Contrast-enhanced Mammogram Research Articles

Rare benign breast neoplasm masquerading as malignancy on anatomical and functional imaging with contrast-enhanced mammography

We discuss a unique case of a benign breast neoplasm that initially appeared to be an intracystic papillary carcinoma on digital breast tomosynthesis, ultrasound, and contrast-enhanced mammogram. However, subsequent histopathological examination (HPE) revealed it to be a benign adenomyoepithelioma, an uncommon neoplasm in the breast. Understanding this rare condition and confirming it pre-operatively through HPE can prevent unnecessary aggressive treatment.

Lesion conspicuity and contrast kinetics as predictors to differentiate benign and malignant breast lesions in contrast-enhanced mammogram

BackgroundContrast-enhanced mammography (CEM) is a recently developed, cost-effective imaging technique that offers both anatomical and functional breast imaging. Lesion conspicuity, a newly introduced lexicon in the ACR BIRADS supplementary atlas on CEM (2022), lacks sufficient data to correlate with malignancy likelihood. The feasibility of assessing contrast kinetics with CEM remains uncertain, and there is a scarcity of available data. Our research aims to address these gaps.ResultsTwo radiologists, blinded to pathological reports, independently evaluated 504 CEM enhanced breast lesions with histopathology reports, out of which 176 were benign and 328 were malignant. Subjective qualitative assessment of lesion conspicuity and contrast kinetics was done for each enhancing lesion. The lesion conspicuity was classified as low, moderate, or high. The kinetic behavior of each lesion was categorized into either persistent, plateau, or washout. The distribution of lesion conspicuity among benign and malignant lesions, respectively, was as follows: for low conspicuity, 74.4% versus 25.6%; for moderate conspicuity, 30.6% versus 69.4%; and for high conspicuity, 8.4% versus 91.6%. Regarding contrast kinetics and their distribution between benign and malignant lesions, persistent kinetics was detected in 95.6% compared to 4.4%, plateau kinetics in 43.4% versus 56.6%, and washout kinetics in 3.5% versus 96.5%. Statistically significant differences in distribution between benign and malignant lesions were observed for both lexicons (P < 0.001). The inter-observer agreement for lesion conspicuity (kappa = 0.97) and contrast kinetics (kappa = 0.92) was deemed excellent.ConclusionThe addition of lesion conspicuity and contrast kinetics as lexicons in CEM could enhance its diagnostic accuracy.

Role of contrast-enhanced mammogram as an adjunct to tomosynthesis in evaluation of circumscribed breast lesions.

We evaluated the role of contrast-enhanced mammogram (CEM) in the characterization of circumscribed lesions detected in digital breast tomosynthesis (DBT) and correlated with histopathology. A retrospective study was done on 205 circumscribed breast masses detected with DBT and for whom CEM was done before core biopsy/excision biopsy. Morphology of lesion enhancement was noted at a 2-min CEM image and depending on the enhancement pattern, they were classified as benign, malignant, and indeterminate. Indeterminate lesions were further characterized by contrast kinetics at 8 min and divided into benign and malignant lesions. The results were correlated with histopathology reports. Among the 205 lesions, 158 were benign and 47 were malignant by histopathology. All 47 malignant cases were diagnosed as malignant (sensitivity-100%) and 150 out of 158 benign lesions were diagnosed as benign by CEM (specificity-95%). Eight benign lesions were reported as malignant (false positive) by CEM. Positive predictive value was 85.4% (47/55); negative predictive value (NPV) was 100% (150/150); accuracy was 96% (197/205). The high sensitivity of CEM in our study suggests that CEM helps in the early diagnosis of benign-looking circumscribed breast malignancies. The high NPV of CEM helps to avoid unnecessary biopsies and interventions in benign lesions. This study describes the contrast enhancement pattern of benign and malignant circumscribed breast lesions and thereby helps in the diagnosis of malignancy at an early stage. CEM is a promising adjunct tool since it offers functional imaging as a supplement to anatomical imaging by DBT.

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.

The integration of artificial intelligence with contrast-enhanced mammogram in the work up of suspicious breast lesions: what do you expect?

