Contrast-enhanced Mammography Research Articles

Stereotactic biopsy with contrast-enhanced mammography: theinitial Australian experience.

Contrast-enhanced mammography (CEM) and MRI detect 'contrast-only' lesions (COLs) occult on standard breast imaging (ultrasound and conventional mammography). Until recently, MRI was the only reliable method of biopsy. This study presents the first Australian experience with CEM-guided biopsy (CEMBx) and the lessons learnt. A prospective audit of the first 15 consecutive patients who underwent CEMBx for COLs was performed. Indications for contrast imaging, patient and lesion characteristics, procedural details, radiation dose and pathology data were collected. The 15 women were aged 37-81 years (mean 59 years). Indications for contrast imaging were problem solving (n = 3), moderate risk screening (n = 2), cancer staging (n = 9) and symptoms (n = 1). The COLs were non-mass (n = 14), mass (n = 1) and an enhancing asymmetry (n = 1). For one patient, two lesions were sampled during the same event. All lesions enhanced and were successfully sampled followed by marker clip insertion. Most biopsies (87.5%) were performed with the breast in cranio-caudal compression using a horizontal approach. Procedural duration ranged from 13 to 33 min (mean 22 min). Radiation dose was similar to standard stereotactic biopsy. Post-biopsy hematomas occurred in three patients, none required intervention. Clip displacement occurred in three cases. Core biopsy histopathology results were benign (n = 8), malignant (n = 7) and a borderline breast lesion (BBL) (n = 1). Patient satisfaction rates were high. Imaging follow-up is ongoing. CEMBx is a quick, safe and reliable alternative to MRIBx to sample COLs.

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Background. Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in breast imaging reporting and data system (BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM. Materials and methods. A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated. Results. The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2–71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements. Conclusion. The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Abstract PO4-07-09: CONTRAST-ENHANCED MAMMOGRAPHY IN LOCAL STAGING OF SCREEN-DETECTED BREAST CANCER: ADDITIONAL LESIONS AND CHANGES TO CLINICAL MANAGEMENT

Abstract Background BreastScreen Australia, a mammographic population screening program, provides assessment of suspected breast cancer to the point of diagnosis using a combination of conventional imaging and percutaneous biopsy. Comprehensive local staging, not performed at BreastScreen, determines the extent of disease and may identify additional clinically significant breast abnormalities. Local staging options include completion of bilateral mammographic work-up, ultrasound, and/or contrast-based imaging (CBI) (magnetic resonance imaging (MRI) or contrast-enhanced mammography (CEM)), with biopsy as required. We introduced CBI for local staging after diagnosis of screen-detected cancer at our academic hospital breast service in Melbourne, Australia. We report diagnostic findings for otherwise occult disease and their impact on treatment decisions in women who underwent CEM for local staging. Material and methods Women with screen-detected breast cancer who underwent CEM for local staging between November 2018 to April 2022 were identified retrospectively. The reporting breast-specialised radiologist compared BreastScreen and CEM images to identify additional enhancing abnormalities. Additional abnormalities were further investigated with preoperative percutaneous biopsy, surgical biopsy, or problem-solving CBI. Additional CEM-detected abnormalities were documented, with invasive cancer or DCIS recorded as true positive (TP) and any other findings as false positive (FP). Impact on surgical decisions was assessed. Results 204 patients underwent CEM. 62/204 (30%) patients had 76 additional abnormalities, of which 36 (47%) were TP and 40 (53%) FP. CEM identified otherwise occult malignant lesions in 30/204 (15%) patients. TPs comprised 75% invasive cancers and 25% DCIS. 83% (30/36) of malignant abnormalities were ipsilateral to the index lesion, while 17% (6/36) were contralateral. The majority of additional invasive cancers were Grade 2 (20/27, 74.1%), followed by Grade 1 (4/27, 14.8%) then Grade 3 (3/27, 11.1%). All additional invasive cancers had the same phenotype as the patient’s index cancer (ER+/HER2-), except for one abnormality which was ER+/HER2+ where the index cancer was ER+/HER2-. The FP abnormalities consisted of normal breast tissue (20/40, 50%), benign lesions (16/40, 40%) and atypical proliferative lesions (4/40, 10%). Occult malignancies were more common for patients with higher background parenchymal enhancement (20% for moderate/marked vs 4% for minimal/mild, p=0.0023), with no statistically significant differences found by breast density (BIRADS A or B = 12%, vs BIRADS C or D = 20%, p=0.23) or by age (40-49=38%, 50-59=13%, 60-69=13%, ³70=19%, p=0.88). Additional abnormalities found on CEM resulted in surgical management change in 45/204 (22%) patients, including wider resection (24/45), conversion to mastectomy (8/45), contralateral breast surgery (6/45), additional ipsilateral excision (5/45), and bracketing (2/45). Conclusions The use of CEM for local staging of screen-detected breast cancers identified otherwise occult malignancy in 15% of patients. Age and mammographic density did not identify groups at minimal risk of additional findings. Pathology of additional malignancy suggests it is clinically significant. CEM may improve local staging and direct appropriate management of screen-detected breast cancers. Citation Format: Caroline MacCallum, Kenneth Elder, Carolyn Nickson, Kelly Ruecker, Allan Park, Bruce Mann. CONTRAST-ENHANCED MAMMOGRAPHY IN LOCAL STAGING OF SCREEN-DETECTED BREAST CANCER: ADDITIONAL LESIONS AND CHANGES TO CLINICAL MANAGEMENT [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-09.

