Breast Imaging Research Articles

Tips and Tricks for Image-Guided Breast Biopsies: Technical Factors for Success.

Image-guided biopsy is an integral step in the diagnosis and management of suspicious image-detected breast or axillary lesions, allowing for accurate diagnosis and, if indicated, treatment planning. Tissue sampling can be performed under guidance of a full spectrum of breast imaging modalities, including stereotactic, tomosynthesis, sonographic, and MRI, each with its own set of advantages and limitations. Procedural planning, which includes consideration of technical, patient, and lesion factors, is vital for diagnostic accuracy and limitation of complications. The purpose of this paper is to review and provide guidance for breast imaging radiologists in selecting the best procedural approach for the individual patient to ensure accurate diagnosis and optimal patient outcomes. Common patient and lesion factors that may affect successful sampling and contribute to postbiopsy complications are reviewed and include obesity, limited patient mobility, patient motion, patients prone to vasovagal reactions, history of anticoagulation, and lesion location, such as proximity to vital structures or breast implant.

Abstract A027: Ethnic variations in breast cancer risk factors distribution in Guam, HIPIMR study

Abstract Background: Guam has significant ethnic disparities in breast cancer incidence, with rates varying from 46.7 per 100,000 among Asian women to 137.5 per 100,000 among Micronesian women. Incidence rates for Filipino, CHamoru (indigenous people), and White women are 59.8, 94.0, and 121.9 per 100,000, respectively (2013-2017). The Hawai‘i and Pacific Island Mammography Registry (HIPIMR), a collaborative effort between the University of Hawai‘i Cancer Center and the University of Guam Cancer Research Center, collects health and breast imaging data to enhance early cancer detection tailored to the unique ethnic profiles of women in Hawai‘i and Guam. This study aims to identify the distribution of breast cancer risk factors among different ethnic groups in women undergoing screening in Guam. Differences in the prevalence of risk factors may explain variations in incidence patterns. Methods: This study included 1,413 women undergoing mammography screening at FHP Health Center in Guam, from November 2023 to May 2024. The risk factors were classified as follows: BMI (≥25), age of menarche (≤13), age of first pregnancy (≥30), number of live births (≤1), age of menopause (≥55), and family history of first-degree relatives. The robust Poisson method was used to estimate prevalence ratios (PR) of risk factors among ethnic groups, adjusted for age and education. Asians were used as the reference group due to their lowest incidence rates. Furthermore, the total risk score was calculated by summing all the risk factors. ANOVA was used to test the differences in average risk scores among different ethnic groups. Results: All ethnic groups had significantly higher prevalence of being overweight or obese compared to Asian women. The prevalence ratios were highest for Micronesians (PR=1.73, 95% CI: 1.40, 2.13) and CHamoru (PR=1.55, 95% CI: 1.27, 1.90). CHamoru women had the highest rates of breast cancer family history (PR=1.74, 95% CI: 1.19, 2.54). However, CHamoru women had a lower prevalence of having a small number of live births (PR=0.39, 95% CI: 0.24, 0.65) and an older age at first birth (PR=0.83, 95% CI: 0.71, 0.96) when compared to Asian women. There was a significant difference in average score among ethnic groups (P<.001). CHamoru women had significantly higher score (2.35) than Filipino (2.08) and Asian (2.00) women. Conclusion: Our study reveals disparities in risk factors across ethnic groups, consistent with the Guam Cancer Registry's reports of high breast cancer rates among Micronesian and CHamoru women. This project is ongoing, with the aim of linking our data with the Guam Cancer Registry. This linkage will enable us to identify breast cancer cases among participants and compare risk factors across different ethnic groups for women with and without breast cancer. Acknowledgement: This research was funded by the National Cancer Institute: The University of Guam/University of Hawaii Cancer Center Partnership to Advance Cancer Health Equity, Grant U54CA143728 (University of Guam, YCP)/Grant U54CA143727 (University of Hawaii Cancer Center). Citation Format: Gian Paras, Su Bin Jin, Rose Grino, Cabrini Aguon, John Shepherd, Grazyna Badowski. Ethnic variations in breast cancer risk factors distribution in Guam, HIPIMR study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A027.

