negative Contrast-enhanced Mammograms: Pitfalls and Strategies to Improve Cancer Detection

Contrast-enhanced mammography (CEM) is a relatively new breast imaging modality that uses intravenous contrast material to increase detection of breast cancer. CEM combines the structural information of conventional mammography with the functional information of tumor neovascularity. Initial studies have demonstrated that CEM and MRI perform with similar accuracies, with CEM having a slightly higher specificity (fewer false positives), although larger studies are needed. There are various reasons for false positives and false negatives at CEM. False positives at CEM can be caused by benign lesions with vascularity, including benign tumors, infection or inflammation, benign lesions in the skin, and imaging artifacts. False negatives at CEM can be attributed to incomplete or inadequate visualization of lesions, marked background parenchymal enhancement (BPE) obscuring cancer, lack of lesion contrast enhancement due to technical issues or less-vascular cancers, artifacts, and errors of lesion perception or characterization. When possible, real-time interpretation of CEM studies is ideal. If additional views are necessary, they may be obtained while contrast material is still in the breast parenchyma. Until recently, a limitation of CEM was the lack of CEM-guided biopsy capability. However, in 2020, the U.S. Food and Drug Administration cleared two devices to support CEM-guided biopsy using a stereotactic biopsy technique. The authors review various causes of false-positive and false-negative contrast-enhanced mammograms and discuss strategies to reduce these diagnostic errors to improve cancer detection while mitigating unnecessary additional imaging and procedures. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Contrast-enhanced Mammography Artifacts and Pitfalls: Tips and Tricks to Avoid Misinterpretation

Contrast-enhanced mammography (CEM) involves addition of intravenous iodinated contrast material at digital mammography, thus increasing the ability to detect breast cancer owing to tumor contrast enhancement. After image acquisition, interpretation includes careful assessment of the technique, artifacts, and pitfalls and reporting with a standard lexicon category and appropriate follow-up recommendations. Artifacts and pitfalls that may cause image misinterpretation should be detected and distinguished from pathologic conditions. Different artifacts apparent on CEM images are usually caused during image acquisition and include CEM-specific and contrast agent-related artifacts, apart from the typical digital mammography artifacts. The pitfalls are related to technical and diagnostic difficulties. One disadvantage of CEM that MRI does not have is a technical factor related to a mammography technique that consists of blind spots that may not be included in the imaging field of mammography views, including the axilla, medial region of the breast, or areas close to the breast wall. Normal breast tissue enhancement called background parenchymal enhancement is also present at CEM and may affect interpretation performance. Diagnostic pitfalls are caused by minimally enhancing lesions, such as invasive lobular carcinomas and mucinous carcinomas, which are difficult to detect with CEM, resulting in false-negative findings. Benign lesions can show enhancement at CEM and represent false-positive lesions that should also be recognized. The authors discuss image interpretation of CEM studies and focus on the artifacts and pitfalls that may be encountered. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

How to Recognize and Correct Artifacts on Contrast-Enhanced Mammography

Contrast-enhanced mammography (CEM) has emerged as an important new technology in breast imaging. It can demonstrate a number of imaging artifacts that have the potential to limit interpretation by either obscuring or potentially mimicking disease. Commonly encountered artifacts on CEM include patient motion artifacts (ripple and misregistration), pectoral highlighting artifact, breast implant artifact, halo artifact, corrugation artifact, cloudy fat artifact, contrast artifacts (retention and contamination), skin artifacts (skin line enhancement and skin overexposure), and skin lesions. Skin lesions may demonstrate a variety of imaging appearances and have both benign and malignant etiologies. It is important that the technologist, radiologist, and physicist be aware of potential artifacts and skin enhancement on CEM that may affect interpretation and understand their causes and potential solutions.

Diagnostic Performance of Contrast-Enhanced Digital Mammography versus Conventional Imaging in Women with Dense Breasts

The aim of this prospective study was to compare the diagnostic performance of contrast-enhanced mammography (CEM) versus digital mammography (DM) combined with breast ultrasound (BUS) in women with dense breasts. Between March 2021 and February 2022, patients eligible for CEM with the breast composition category ACR BI-RADS c-d at DM and an abnormal finding (BI-RADS 3-4-5) at DM and/or BUS were considered. During CEM, a nonionic iodinated contrast agent (Iohexol 350 mg I/mL, 1.5 mL/kg) was power-injected intravenously. Images were evaluated independently by two breast radiologists. Findings classified as BI-RADS 1-3 were considered benign, while BI-RADS 4-5 were considered malignant. In case of discrepancies, the higher category was considered for DM+BUS. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, using histology/≥12-month follow-up as gold standards. In total, 51 patients with 65 breast lesions were included. 59 (90.7%) abnormal findings were detected at DM+BUS, and 65 (100%) at CEM. The inter-reader agreement was excellent (Cohen's k = 0.87 for DM+BUS and 0.97 for CEM). CEM showed a 93.5% sensitivity (vs. 90.3% for DM+BUS), a 79.4-82.4% specificity (vs. 32.4-35.5% for DM+BUS) (McNemar p = 0.006), a 80.6-82.9% PPV (vs. 54.9-56.0% for DM+BUS), a 93.1-93.3% NPV (vs. 78.6-80.0% for DM+BUS), and a 86.1-87.7% accuracy (vs. 60.0-61.5% for DM+BUS). The AUC was higher for CEM than for DM+BUS (0.865 vs. 0.613 for Reader 1, and 0.880 vs. 0.628, for Reader 2) (p < 0.001). In conclusion, CEM had a better diagnostic performance than DM and BUS alone and combined together in patients with dense breasts.