Abstract PS05-06: Outcomes of Surveillance using Contrast Enhanced Mammography in Women with a Personal History of Breast Cancer

Abstract Background Women with a personal history of breast cancer (PHBC) have a high rate of subsequent breast malignancy. Annual mammography with selective ultrasound has been the standard method of surveillance after breast cancer for many years, aiming for early detection, treatment and improved survival. In surveillance populations, interval cancers, which can account for ~30% of subsequent breast cancers, are more likely to be larger, hormone receptor negative and lymph node positive than cancers detected by surveillance (Lee et al, Radiology 2021). Interval cancer rates of 3.6 per 1000 mammographic screens have been reported previously in women with PHBC (Houssami et al, JAMA 2011). For women with PHBC a more sensitive surveillance approach may be justified, noting mammography has lower program sensitivity in PHBC surveillance than in screening. Magnetic resonance imaging (MRI) is used selectively but with resource and access limitations. Contrast enhanced mammography (CEM) offers a more sensitive modality than conventional mammography with specificity comparable to MRI, but its utility for surveillance is uncertain. Methods Retrospective study of 1,190 women with PHBC who commenced annual CEM surveillance in an Australian hospital setting between June 2016 and December 2022 (Elder et al, Breast Cancer Res Treat 2023) combining outcomes of initial CEM and any subsequent surveillance imaging over that period, including incident surveillance-detected cancers and interval cancers (cancers diagnosed in the year following a negative surveillance episode), recalls for assessment, the contribution of contrast to recall, and pathology and treatment details for cancer diagnoses. Outcomes were reported using descriptive statistics and hazards modelling. Results There were 3,784 episodes for analysis: 1,190 first CEM surveillance episodes, and 2,594 subsequent surveillance episodes of which >90% were contrast based imaging. 79% of women had at least three annual rounds of surveillance imaging. 186 cases from the total 3784 surveillance episodes were recalled for assessment (recall rate 4.9%). 72 (39%) recalled cases identified malignant lesions (true positives (TP)), with 50 invasive cancers and 22 cases of DCIS. 114 (61%) recalled cases were false positives (FP). 51% of cases were only recalled due to contrast and 35% of these were TP. Invasive cancers were predominantly stage 1 (64%) or stage 2 (32%) and most were grade 2 (44%) or grade 3 (47%). The median invasive cancer size was 16mm (IQR 9-25mm). 62% of invasive cancers were hormone receptor positive HER2 negative, and 24% were triple negative. The median DCIS size was 19mm (IQR 10-26mm) with only a single low grade case. 40% of invasive cancers and 59% of DCIS were recalled due to contrast. Comparison of tumour features indicates similarities between contrast-directed recalls and other diagnoses; conclusive findings would require a larger sample. Five interval cancers were identified, of which three were asymptomatic and detected on surveillance imaging scheduled early for other reasons. Thus, the rate of symptomatic interval cancers was 0.8 per 1000 screens (program sensitivity 96.0%). Surveillance-detected cancer rates differed significantly by index cancer subtype (χ2=11.9, p=0.0026), with highest rates for women with triple negative index cancers. Incidence cancer rates were higher among the 6.9% of women with moderate or marked BPE at first CEM surveillance episode (χ2=8.8, p=0.032), but did not differ significantly by age group (χ2=5.2, p=0.39) nor breast density (χ2=4.7, p=0.19). Conclusions Routine use of CEM in annual surveillance of women with PHBC led to 1.85-fold increase in the detection of clinically significant malignant lesions, with lower interval cancer rates than previous published series of women with PHBC. CEM appears to increase the sensitivity of surveillance programs for women with PHBC, improving on imaging without contrast. Citation Format: Julia Matheson, Carolyn Nickson, Kenneth Elder, Allan Park, Allison Rose, Bruce Mann. Outcomes of Surveillance using Contrast Enhanced Mammography in Women with a Personal History of Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS05-06.