Abstract A128: Adherence to mammography screening guidelines by family history of breast cancer in Guam: Preliminary results of HIPIMR study

Abstract Background: In Guam, breast cancer is the most common cancer affecting women, accounting for approximately 34% of all cancer cases. The risk of developing breast cancer is higher for women with a first-degree relative diagnosed with the disease (FHBC). The American Cancer Society recommends annual mammograms for women aged 45-54, with the option to start screening at 40-44. For women aged 55 and older, biennial or annual screenings are advised. Our study aims to investigate the relationship between having a first-degree relative with breast cancer (FHBC) and adherence to these screening guidelines. Methods: This research utilizes preliminary data from the Hawai‘i and Pacific Island Mammography Registry (HIPIMR), a collaborative effort between the University of Hawai‘i Cancer Center and the University of Guam Cancer Research Center, which collects health and breast imaging data in Hawai‘i and Guam. Logistic regression was used to assess the association between having a first-degree relative with breast cancer (FHBC) and adherence to breast cancer screening guidelines. The analysis was conducted across different ethnic groups and adjusted for education and age. Results: The study included 1,017 women who filled out a risk factor questionnaire, including questions about FHBC, before undergoing screening mammography at a clinic in Guam from November 2023 to May 2024. Among the participants, almost 40% were CHamoru, 41% Filipino, 5% Micronesian, 7.4% Asian, and 4.4% White. Of the 1,017 participants, 18.3% reported having a FHBC, and 37.1% did not meet the screening guidelines. Women with FHBC were more likely to meet screening recommendations than those without FHBC (OR=1.63, 95% CI: 1.12, 2.38, adjusted for age and education). Micronesian women (OR=0.54, 95% CI: 0.30, 0.97) and Asian women (OR=0.50, 95% CI: 0.31, 0.82) were less likely to follow screening guidelines compared to Filipino women, after adjusting for age and education. Conclusion: Women with a FHBC were more likely to follow screening guidelines. However, a significant percentage of women in Guam do not adhere to these recommendations, particularly Micronesian women, who also have the highest rates of breast cancer in Guam. Therefore, more outreach and education efforts are needed to improve breast cancer screening adherence in this population. Acknowledgement: This research was funded by the National Cancer Institute: The University of Guam/University of Hawaii Cancer Center Partnership to Advance Cancer Health Equity, Grant U54CA143728 (University of Guam)/Grant U54CA143727 (University of Hawaii Cancer Center). Citation Format: Reina Evangelista, Su Bin Jin, John Shepherd, Grazyna Badowski. Adherence to mammography screening guidelines by family history of breast cancer in Guam: Preliminary results of HIPIMR study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A128.

Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence.

Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects.

A Complete Appraisal of Polymeric Micelle-Based Nanocarrier for Targeting Breast Cancer: Recent Strategies and Advancements

Aim/Objective: Polymeric micelle-based drug delivery systems have emerged as a beacon of hope in the challenging landscape of breast cancer therapy. This review aimed to explore the recent advancements and promising developments in the field, offering an in-depth analysis of their potential and challenges. The significance of this study lies in providing a comprehensive examination of the current advances in polymeric micelles technology, which have the potential to improve the efficiency and specificity of breast cancer therapy while minimizing adverse events. Methods: Polymeric micelles offer a revolutionary therapy by encapsulating therapeutic agents, allowing for controlled drug release and the precise targeting of malignant cells, while lowering systemic toxicity. Recent advances have focused on enhancing targeting strategies, including active and passive targeting, to increase the specificity of polymeric micelle-based therapies. A systematic review of literature was carried out using databases, like PubMed, Scopus, and Web of Science, which included papers published between 2010 and 2023. The selection criteria of the literature focused on the drug-loading capabilities and targeting strategies of the polymeric micelles, which could be advantageous for breast cancer treatment. Results: Clinical drug approaches leverage the versatility of micelles to co-deliver multiple agents, addressing the multifaceted nature of breast tumors and combating drug resistance. Additionally, polymeric micelles are finding applications in breast cancer diagnostics and imaging, enabling early detection and precise monitoring. The journey of polymeric micelle-based breast cancer therapy extends to formulation and engineering, preclinical and clinical studies, and confronts regulatory and commercialization challenges. However, the horizon is illuminated by emerging technologies, like personalized medicine, biomarker-driven targeting, and integration with novel therapies, promising a future where breast cancer treatment is tailored to individual patients and marked by improved efficacy and reduced side effects. Conclusion: In conclusion, this review underscores the transformative potential of polymeric micelle- based delivery systems in breast cancer therapy.

Can the Newer Model of Breast-Specific Positron Emission Tomography Reduce the "Blind Area"?