Preoperative staging by multimodal imaging in newly diagnosed breast cancer: Diagnostic performance of contrast-enhanced spectral mammography compared to conventional mammography, ultrasound, and MRI

PURPOSE

To compare contrast-enhanced spectral mammography (CESM) with mammography (Mx), ultrasound (US), and magnetic resonance imaging (MRI) regarding breast cancer detection rate and preoperative local staging.

MATERIAL AND METHODS

This prospective observational, single-centre study included 128 female patients (mean age 55.8 ± 11.5 years) with a newly diagnosed malignant breast tumour during routine US and Mx were prospectively enrolled. CESM and MRI examinations were performed within the study. Analysis included interreader agreement, tumour type and grade distribution, detection rates (DR), imaging morphology, contrast-enhancement and was performed by two independent readers blinded to patient history and histopathological diagnosis. Assessment of local disease extent was compared between modalities via Bland-Altman plots.

RESULTS

One-hundred-and-ten tumours were classified as NST (85.9%), 4 as ILC (3.1%) and 10 as DCIS (7.8%). DR was highest for MRI (128/128, 100.0%), followed by US (124/128, 96.9%) and CESM (123/128, 96.1%) and lowest for conventional Mx (106/128, 82.8%) (p = 0.0002). Higher breast density did not negatively affect DR of US, CESM or MRI. Local tumour extent measurements based on CESM (Bland-Altman bias 6.6, standard deviation 30.2) showed comparable estimation results to MRI, surpassing Mx (23.4/43.7) and US (35.4/40.5). Even though detection of multifocality and multicentricity was highest for CESM and MRI (p < 0.0001), second-look rates, i.e., targeted US examinations after MRI or CESM, were significantly lower for CESM (10.2% of cases) compared to MRI (16.2%) with a significantly higher true positive rate for CESM (72.0%) vs. MRI (42.5%).

CONCLUSION

CESM is a viable alternative to MRI for lesion detection and local staging in newly diagnosed malignant breast cancer and provides higher specificity in regard to second-look examinations.

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.

Diagnostic Challenge of Invasive Lobular Carcinoma of the Breast: What Is the News? Breast Magnetic Resonance Imaging and Emerging Role of Contrast-Enhanced Spectral Mammography

Invasive lobular carcinoma is the second most common histologic form of breast cancer, representing 5% to 15% of all invasive breast cancers. Due to an insidious proliferative pattern, invasive lobular carcinoma remains clinically and radiologically elusive in many cases. Breast magnetic resonance imaging (MR) is considered the most accurate imaging modality in detecting and staging invasive lobular carcinoma and it is strongly recommended in pre-operative planning for all ILC. Contrast-enhanced spectral mammography (CESM) is a new diagnostic method that enables the accurate detection of malignant breast lesions similar to that of breast MR. CESM is also a promising breast imaging method for planning surgeries. In this study, we compare the ability of contrast-enhanced spectral mammography (CESM) with breast MR in the preoperative assessment of the extent of invasive lobular carcinoma. All patients with proven invasive lobular carcinoma treated in our breast cancer center underwent preoperative breast MRI and CESM. Images were reviewed by two dedicated breast radiologists and results were compared to the reference standard histopathology. CESM was similar and in some cases more accurate than breast MR in assessing the extent of disease in invasive lobular cancers. Further evaluation in larger prospective randomized trials is needed to validate our preliminary results.

Identifying factors that may influence the classification performance of radiomics models using contrast-enhanced mammography (CEM) images

Background

Radiomics plays an important role in the field of oncology. Few studies have focused on the identification of factors that may influence the classification performance of radiomics models. The goal of this study was to use contrast-enhanced mammography (CEM) images to identify factors that may potentially influence the performance of radiomics models in diagnosing breast lesions.

Methods

A total of 157 women with 161 breast lesions were included. Least absolute shrinkage and selection operator (LASSO) regression and the random forest (RF) algorithm were employed to construct radiomics models. The classification result for each lesion was obtained by using 100 rounds of five-fold cross-validation. The image features interpreted by the radiologists were used in the exploratory factor analyses. Univariate and multivariate analyses were performed to determine the association between the image features and misclassification. Additional exploratory analyses were performed to examine the findings.

