Contrast-Enhanced Mammography (CEM) Capability to Distinguish Molecular Breast Cancer Subtypes

Breast cancer staging with contrast-enhanced imaging. The benefits and drawbacks of MRI, CEM, and dedicated breast CT

Pretherapeutic breast cancer staging is pivotal for patient-centered disease management, guiding treatment stratification and assessing prognostic outcomes. Breast imaging plays a key role in both anatomic and prognostic staging by providing comprehensive insights into disease extent, localization, and tumor aggressiveness characteristics. To date, clinical image-based staging relies on conventional modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), and ultrasound. Considering the phenotypic disparities of breast cancer and their relation to treatment response, other imaging techniques based on contrast-enhanced mechanisms, which highlight areas of increased neovascularity, appear indispensable in breast cancer staging. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers a comprehensive 3-dimensional assessment of extent of disease and characterization of lesions, capturing both morphological and functional aspects which are crucial for treatment and monitoring of the disease. Based on this established approach, contrast-enhanced x-ray-based techniques, with high spatial resolution, such as contrast-enhanced mammography (CEM) and dedicated contrast-enhanced breast computed tomography (dCEBCT), have emerged. This review outlines the current status, limitations, and ongoing challenges associated with each one contrast-enhanced imaging modality, while emphasis is given to key breast cancer manifestations and the optimal interpretation of their imaging phenotypes, in the current era of image-based (anatomic and prognostic) breast cancer staging.

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.

Contrast Enhancement in Breast Cancer: Magnetic Resonance vs. Mammography: A 10-Year Systematic Review

PURPOSE

Contrast Enhancement Magnetic Resonance (CEMR) and Contrast-Enhanced Mammography (CEM) are important diagnostic tools to evaluate breast cancer patients, and both are objects of interest in the literature. The purpose of this systematic review was to select publications from the last ten years in order to evaluate the literature contributions related to the frequency of contrast agents used, administration techniques and the presence of adverse reactions.

METHODS

We have selected, according to the PRISMA statement, publications reviewed on Pub Med in the period from 1 January 2012 to 31 December 2022. The search engine was activated using the following keywords: "CESM", "CEM", "CEDM", "Contrast mammography" for CEM, "DCE-MRI", "Contrast Enhancement MRI" for CEMR, excluding reviews, book chapters and meta-analyses. From the total number of publications, we made a preliminary selection based on titles and abstracts and excluded all articles published in languages other than English and all experimental studies performed on surgical specimen or animal population, as well as all articles for which the extended version was not available. Two readers evaluated all the articles and compiled a pre-compiled form accordingly.

RESULTS

After a preliminary collection of 571 CEM publications, 118 articles were selected, relating to an overall population of 21,178 patients. From a total of 3063 CEMR publications, 356 articles relating to an overall population of 45,649 patients were selected. The most used contrast agents are Iohexol for CEM (39.83%) and Gadopentetic acid (Gd-DTPA) for CEMR (32.5%). Regarding the CEM contrast administration protocol, in 84.7% of cases a dose of 1.5 mL/kg was used with an infusion rate of 2-3 mL/s. Regarding the CEMR infusion protocol, in 71% of cases a dose of 1 mmol/kg was used at an infusion rate of 2-4 mL/s. Twelve out of 118 CEM articles reported allergic reactions, involving 29 patients (0.13%). In DCE-MRI, only one out of 356 articles reported allergic reactions, involving two patients (0.004%). No severe reactions were observed in either cohort of exams.

CONCLUSIONS

CEM and CEMR are essential contrast methods to evaluate breast diseases. However, from the literature analysis, although there are preferences on the uses of the contrast agent (Iohexol for CESM, G-DTPA for CEMR), a wide range of molecules are still used in contrast methods, with different administration protocols. Based on the collected data, it is possible to state that both methods are safe, and no severe reactions were observed in our evaluation.

Performance of Contrast-Enhanced Mammography (CEM) for Monitoring Neoadjuvant Chemotherapy Response among Different Breast Cancer Subtypes

Neoadjuvant chemotherapy (NAT) plays a crucial role in breast cancer (BC) treatment, both in advanced BC and in early-stage BC, with different rates of pathological complete response (pCR) among the different BC molecular subtypes. Imaging monitoring is mandatory to evaluate the NAT efficacy. This study evaluates the diagnostic performance of Contrast-Enhanced Mammography (CEM) in BC patients undergoing NAT. This retrospective two-center study included 174 patients. The breast lesions were classified based on the molecular subtypes in hormone receptor (HR+)/HER2-, HER2+, and triple-negative breast cancer (TNBC). The histopathological analysis performed following surgery was used as a reference standard for the pCR. Sensitivity, specificity, PPV, and NPV were measured overall and for the different subtypes. We enrolled 174 patients, 79/174 (46%) HR+/HER2-, 59/174 (33.9%) HER2+, and 35/174 (20.1%) TNBC; the pCR was found in 64/174 (36.8%), of which 57.1% were TNBCs. In the total population, the CEM sensitivity and specificity were 66.2% and 75.2%, with a PPV of 61.4% and an NPV of 78.8%. The highest specificity (80.9%) and NPV (91.7%) were found in HR+/HER2-, while the highest sensitivity (70%) and PPV appeared (73.7%) in TNBC. The results indicate that CEM is a valid tool to assess the pCR, with different performances among the subtypes of BC.