Objectives: Breast-specific positron emission tomography (PET) provides higher sensitivity and spatial resolution than whole-body PET/CT, but it has a blind area. Mammary glands near the chest wall sometimes present outside the field of view (FOV). A newer, dedicated breast PET (dbPET) model has a cylindrical detector with a larger diameter than previous models, so it is expected to eliminate or reduce blind areas. This study aimed to compare breast images acquired on the new dbPET model with images acquired on an older dbPET model to evaluate blind area reduction. Methods: The nipple-to-chest wall distance (mm), maximum breast cross-sectional area at the FOV edge (cm2) on the dbPET transverse images of the scanners, and the effects of patient age and body mass index (BMI) were compared. Results: There was no significant difference in the nipple-to-chest wall distance between the models (p = 0.223). The maximum breast cross-sectional area at the FOV edge was significantly larger on the newer model's images (p < 0.001). There was no significant correlation between breast size and the rate of change in both parameters. Conclusions: The new ring-type dbPET scanners with larger diameter detectors did not reduce the blind area observed on older dbPET scanners.

Artificial Intelligence in Breast Imaging Daily Clinical Practice: Counterpoint-Proceed With Caution.

Artificial Intelligence in Breast Imaging Daily Clinical Practice: Counterpoint-Proceed With Caution.

Energy curve based enhanced smell agent optimizer for optimal multilevel threshold selection of thermographic breast image segmentation

Multilevel thresholding image segmentation will subdivide an image into several meaningful regions or objects, which makes the image more informative and easier to analyze. Optimal multilevel thresholding approaches are extensively used for segmentation because they are easy to implement and offer low computational cost. Multilevel thresholding image segmentation is frequently performed using popular methods such as Otsu’s between-class variance and Kapur’s entropy. Numerous researchers have used evolutionary algorithms to identify the best multilevel thresholds based on the above approaches using histogram. This paper uses the Energy Curve (EC) based thresholding method instead of the histogram. Chaotic Bidirectional Smell Agent Optimization with Adaptive Control Strategy (ChBSAOACS), a powerful evolutionary algorithm, is developed and employed in this paper to create and execute an effective method for multilevel thresholding segmentation of breast thermogram images based on energy curves. The proposed algorithm was tested for viability on standard breast thermogram images. All experimental data are examined quantitatively and qualitatively to verify the suggested method’s efficacy.

Artificial Intelligence in Breast Imaging Daily Clinical Practice: Point-Reshaping Breast Cancer Screening.

Artificial Intelligence in Breast Imaging Daily Clinical Practice: Point-Reshaping Breast Cancer Screening.

Usefulness of Combined Advanced Dynamic Contrast-Enhanced and Diffusion-Weighted MRI Over Ultrasonography in Differentiating Cancer From Benign Lesions in Dense Breasts.

Aim To evaluate the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in characterizing suspicious lesions in dense breasts compared to ultrasonography (USG). Materials and methods Eighty-two consecutive female patients with suspicious lesions in dense breast parenchyma showing the American College of Radiology Breast Imaging Reporting And Data System (ACR BI-RADS) c/d composition on mammography underwent USG, where 63 lesions of 63 patients were suspicious. They underwent multiparametric MRI, followed by histopathological evaluation (HPE) of the lesions.Statistical analysis was done to calculate the sensitivity, specificity, and accuracy of USG and MRI in lesion characterization and the combined accuracy of DCE-MRI with DWI.The receiver operating characteristic (ROC) curve analysis provided the cut-off for the apparent diffusion coefficient (ADC) value. Results The sensitivity, specificity, and accuracy of USG were 91.7%, 63%, and 79.4%, respectively. Kinetic curve analysis on DCE-MRI showed a type I curve only in benign lesions. Malignant lesions predominantly showed a type III curve.The sensitivity, specificity, and accuracy of DCE-MRI were 95.8%, 78.5%, and 85.7%, respectively.The optimum cut-off ADC value was 1.05x10-3 mm2/s with sensitivity, specificity, and accuracy of 83.3 % each.The specificity and accuracy of combined DCE-MRI with DWI were 94.4% and 88.1%, respectively. Conclusion Advanced MRI, including a combination of DCE-MRI kinetics and DWI, would be more effective and accurate for lesion characterization in dense breasts and act as a superior problem-solving tool compared to USG in differentiating carcinoma from benign lesions.

Framework for Successful Integration of Health Services Research Into a Breast Imaging Career.