Results

Among the lesions misclassified by both LASSO and RF ≥ 20% of the iterations in the cross-validation and those misclassified by both algorithms ≤5% of the iterations, univariate analysis showed that larger lesion size and the presence of rim artifacts and/or ripple artifacts were associated with more misclassifications among benign lesions, and smaller lesion size was associated with more misclassifications among malignant lesions (all p <  0.050). Multivariate analysis showed that smaller lesion size (odds ratio [OR] = 0.699, p = 0.002) and the presence of air trapping artifacts (OR = 35.568, p = 0.025) were factors that may lead to misclassification among malignant lesions. Additional exploratory analyses showed that benign lesions with rim artifacts and small malignant lesions (< 20 mm) with air trapping artifacts were misclassified by approximately 50% more in rate compared with benign and malignant lesions without these factors.

Conclusions

Lesion size and artifacts in CEM images may affect the diagnostic performance of radiomics models. The classification results for lesions presenting with certain factors may be less reliable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00460-8.

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.

Artifacts in contrast-enhanced mammography: are there differences between vendors?

PURPOSE

Contrast-Enhanced Mammography (CEM) produces a dual-energy subtracted (DES) image that demonstrates iodine uptake (neovascularity) in breast tissue. We aim to review a range of artifacts on DES images produced using equipment from two different vendors and compare their incidence and subjective severity.

METHODS

We retrospectively reviewed CEM studies performed between September 2013 and March 2017 using GE Senographe Essential (n = 100) and Hologic Selenia Dimensions (n = 100) equipment. Artifacts were categorized and graded in severity by a subspecialist breast radiologist and one of two medical imaging technologists in consensus. The incidence of artifacts between vendors was compared by calculating the relative risk, and the severity gradings were compared using a Wilcoxon rank-sum test.

RESULTS

Elephant rind, corrugations and the black line on chest wall artifact were seen exclusively in Hologic images. Artifacts such as cloudy fat, negative rim around lesion and white line on pectoral muscle were seen in significantly more Hologic images (p < 0.05) whilst halo, ripple, skin line enhancement, black line on pectoral muscle, bright pectorals, chest wall high-lighting and air gap were seen in significantly more GE images (p < 0.05). The severity gradings for cloudy fat had a significantly higher mean rank in Hologic images (p < 0.001) whilst halo and ripple artifacts had a significantly higher mean rank in GE images (p < 0.001 and p = 0.028 respectively).

CONCLUSION

The type, incidence and subjective severity of CEM-specific artifacts differ between vendors. Further research is needed, but differences in algorithms used to produce the DE image are postulated to be a significant contributor.

Axillary Nodal Metastases from Carcinoma of Unknown Primary (CUPAx): Role of Contrast-Enhanced Spectral Mammography (CESM) in Detecting Occult Breast Cancer

Axillary lymph node metastases of occult breast cancer (CUPAx) is an unusual condition that represents both a diagnostic and therapeutic challenge. The first steps in the diagnostic work-up of patients with CUPAx are the histological analysis of the lymph node metastasis and the execution of basic breast diagnostic imaging (mammography and ultrasound). In the case of occult breast cancer, breast Magnetic Resonance (MR) must be performed. Breast MR identifies a suspicious lesion in many patients and second-look ultrasound detects a corresponding ultrasound alteration in about half of cases, allowing the performance of a US-guided biopsy. In the case of an MR-only lesion, MR-guided biopsy is mandatory. We present a case of CUPAx in which contrast-enhanced spectral mammography (CESM) is used to help the detection of occult breast cancer and to guide stereotactic vacuum breast biopsy (VABB). CESM is a new breast imaging technique that is proving to have good performance in breast cancer detection and that is showing potential in the identification of occult breast cancer in a CUPAx setting. The use of an innovative and personalized breast imaging approach in breast cancer patients improves diagnostic possibilities and promises to become the focus in decision strategies.

Polymer Nanoparticles and Nanomotors Modified by DNA/RNA Aptamers and Antibodies in Targeted Therapy of Cancer

Polymer nanoparticles and nano/micromotors are novel nanostructures that are of increased interest especially in the diagnosis and therapy of cancer. These structures are modified by antibodies or nucleic acid aptamers and can recognize the cancer markers at the membrane of the cancer cells or in the intracellular side. They can serve as a cargo for targeted transport of drugs or nucleic acids in chemo- immuno- or gene therapy. The various mechanisms, such as enzyme, ultrasound, magnetic, electrical, or light, served as a driving force for nano/micromotors, allowing their transport into the cells. This review is focused on the recent achievements in the development of polymer nanoparticles and nano/micromotors modified by antibodies and nucleic acid aptamers. The methods of preparation of polymer nanoparticles, their structure and properties are provided together with those for synthesis and the application of nano/micromotors. The various mechanisms of the driving of nano/micromotors such as chemical, light, ultrasound, electric and magnetic fields are explained. The targeting drug delivery is based on the modification of nanostructures by receptors such as nucleic acid aptamers and antibodies. Special focus is therefore on the method of selection aptamers for recognition cancer markers as well as on the comparison of the properties of nucleic acid aptamers and antibodies. The methods of immobilization of aptamers at the nanoparticles and nano/micromotors are provided. Examples of applications of polymer nanoparticles and nano/micromotors in targeted delivery and in controlled drug release are presented. The future perspectives of biomimetic nanostructures in personalized nanomedicine are also discussed.