Comparison of the effectiveness of contrast-enhanced mammography in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population

Purpose

To assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population.

Material and methods

792 patients with 808 breast lesions, in whom the final decision on core-needle biopsy was made based on CEM, and who received the result of histopathological examination, were qualified for a single-centre, retrospective study. Patient electronic records and imaging examinations were reviewed to establish demographics, clinical and imaging findings, and histopathology results. The CEM images were reassessed and assigned to the appropriate American College of Radiology (ACR) density categories.

Results

Extremely dense breasts were present in 86 (10.9%) patients. Histopathological examination confirmed the presence of malignant lesions in 52.6% of cases in the entire group of patients and 43% in the group of extremely dense breasts. CEM incorrectly classified the lesion as false negative in 16/425 (3.8%) cases for the whole group, and in 1/37 (2.7%) cases for extremely dense breasts. The sensitivity of CEM for the group of all patients was 96.2%, specificity - 60%, positive predictive values (PPV) - 72.8%, and negative predictive values (NPV) - 93.5%. In the group of patients with extremely dense breasts, the sensitivity of the method was 97.3%, specificity - 59.2%, PPV - 64.3%, and NPV - 96.7%.

Conclusions

CEM is characterised by high sensitivity and NPV in detecting malignant lesions regardless of the type of breast density. In patients with extremely dense breasts, CEM could serve as a complementary or additional examination in the absence or low availability of MRI.

MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms

BACKGROUND AND OBJECTIVE

Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer respond better to different therapies.

METHODS

In this work, we propose a novel lightweight dual-channel attention-based deep learning model MOB-CBAM that utilizes the backbone of MobileNet-V3 architecture with a Convolutional Block Attention Module to make highly accurate and precise predictions about breast cancer. We used the CMMD mammogram dataset to evaluate the proposed model in our study. Nine distinct data subsets were created from the original dataset to perform coarse and fine-grained predictions, enabling it to identify masses, calcifications, benign, malignant tumors and molecular subtypes of cancer, including Luminal A, Luminal B, HER-2 Positive, and Triple Negative. The pipeline incorporates several image pre-processing techniques, including filtering, enhancement, and normalization, for enhancing the model's generalization ability.

RESULTS

While identifying benign versus malignant tumors, i.e., coarse-grained classification, the MOB-CBAM model produced exceptional results with 99 % accuracy, precision, recall, and F1-score values of 0.99 and MCC of 0.98. In terms of fine-grained classification, the MOB-CBAM model has proven to be highly efficient in accurately identifying mass with (benign/malignant) and calcification with (benign/malignant) classification tasks with an impressive accuracy rate of 98 %. We have also cross-validated the efficiency of the proposed MOB-CBAM deep learning architecture on two datasets: MIAS and CBIS-DDSM. On the MIAS dataset, an accuracy of 97 % was reported for the task of classifying benign, malignant, and normal images, while on the CBIS-DDSM dataset, an accuracy of 98 % was achieved for the classification of mass with either benign or malignant, and calcification with benign and malignant tumors.

CONCLUSION

This study presents lightweight MOB-CBAM, a novel deep learning framework, to address breast cancer diagnosis and subtype prediction. The model's innovative incorporation of the CBAM enhances precise predictions. The extensive evaluation of the CMMD dataset and cross-validation on other datasets affirm the model's efficacy.

Is contrast-enhanced mammography (CEM) an alternative to MRI in assessing the response to primary systemic therapy of breast cancer?

PURPOSE

To evaluate the accuracy of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) in the assessing radiological response to primary systemic therapy (PST).

METHOD

Prospective study between February 2021 and October 2022. Women with breast cancer and indication of PST were enrolled. CEM and MRI were performed before and after PST, and the findings, including size and radiological response pattern, were compared with the size of the residual lesion measured in surgical specimens and its Miller-Payne classification (considered the gold standard). Two of four independent radiologists, with 2 years of CEM experience and 10 years of MRI experience, reviewed the images while being blinded to the results of the other technique. The agreement between measurements was evaluated using the Pearson correlation coefficient (r) and Lin's coefficient.

RESULTS

Forty-eight women with breast cancer who required PST were enrolled in the study, with a mean age of 57.21 ± 10.14 years. A total of thirty-three participants (68.75 %) completed the study. The correlation between CEM and MRI measurements was high before PST (r: 0.97), and local staging was identical for 45 out of 48 patients. MRI demonstrated better accuracy in predicting residual tumor size than CEM, with Lin's coefficient 0.91 and 0.73, respectively. However, no significant differences were observed in predicting response to therapy. Both methods tended to overestimate the size and degree of response in our study, with mean overestimations of 2.87 mm in CEM and 0.51 mm in MRI.

CONCLUSION

CEM was found to be as accurate as MRI in predicting response to PST, indicating its potential as an alternative imaging technique, but further research is necessary.