Abstract ED02-03: Innovative approaches at the diagnosis and treatment of invasive lobular carcinoma

Abstract Invasive Lobular Carcinoma (ILC) represents a unique subtype of breast cancer characterized by distinctive clinical and molecular features. This presentation aims to delve into innovative approaches in the diagnosis and treatment of ILC, focusing on novel techniques in imaging, unique molecular findings, signaling pathway alterations, targetable biomarkers, and microenvironment profiling that can pave the way for the development of novel therapies. Imaging Techniques in ILC: Accurate diagnosis is the foundation for effective treatment. ILC often presents diagnostic challenges due to the fact that lobular cells tends to infiltrate the affected organs in a diffuse manner instead of forming a discrete tumor mass making diagnostic imaging such as screening mammograms and CT scans less precise. This presentation will explore cutting-edge imaging techniques such as contrast-enhanced mammography, novel radioisotopes for positron emission tomography (PET) such as Fluoroestradiol F18 (FES), Fluciclovine and prostate-specific membrane antigen (PSMA) for improved detection and characterization of ILC lesions. Furthermore, we will discuss emerging modalities like radiomics and artificial intelligence, which hold promise in enhancing early diagnosis and risk assessment. Molecular Insights into ILC: ILC is distinguished by its unique molecular profile, notably the loss of E-cadherin expression. We will delve into recent advancements in understanding the molecular landscape of ILC, including genomic alterations, epigenetic changes, and the role of signaling pathways providing insights into potential therapeutic vulnerabilities. Targetable Biomarkers and Pathways: Identification of targetable biomarkers is essential for the development of precision therapies in ILC. This presentation will highlight promising targets, including ERBB2 (HER2), ROS1, TROP2, AKT1 along with other novel biomarkers. Microenvironment Profiling: The tumor microenvironment plays a pivotal role in cancer progression and therapy response. We will discuss recent advancements in characterizing the ILC microenvironment, including the immune cell infiltrate and stromal components. Understanding the dynamic interplay between the tumor and its microenvironment can inform the development of immunotherapies and combination strategies tailored to ILC. In conclusion, this presentation will provide a comprehensive overview of the latest innovations in ILC diagnosis and treatment. Our exploration of advanced imaging techniques, molecular findings, targetable pathways, biomarkers, and microenvironment profiling will shed light on the exciting potential for personalized therapeutic approaches in the management of ILC. By staying at the forefront of these developments, we can pave the way for improved outcomes and enhanced quality of life for patients with ILC. Citation Format: J. Mouabbi. Innovative approaches at the diagnosis and treatment of invasive lobular carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr ED02-03.