Health services research (HSR)is a multidisciplinary field of inquiry that examines how health care is structured, providing valuable data on health care outcomes and delivery. Over the past few decades, a shift in the U.S. health care system toward value-based care has placed a priority on health services topics. Health services research has been central to the evolution of breast imaging over this period, with increased emphasis placed on the following: (1) design of appropriate-use criteria for imaging services; (2) determination of cost-effectiveness of imaging protocols and screening regimens guiding policy; and (3) evaluation of policy related to reimbursement for diagnostic imaging and image-guided procedures. Examples of HSR topics that can be applied directly to breast imaging include evaluation of health care availability and accessibility, analysis of health care use patterns, exploration of patient preferences, assessment of technological innovation, development and implementation of clinical practice guidelines and screening strategies, and examination of health care organization and delivery models. Breast imaging radiologists who perform HSR are uniquely positioned to advocate for patients, to promote transformative health care interventions, and to influence policy changes and public health initiatives in breast imaging through analysis of health care data and translation of their research findings. In this Training and Professional Development article, we aim to provide practical approaches to explore interest in HSR and to describe a framework for successful integration of HSR into a breast imaging career.

An Analysis of the Results of Breast Imaging Reporting and Data System 4 Lesions in Tertiary Care Center in South India

An Analysis of the Results of Breast Imaging Reporting and Data System 4 Lesions in Tertiary Care Center in South India

Dedicated Breast CT: Getting Ready for Prime Time.

Dedicated breast CT is an imaging modality that provides true 3D imaging of the breast with many advantages over current conventional breast imaging modalities. The addition of intravascular contrast increases the sensitivity of breast CT substantially. As such, there are immediate potential applications in the clinical workflow. These include using breast CT to replace much of the traditional diagnostic workup when faced with indeterminate breast lesions. Contrast-enhanced breast CT may be appropriate as a supplemental screening tool for women at high risk of breast cancer, similar to breast MRI. In addition, emerging studies are demonstrating the utility of breast CT in neoadjuvant chemotherapy tumor response monitoring as well as planning for surgical treatment options. While short exam times and fully 3D imaging in a noncompressed position are advantages of this modality, limited coverage of chest wall/axilla due to prone positioning and use of ionizing radiation are drawbacks. To date, several studies have reported on the performance characteristics of this promising modality.

Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5th edition.

This study aimed to evaluate the performance of large language models (LLMs) and multimodal LLMs in interpreting the Breast Imaging Reporting and Data System (BI-RADS) categories and providing clinical management recommendations for breast radiology in text-based and visual questions. This cross-sectional observational study involved two steps. In the first step, we compared ten LLMs (namely ChatGPT 4o, ChatGPT 4, ChatGPT 3.5, Google Gemini 1.5 Pro, Google Gemini 1.0, Microsoft Copilot, Perplexity, Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Opus 200K), general radiologists, and a breast radiologist using 100 text-based multiple-choice questions (MCQs) related to the BI-RADS Atlas 5th edition. In the second step, we assessed the performance of five multimodal LLMs (ChatGPT 4o, ChatGPT 4V, Claude 3.5 Sonnet, Claude 3 Opus, and Google Gemini 1.5 Pro) in assigning BI-RADS categories and providing clinical management recommendations on 100 breast ultrasound images. The comparison of correct answers and accuracy by question types was analyzed using McNemar's and chi-squared tests. Management scores were analyzed using the Kruskal- Wallis and Wilcoxon tests. Claude 3.5 Sonnet achieved the highest accuracy in text-based MCQs (90%), followed by ChatGPT 4o (89%), outperforming all other LLMs and general radiologists (78% and 76%) (P < 0.05), except for the Claude 3 Opus models and the breast radiologist (82%) (P > 0.05). Lower-performing LLMs included Google Gemini 1.0 (61%) and ChatGPT 3.5 (60%). Performance across different categories of showed no significant variation among LLMs or radiologists (P > 0.05). For breast ultrasound images, Claude 3.5 Sonnet achieved 59% accuracy, significantly higher than other multimodal LLMs (P < 0.05). Management recommendations were evaluated using a 3-point Likert scale, with Claude 3.5 Sonnet scoring the highest (mean: 2.12 ± 0.97) (P < 0.05). Accuracy varied significantly across BI-RADS categories, except Claude 3 Opus (P < 0.05). Gemini 1.5 Pro failed to answer any BI-RADS 5 questions correctly. Similarly, ChatGPT 4V failed to answer any BI-RADS 1 questions correctly, making them the least accurate in these categories (P < 0.05). Although LLMs such as Claude 3.5 Sonnet and ChatGPT 4o show promise in text-based BI-RADS assessments, their limitations in visual diagnostics suggest they should be used cautiously and under radiologists' supervision to avoid misdiagnoses. This study demonstrates that while LLMs exhibit strong capabilities in text-based BI-RADS assessments, their visual diagnostic abilities are currently limited, necessitating further development and cautious application in clinical practice.

White Light Diffraction Phase Microscopy in Imaging of Breast and Colon Tissues.