Correlation between the CEM imaging characteristics and different molecular subtypes of breast cancer

PURPOSE

To investigate the correlation between the contrast-enhanced mammography (CEM) imaging characteristics and different molecular subtypes of breast cancer (BC).

METHODS

We retrospectively included 313 eligible female patients who underwent CEM examination and surgery in our hospital from July 2017 to July 2021. Their lesions were confirmed on histopathological examination and immunohistochemical analysis. BC was divided into luminal A, luminal B, HER2-enriched, and triple-negative BC (TNBC) subtypes according to immunohistochemical markers. Nine features were extracted from CEM images, including tumor shape, margins, spiculated mass, lobulated mass, malignant calcification, lesion conspicuity, internal enhancement pattern, multifocal mass, and swollen axillary lymph nodes. Statistical analysis was performed using SPSS 25.0. Univariate analysis and binomial regression were used to analyze the correlation between CEM imaging features and BC molecular subtypes.

RESULTS

There were 184 (58.8 %) Luminal A, 44 (14.1 %) Luminal B, 47 (15.0 %) HER-2-enriched and 38 (12.1 %) TNBC, respectively. Molecular subtypes were significantly related to the tumor shape, margins, spiculated mass, internal enhancement pattern, malignant calcification and swollen axillary lymph nodes. Spiculated and calcified tumors were associated with Luminal subtypes, especially Luminal B (P < 0.05). Irregular tumor shape and malignant calcification were associated with HER-2-enriched subtype (P < 0.05). Oval or round tumor shape, rim enhancement, and swollen axillary lymph nodes were associated with TNBC (P < 0.05).

CONCLUSION

CEM imaging features could distinguish BC molecular subtypes. In particular, TNBC showed oval or round tumor shape, rim enhancement, and swollen axillary lymph nodes, providing insights into the diagnosis and prognosis of TNBC.

BIRADS 4 - Is it possible to downgrade lesions that do not enhance on recombinant contrast-enhanced mammography images?

PURPOSE

Analysis of the morphology of lesions classified into the BI-RADS 4 category and assessment of the possibility of downgrade the BI-RADS category in those that did not show enhancement on recombinant contrast-enhanced mammography (CEM) images.

METHOD

The retrospective, single-center study included 528 patients who underwent a core needle biopsy performed from January 2017 to November 2022 due to a breast lesion classified as BI-RADS 4 on CEM. Patients' electronic records and imaging examinations were reviewed. Individual lesions were classified into the morphological categories of mass, non-mass, and microcalcifications. Sensitivity, specificity, positive as well as negative predictive values were calculated for the whole group and individual morphological categories. The influence of the lesions' diameter on the results was analyzed.

RESULTS

CEM NPV for the whole group was 93.9% (±95% CI: 90.0-96.4), for mass lesions 100% (±95% CI: 94.5-100), for non-mass lesions 97.8% (±95% CI: 87.0-99.9) and 87.9% (±95% CI: 80.3-93.0) for microcalcifications. Given that 230 out of 383 benign lesions were not contrast-enhancing, 60.1% of unnecessary CNBs would have been correctly avoided. CEM sensitivity for lesions < 20 mm was lower than for lesions ≥ 20 mm and was respectively 86.6% (±95% CI: 76.8-92.8) vs 94.6% (±95% CI: 86.0-98.2), respectively.

CONCLUSION

CEM is characterized by high sensitivity in the detection of malignant lesions in the case of lesions with mass and non-mass morphology. The high NPV for recombinant images suggests that in the case of these lesions, the lack of enhancement supports the benign nature of the lesion and may lead to a downgrade of the BI-RADS category.

Is the Level of Contrast Enhancement on Contrast-Enhanced Mammography (CEM) Associated with the Presence and Biological Aggressiveness of Breast Cancer?

There is limited information about whether the level of enhancement on contrast-enhanced mammography (CEM) can be used to predict malignancy. The purpose of this study was to correlate the level of enhancement with the presence of malignancy and breast cancer (BC) aggressiveness on CEM. This IRB-approved, cross-sectional, retrospective study included consecutive patients examined with CEM for unclear or suspicious findings on mammography or ultrasound. Excluded were examinations performed after biopsy or during neoadjuvant treatment for BC. Three breast radiologists who were blinded to patient data evaluated the images. The enhancement intensity was rated from 0 (no enhancement) to 3 (distinct enhancement). ROC analysis was performed. Sensitivity and negative likelihood ratio (LR-) were calculated after dichotomizing enhancement intensity as negative (0) versus positive (1-3). A total of 156 lesions (93 malignant, 63 benign) in 145 patients (mean age 59 ± 11.6 years) were included. The mean ROC curve was 0.827. Mean sensitivity was 95.4%. Mean LR- was 0.12%. Invasive cancer presented predominantly (61.8%) with distinct enhancement. A lack of enhancement was mainly observed for ductal carcinoma in situ. Stronger enhancement intensity was positively correlated with cancer aggressiveness, but the absence of enhancement should not be used to downgrade suspicious calcifications.