Abstract PO5-07-10: CONTRAST MAMMOGRAPHY FOR PREOPERATIVE STAGING OF PATIENTS WITH EARLY BREAST CANCER: PRELIMINARY RESULTS

Abstract a) Brief description of the objective(s) The aim of this study is to compare contrast-enhanced mammography (CM) with magnetic resonance imaging (MRI) for preoperative locoregional staging of patients with early-stage breast cancer. b) Material(s) and method(s) Prospective, single-center study, approved by the Research Ethics Committee, which included patients with a histological diagnosis of invasive breast carcinoma, candidates for initial medical treatment. Patients treated with neoadjuvant chemotherapy and those in whom the tumor was completely resected in the initial biopsy were excluded. CM and MRI were performed in the week before surgery and the anatomopathological result of the surgical specimen was considered the gold standard. Pearson's modification (r) was used to compare tumor size between different methods. c) Results and discussion Thirty-seven patients were included, with a mean age of 54.5±6.9 years (43-70 years). The mean size of the tumors in pathology was 2.9±1.4 cm (1.1-8.0 cm) and the most common histological type was invasive ductal carcinoma (n=34;87.2%). The clinical stage was IA for 14 patients (37.8%) and IIA for 23 patients (62.2%). The main tumor was identified in 33 cases (89.2%) on conventional mammography and in all cases (100%) on both CM and MRI. There was excellent elasticity between tumor size measured on CM and MRI (r=0.897;p< 0.01). When compared with tumor size in pathology, both methods were adopted in a good way (p< 0.01), however, MRI was functionally superior to CM (r=0.847 vs. 0.683), while mammography conventional showed no therapeutic effect (r=0.222;p=0.21). Multifocal lesions were observed on CM in 2 cases (5.4%) and on MRI in 6 cases (16.2%), with 1 case confirmed as multifocal and the others presenting DCIS associated with the surgical specimen. Suspicious additional lesions were seen in 4 patients (10.8%) on CM and in 3 patients (8.1%) on MRI, of which one was confirmed as malignant in the contralateral breast, which had not been identified in previous exams. d) Conclusions CM showed good adaptability with MRI for assessing tumor size and searching for additional lesions, and may be a cheaper and more accessible alternative for preoperative staging in patients at an early clinical stage. Citation Format: Andressa Amorim, Andre Mattar, Almir Bitencourt, Paula Moraes, Flora Finguerman. CONTRAST MAMMOGRAPHY FOR PREOPERATIVE STAGING OF PATIENTS WITH EARLY BREAST CANCER: PRELIMINARY RESULTS [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-10.

Clinical and Imaging Characteristics of Contrast-enhanced Mammography and MRI to Distinguish Microinvasive Carcinoma from Ductal Carcinoma In situ

Rationale and ObjectivesThe prognosis of ductal carcinoma in situ with microinvasion (DCISM) is more similar to that of small invasive ductal carcinoma (IDC) than to pure ductal carcinoma in situ (DCIS). It is particularly important to accurately distinguish between DCISM and DCIS. The present study aims to compare the clinical and imaging characteristics of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) between DCISM and pure DCIS, and to identify predictive factors of microinvasive carcinoma, which may contribute to a comprehensive understanding of DCISM in clinical diagnosis and support surveillance strategies, such as surgery, radiation, and other treatment decisions. Materials and MethodsForty-seven female patients diagnosed with DCIS were included in the study from May 2019 to August 2023. Patients were further divided into two groups based on pathological diagnosis: DCIS and DCISM. Clinical and imaging characteristics of these two groups were analyzed statistically. The independent clinical risk factors were selected using multivariate logistic regression and used to establish the logistic model [Logit(P)]. The diagnostic performance of independent predictors was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test. ResultsIn CEM, the maximum cross-sectional area (CSAmax), the percentage signal difference between the enhancing lesion and background in the craniocaudal and mediolateral oblique projection (%RSCC, and %RSMLO) were found to be significantly higher for DCISM compared to DCIS (p = 0.001; p < 0.001; p = 0.008). Additionally, there were noticeable statistical differences in the patterns of enhancement morphological distribution (EMD) and internal enhancement pattern (IEP) between DCIS and DCISM (p = 0.047; p = 0.008). In MRI, only CSAmax (p = 0.012) and IEP (p = 0.020) showed significant statistical differences. The multivariate regression analysis suggested that CSAmax (in CEM or MR) and %RSCC were independent predictors of DCISM (all p < 0.05). The area under the curve (AUC) of CSAmax (CEM), %RSCC (CEM), Logit(P) (CEM), and CSAmax (MR) were 0.764, 0.795, 0.842, and 0.739, respectively. There were no significant differences in DeLong's test for these values (all p > 0.10). DCISM was significantly associated with high nuclear grade, comedo type, high axillary lymph node (ALN) metastasis, and high Ki-67 positivity compared to DCIS (all p < 0.05). ConclusionThe tumor size (CSAmax), enhancement index (%RS), and internal enhancement pattern (IEP) were highly indicative of DCISM. DCISM tends to express more aggressive pathological features, such as high nuclear grade, comedo-type necrosis, ALN metastasis, and Ki-67 overexpression. As with MRI, CEM has the capability to help predict when DCISM is accompanying DCIS.