BackgroundThe enhancement overlaps at contrast-enhanced mammogram (CEM) between benign and malignant breast abnormalities presents a high probability of false-positive lesions and subjects females’ candidate for screening and diagnostic mammograms to unnecessary biopsy and anxiety. The current work aimed to evaluate the ability of mammograms scanned by artificial intelligence (AI) to enhance the specificity of CEM and support the probability of malignancy in suspicious and malignant looking breast lesions.MethodsThe study included 1524 breast lesions. The AI algorithm applied to the initial mammograms and generated location information for lesions. AI scoring suggested the probability of malignancy ranged from 100% (definite cancers) and < 10% (definite non-cancer) and correlated with recombinant contrast enhanced images.ResultsThe malignant proved abnormalities were 1165 (76.5%), and the benign ones were 359 (26.5%). BI-RADS 4 category was assigned in 704 lesions (46.2%) divided into 400 malignant (400/704, 56.8%) and 304 benign (304/704, 43.2%). BI-RADS 5 category presented by 820 lesions (53.8%), 765 of them were malignant (765/820, 93.3%) and 55 were benign (55/820, 6.7%). The sensitivity of digital mammogram whether supported by AI (93.9%) or contrast media (94.4%) was significantly increased to 97.2% (p < 0.001) when supported by both methods. Improvement of the negative predictive value (from 80.6% and 79.6% to 89.8%, p < 0.05) and the accuracy (from 91.1 and 88.8 to 94.0%, p < 0.01) was detected.ConclusionsContrast-enhanced mammogram helps in specification of different breast lesions in view of patterns of contrast uptake and morphology descriptors, yet with some overlap. The use of artificial intelligence applied on digital mammogram reduced the interpretational variability and limited attempts of re-biopsies of suspicious looking breast lesions assessed by contrast-enhanced mammograms.

Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced Mammograms.

Background Handcrafted radiomics and deep learning (DL) models individually achieve good performance in lesion classification (benign vs malignant) on contrast-enhanced mammography (CEM) images. Purpose To develop a comprehensive machine learning tool able to fully automatically identify, segment, and classify breast lesions on the basis of CEM images in recall patients. Materials and Methods CEM images and clinical data were retrospectively collected between 2013 and 2018 for 1601 recall patients at Maastricht UMC+ and 283 patients at Gustave Roussy Institute for external validation. Lesions with a known status (malignant or benign) were delineated by a research assistant overseen by an expert breast radiologist. Preprocessed low-energy and recombined images were used to train a DL model for automatic lesion identification, segmentation, and classification. A handcrafted radiomics model was also trained to classify both human- and DL-segmented lesions. Sensitivity for identification and the area under the receiver operating characteristic curve (AUC) for classification were compared between individual and combined models at the image and patient levels. Results After the exclusion of patients without suspicious lesions, the total number of patients included in the training, test, and validation data sets were 850 (mean age, 63 years ± 8 [SD]), 212 (62 years ± 8), and 279 (55 years ± 12), respectively. In the external data set, lesion identification sensitivity was 90% and 99% at the image and patient level, respectively, and the mean Dice coefficient was 0.71 and 0.80 at the image and patient level, respectively. Using manual segmentations, the combined DL and handcrafted radiomics classification model achieved the highest AUC (0.88 [95% CI: 0.86, 0.91]) (P < .05 except compared with DL, handcrafted radiomics, and clinical features model, where P = .90). Using DL-generated segmentations, the combined DL and handcrafted radiomics model showed the highest AUC (0.95 [95% CI: 0.94, 0.96]) (P < .05). Conclusion The DL model accurately identified and delineated suspicious lesions on CEM images, and the combined output of the DL and handcrafted radiomics models achieved good diagnostic performance. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bahl and Do in this issue.

Invasive Lobular Carcinoma: A Review of Imaging Modalities with Special Focus on Pathology Concordance.

Invasive lobular cancer (ILC) is the second most common type of breast cancer. It is characterized by a unique growth pattern making it difficult to detect on conventional breast imaging. ILC can be multicentric, multifocal, and bilateral, with a high likelihood of incomplete excision after breast-conserving surgery. We reviewed the conventional as well as newly emerging imaging modalities for detecting and determining the extent of ILC- and compared the main advantages of MRI vs. contrast-enhanced mammogram (CEM). Our review of the literature finds that MRI and CEM clearly surpass conventional breast imaging in terms of sensitivity, specificity, ipsilateral and contralateral cancer detection, concordance, and estimation of tumor size for ILC. Both MRI and CEM have each been shown to enhance surgical outcomes in patients with newly diagnosed ILC that had one of these imaging modalities added to their preoperative workup.

Neoadjuvant approach in patients with early breast cancer: patient assessment, staging, and planning

Neoadjuvant treatment (NAT) has become an option in early stage (stage I-II) breast cancer (EBC). New advances in systemic and targeted therapies have increased rates of pathologic complete response increasing the number of patients undergoing NAT. Clear benefits of NAT are downstaging the tumor and the axillary nodes to de-escalate surgery and to evaluate response to treatment. Selection of patients for NAT in EBC rely in several factors that are related to patient characteristics (i.e, age and comorbidities), to tumor histology, to stage at diagnosis and to the potential changes in surgical or adjuvant treatments when NAT is administered.Imaging and histologic confirmation is performed to assess extent of disease y to confirm diagnosis. Besides mammogram and ultrasound, functional breast imaging MRI has been incorporated to better predict treatment response and residual disease. Contrast enhanced mammogram (CEM), shear wave elastography (SWE), or Dynamic Optical Breast Imaging (DOBI) are emerging techniques under investigation for assessment of response to neoadjuvant therapy as well as for predicting response. Surgical plan should be delineated after NAT taking into account baseline characteristics, tumor response and patient desire.In the COVID era, we have witnessed also the increasing use of NAT in patients who may be directed to surgery, unable to have it performed as surgery has been reserved for emergency cases only.