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

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

Imaging the dense breast.

The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.

Exploring the Potential of a Novel Iodine-Based Material as an Alternative Contrast Agent in X-ray Imaging Studies.

Contrast-enhanced mammography is one of the new emerging imaging techniques used for detecting breast tissue lesions. Optimization of imaging protocols and reconstruction techniques for this modality, however, requires the involvement of physical phantoms. Their development is related to the use of radiocontrast agents. This study assesses the X-ray properties of a novel contrast material in clinical settings. This material is intended for experimental use with physical phantoms, offering an alternative to commonly available radiocontrast agents. The water-soluble sodium salt of the newly synthesized diiodine-substituted natural eudesmic acid, Sodium 2,6-DiIodo-3,4,5-TriMethoxyBenzoate [NaDITMB], has been investigated with respect to one of the most commonly applied radiocontrast medium in medical practice-Omnipaque®. For this purpose, simulation and experimental studies were carried out with a computational phantom and a physical counterpart, respectively. Synthetic and experimental X-ray images were subsequently produced under varying beam kilovoltage peaks (kVps), and the proposed contrast material was evaluated. Simulation results revealed equivalent absorptions between the two simulated radiocontrast agents. Experimental findings supported these simulations, showing a maximum deviation of 3.7% between the image gray values of contrast materials for NaDITMB and Omnipaque solutions for a 46 kVp X-ray beam. Higher kVp X-ray beams show even smaller deviations in the mean grey values of the imaged contrast agents, with the NaDITMB solution demonstrating less than a 2% deviation compared to Omnipaque. The proposed contrast agent is a suitable candidate for use in experimental work related to contrast-enhanced imaging by utilizing phantoms. It boasts the advantages of easy synthesis and is recognized for its safety, ensuring a secure environment for both the experimenter and the environment.

ESR Essentials: screening for breast cancer - general recommendations by EUSOBI.

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a "one-size-fits-all" approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. KEY POINTS: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. KEY RECOMMENDATIONS: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35-40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians.

Lesion attention guided neural network for contrast-enhanced mammography-based biomarker status prediction in breast cancer

Background and objectiveAccurate identification of molecular biomarker statuses is crucial in cancer diagnosis, treatment, and prognosis. Studies have demonstrated that medical images could be utilized for non-invasive prediction of biomarker statues. The biomarker status-associated features extracted from medical images are essential in developing medical image-based non-invasive prediction models. Contrast-enhanced mammography (CEM) is a promising imaging technique for breast cancer diagnosis. This study aims to develop a neural network-based method to extract biomarker-related image features from CEM images and evaluate the potential of CEM in non-invasive biomarker status prediction. MethodsAn end-to-end learning convolutional neural network with the whole breast images as inputs was proposed to extract CEM features for biomarker status prediction in breast cancer. The network focused on lesion regions and flexibly extracted image features from lesion and peri‑tumor regions by employing supervised learning with a smooth L1-based consistency constraint. An image-level weakly supervised segmentation network based on Vision Transformer with cross attention to contrast images of breasts with lesions against the contralateral breast images was developed for automatic lesion segmentation. Finally, prediction models were developed following further selection of significant features and the implementation of random forest-based classification. Results were reported using the area under the curve (AUC), accuracy, sensitivity, and specificity. ResultsA dataset from 1203 breast cancer patients was utilized to develop and evaluate the proposed method. Compared to the method without lesion attention and with only lesion regions as inputs, the proposed method performed better at biomarker status prediction. Specifically, it achieved an AUC of 0.71 (95 % confidence interval [CI]: 0.65, 0.77) for Ki-67 and 0.73 (95 % CI: 0.65, 0.80) for human epidermal growth factor receptor 2 (HER2). ConclusionsA lesion attention-guided neural network was proposed in this work to extract CEM image features for biomarker status prediction in breast cancer. The promising results demonstrated the potential of CEM in non-invasively predicting the biomarker statuses in breast cancer.

Phantom-based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM

BackgroundAutomatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers.MethodsUsing phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers’ strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR).ResultsFor all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM.ConclusionsThe study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers’ strategies, with potential implications in radiation dose and image quality in clinical settings.Relevance statementThe study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities.Key points• AEC plays a crucial role in DM, DBT, and CEM.• AEC determines the “optimal” exposure conditions needed to achieve specific image quality.• The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies.Graphical

Diagnostic value of qualitative and quantitative enhancement parameters on contrast-enhanced mammography.

To determine whether qualitative and quantitative enhancement parameters obtained from contrast-enhanced mammography (CEM) can be used in predicting malignancy. After review board approval, consecutive 136 suspicious lesions with definite diagnosis were retrospectively analyzed on CEM. Acquisition was routinely started with craniocaudal view and ended with mediolateral oblique view of the affected breast. Lesion conspicuity (low, moderate, high), internal enhancement pattern (homogeneous, heterogeneous, rim), contrast-to-noise ratio (CNR), percentage of signal difference (PSD) and relative enhancement from early to late view were analyzed. PSD and relative enhancements were used to determine patterns of descending, steady or ascending enhancements. Receiver operating characteristic analysis, Cohen's kappa statistics and Spearman correlation tests were used. There were 29 benign and 107 malignant lesions. 64% of the malignant lesions exhibited high conspicuity compared to 14% of the benign lesions (P < 0.001). CNR values were higher in malignant lesions compared to benign ones (P ≤ 0.004). CNR from early view yielded 82% sensitivity, 72% specificity and PSD yielded 79% sensitivity, 65% specificity. Descending pattern and rim enhancement observed in 44% and 21% of breast cancers, respectively, and both provided 96% positive predictive value for malignancy. Diagnostic accuracy of quantitative parameters was higher than that of qualitative parameters. High CNR, rim enhancement, and descending pattern were features commonly seen in malignant lesions, while low CNR, homogeneous enhancement, and ascending pattern were commonly seen in benign lesions.

Assessing the malignancy of suspicious breast microcalcifications: the role of contrast enhanced mammography.

To assess the role of contrast-enhanced mammography (CEM) in predicting the malignancy of breast calcifications. We retrospectively evaluated patients with suspicious calcifications (BIRADS 4) who underwent CEM and stereotactic vacuum-assisted biopsy (VAB) at our institution. We assessed the sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) of CEM in predicting malignancy of microcalcifications with a 95% confidence interval; we performed an overall analysis and a subgroup analysis stratified into group A-low risk (BIRADS 4a) and group B-medium/high risk (BIRADS 4b-4c). We then evaluated the correlation between enhancement and tumour proliferation index (Ki-67) for all malignant lesions. Data from 182 patients with 184 lesions were collected. Overall the SE of CEM in predicting the malignancy of microcalcifications was 0.70, SP was 0.85, the PPV was 0.82, the NPV was 0.76 and AUC was 0.78. SE in group A was 0.89, SP was 0.89, PPV was 0.57, NPV was 0.98 and AUC was 0.75. SE in group B was 0.68, SP was 0.80, PPV was 0.87, NPV was 0.57 and AUC was 0.75. Among malignant microcalcifications that showed enhancement (N = 52), 61.5% had Ki-67 ≥ 20% and 38.5% had low Ki-67 values. Among the lesions that did not show enhancement (N = 22), 90.9% had Ki-67 < 20% and 9.1% showed high Ki-67 values 20%. The absence of enhancement can be used as an indicative parameter for the absence of disease in cases of low-suspicious microcalcifications, but not in intermediate-high suspicious ones for which biopsy remains mandatory and can be used to distinguish indolent lesions from more aggressive neoplasms, with consequent reduction of overdiagnosis and overtreatment.