Contrast-Enhanced Mammogram: A New Game-Changer in Breast Imaging. Indications with Case-Based Experience

Mammograms are the mainstay of diagnostic breast imaging and cancer screening. Despite advances in technology such as Full Field Digital Mammography (FFDM) and Digital Breast Tomosynthesis (DBT), these imaging techniques are purely structural, and are unable to overcome shortcomings in mammography posed by dense breast parenchyma. Magnetic Resonance Imaging (MRI) is the most sensitive modality in breast imaging, due to its functional aspects in addition to structural imaging with this modality. The use of MRI is however constrained by cost and availability. The utilization of intravenous contrast with mammograms introduces a functional element to imaging. This greatly improves the sensitivity of the examination, approaching sensitivity levels of Magnetic Resonance Imaging (MRI) in the detection of primary breast cancer. With increased sensitivity afforded by assessment of tumor neo-vascularity, as well as its low cost, low energy imaging that is more readily available than MRI, Contrast-Enhanced Mammography (CEM) if more readily available than MRI, this imaging modality is a potential game-changer in breast imaging. In this article, we share our experience in the use of CEM, for indications previously reserved for MRI, with the literature review of these indications. In resonance with prior studies, we echo the ease of performing and reporting of CEM as well as greater patient comfort as the distinct advantages of CEM. In spite of slightly higher radiation dose and some risks related to contrast media, functional results at a significantly lower cost may change how we practice breast imaging in the future using CEM.

Can contrast-enhanced mammography replace dynamic contrast-enhanced MRI in the assessment of sonomammographic indeterminate breast lesions?

BackgroundDynamic contrast-enhanced MRI of the breast has been used for several years in the assessment of indeterminate mammographic findings. Contrast-enhanced mammography is a relatively novel imaging technique that has shown comparable sensitivity and specificity to MRI. Contrast-enhanced mammography is a relatively easy feasible study with high sensitivity and low cost. Our aim was to assess the feasibility of replacing dynamic contrast-enhanced (DCE)-MRI by contrast-enhanced mammography in the assessment of sonomammographic indeterminate lesions (BIRADS 3 and 4).ResultsThe study included 82 patients with 171 breast lesions. They all performed contrast-enhanced mammography and dynamic contrast-enhanced MRI. DCE-MRI sensitivity and NPV were significantly higher than those of contrast-enhanced mammogram (CEM). The overall accuracy of MRI was better than that of CEM; however, no statistically significant difference could be detected.ConclusionContrast-enhanced mammography and dynamic contrast-enhanced MRI improved the characterization of breast lesions. CEM showed slightly lower sensitivity and accuracy compared to MRI; however, because of being relatively easy, available, cheap, and acceptable by women, CEM can replace DC-MRI as a problem-solving tool in the characterization of indeterminate breast lesions.

Contrast Enhancement of Mammograms for Rapid Detection of Microcalcification Clusters

Introduction Breast cancer is one of the most common types of cancer among women. Early detection of breast cancer is the key to reducing the associated mortality rate. The presence of microcalcifications clusters (MCCs) is one of the earliest signs of breast cancer. Due to poor imaging contrast of mammograms and noise contamination, radiologists may overlook some diagnostic signs, specially the presence of MCCs. In order to improve cancer detection, image enhancement methods are often used to aid radiologists. In this paper, a new enhancement method was presented for the accurate and early detection of MCCs in mammograms. Materials and Methods The proposed system consisted of four main steps including: 1) image scaling;2) breast region segmentation;3) noise cancellation using a filter, which is sensitive to MCCs; and 4) contrast enhancement of mammograms using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and wavelet transform. To evaluate this method, 120 clinical mammograms were used. Results To evaluate the performance of the image enhancement algorithm, contrast improvement index (CII) was used. The proposed enhancement method in this research achieved the highest CII in comparison with other methods applied in this study. The Validity of the results was confirmed by an expert radiologist through visual inspection. Conclusion Detection of MCCs significantly improved in contrast-enhanced mammograms. The proposed method could be helpful for radiologists to easily detect MCCs; it could also decrease the number of biopsies and reduce the frequency of clinical misdiagnosis. Moreover, it could be useful prior to segmentation or classification stages.

Detection of clustered microcalcifications in small field digital mammography

The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture with a filter that is sensitive to microcalcification contrast shape. Then, we enhance the mammogram contrast by using wavelet-based sharpening algorithm. Afterwards, we present to radiologist, for visual analysis, such a contrast-enhanced mammogram with suggested positions of microcalcification clusters. We have evaluated the usefulness of the system with the help of four experienced radiologists, who found that it significantly improves the detection of microcalcifications in small field digital mammography.