Association of breast cancer with quantitative mammographic density measures for women receiving contrast-enhanced mammography.

Women with high mammographic density have an increased risk of breast cancer. They may be offered contrast-enhanced mammography to improve breast cancer screening performance. Using a cohort of women receiving contrast-enhanced mammography, we evaluated whether conventional and modified mammographic density measures were associated with breast cancer. Sixty-six patients with newly diagnosed unilateral breast cancer were frequency matched on the basis of age to 133 cancer-free control individuals. On low-energy craniocaudal contrast-enhanced mammograms (equivalent to standard mammograms), we measured quantitative mammographic density using CUMULUS software at the conventional intensity threshold ("Cumulus") and higher-than-conventional thresholds ("Altocumulus," "Cirrocumulus"). The measures were standardized to enable estimation of odds ratio per adjusted standard deviation (OPERA). In multivariable logistic regression of case-control status, only the highest-intensity measure (Cirrocumulus) was statistically significantly associated with breast cancer (OPERA = 1.40, 95% confidence interval = 1.04 to 1.89). Conventional Cumulus did not contribute to model fit. For women receiving contrast-enhanced mammography, Cirrocumulus mammographic density may better predict breast cancer than conventional quantitative mammographic density.

Addition of Contrast-enhanced Mammography to Tomosynthesis for Breast Cancer Detection in Women with a Personal History of Breast Cancer: Prospective TOCEM Trial Interim Analysis.

Background Digital breast tomosynthesis (DBT) is often inadequate for screening women with a personal history of breast cancer (PHBC). The ongoing prospective Tomosynthesis or Contrast-Enhanced Mammography, or TOCEM, trial includes three annual screenings with both DBT and contrast-enhanced mammography (CEM). Purpose To perform interim assessment of cancer yield, stage, and recall rate when CEM is added to DBT in women with PHBC. Materials and Methods From October 2019 to December 2022, two radiologists interpreted both examinations: Observer 1 reviewed DBT first and then CEM, and observer 2 reviewed CEM first and then DBT. Effects of adding CEM to DBT on incremental cancer detection rate (ICDR), cancer type and node status, recall rate, and other performance characteristics of the primary radiologist decisions were assessed. Results Among the participants (mean age at entry, 63.6 years ± 9.6 [SD]), 1273, 819, and 227 women with PHBC completed year 1, 2, and 3 screening, respectively. For observer 1, year 1 cancer yield was 20 of 1273 (15.7 per 1000 screenings) for DBT and 29 of 1273 (22.8 per 1000 screenings; ICDR, 7.1 per 1000 screenings [95% CI: 3.2, 13.4]) for DBT plus CEM (P < .001). Year 2 plus 3 cancer yield was four of 1046 (3.8 per 1000 screenings) for DBT and eight of 1046 (7.6 per 1000 screenings; ICDR, 3.8 per 1000 screenings [95% CI: 1.0, 7.6]) for DBT plus CEM (P = .001). Year 1 recall rate for observer 1 was 103 of 1273 (8.1%) for (incidence) DBT alone and 187 of 1273 (14.7%) for DBT plus CEM (difference = 84 of 1273, 6.6% [95% CI: 5.3, 8.1]; P < .001). Year 2 plus 3 recall rate was 40 of 1046 (3.8%) for DBT and 92 of 1046 (8.8%) for DBT plus CEM (difference = 52 of 1046, 5.0% [95% CI: 3.7, 6.3]; P < .001). In 18 breasts with cancer detected only at CEM after integration of both observers, 13 (72%) cancers were invasive (median tumor size, 0.6 cm) and eight of nine (88%) with staging were N0. Among 1883 screenings with adequate reference standard, there were three interval cancers (one at the scar, two in axillae). Conclusion CEM added to DBT increased early breast cancer detection each year in women with PHBC, with an accompanying approximately 5.0%-6.6% recall rate increase. Clinical trial registration no. NCT04085510 © RSNA, 2024 Supplemental material is available for this article.

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

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

Diagnostic performance of contrast-enhanced mammography for suspicious findings in dense breasts: A systematic review and meta-analysis.

Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts. The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies. A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45). CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.