This paper reports results obtained using white light diffraction phase microscopy (wDPM) on captured images of breast and colon tissue samples, marking a contribution to the advancement in biomedical imaging. Unlike conventional brightfield microscopy, wDPM offers the capability to capture intricate details of biological specimens with enhanced clarity and precision. It combines high resolution, enhanced contrast, and quantitative capabilities with non-invasive, label-free imaging. These features make it a useful tool for tissue imaging, providing detailed and accurate insights into tissue structure and dynamics without compromising the integrity of the samples. Our findings underscore the potential of quantitative phase imaging in histopathology, in the context of automating the process of tissue analysis and diagnosis. Of particular note are the insights gained from the reconstructed phase images, which provide physical data regarding peripheral glandular cell membranes. These observations serve to focus attention on pathologies involving the basal membrane, such as early invasive carcinoma. Through our analysis, we aim to contribute to catalyzing further advancements in tissue (breast and colon) imaging.

Idiopathic Granulomatous Mastitis: Imaging Findings and Outcomes with Nonsteroidal Treatment in a Predominantly Hispanic Population.

We describe the demographics, clinical presentation, imaging findings, and treatment response among 235 cases of biopsy-proven idiopathic granulomatous mastitis (IGM) at a single institution. An institutional review board-approved retrospective search of the breast imaging database was performed to select patients with biopsy-proven IGM between 2017 and 2022. Retrospective review evaluated clinical presentation, imaging findings with US and mammography, and treatment recommendations (antibiotics, nonsteroidal anti-inflammatory drugs [NSAIDs], warm compresses, or observation only). Response to treatment was evaluated on follow-up US. A favorable treatment response was a decrease in size or resolution of disease on follow-up imaging. Statistical analysis using Poisson regression was performed to evaluate the clinical outcomes associated with each treatment. A total of 235 patients met the selection criteria with a mean age of 38 years (18 to 68). The majority of patients were Hispanic (95%, 223/235). Of all patients, 75.3% (177/235) received treatment (consisting of 1 or any combination of antibiotics, NSAIDs, warm compresses), 24.7% (58/235) were treated with observation, 78.7% (185/235) returned for follow-up imaging, and 21.3% (50/235) were lost to follow-up. Of those with follow-up imaging, disease improvement was seen in 70.3% (102/145) of patients who received treatment compared with 72.5% (29/40) of patients treated by observation alone. Multivariate analysis further showed no difference in clinical outcomes among the treatment of unifocal, multifocal, or recurrent IGM. Nonsteroidal treatment of IGM showed no significant improvement on follow-up imaging compared to treatment with observation alone in a predominantly Hispanic patient population.

Breast cancer characterization using region-based convolutional neural network with screening and diagnostic mammogram

Background: Detection and classification of microcalcifications in breast tissues is crucial for early breast cancer diagnosis and long-term treatment. Objective: This paper aims to propose a robust model capable of detection and classification of breast cancer calcifications in digital mammogram images using Deep Convolutional Neural Networks (DCNN). Materials and methods: An expert breast radiologist annotated the 3,265 clinical mammogram images to create a comprehensive ground truth dataset comprising 2,500 annotations for malignant and benign calcifications. This dataset was utilized to train our model, a two-stage detection system incorporating a Region-based Convolutional Neural Network (RCNN) with AlexNet and support vector machines to enhance the system’s robustness. The proposed model was compared to the one-stage detection, utilizing YOLOv4 combined with the Cross-Stage Partial Darknet53 (CSPDarknet53) architecture. A separate dataset of 504 mammogram images was explicitly set aside for model testing. The efficacy of the proposed model was evaluated based on key performance metrics, including precision, recall, F1 score, and mean average precision (mAP). Results: The results showed that the proposed RCNN-2 model could automatically identify and categorize calcifications as malignant or benign, outperforming the YOLOv4 models. The RCNN-2’s overall effectiveness, as evaluated by precision, recall, F1 score, and mean average precision (mAP), achieved scores of 0.82, 0.85, 0.83, and 0.74, respectively. Conclusion: The proposed RCNN-2 model demonstrates very effective detection and classification of calcification in mammogram images, especially in high-dense breast images. The performance of the proposed model was compared to that of YOLOv4, and it can be concluded that the proposed RCNN model yields outstanding performance. The model can be a helpful tool for radiologists.

Diagnostic Accuracy and Incremental Value of Contrast-Enhanced Mammography Compared With Full Field Digital Mammography in a Tertiary Cancer Care Center.

To assess the diagnostic accuracy and incremental value of contrast-enhanced mammography (CEM) compared with full-field digital mammography (FFDM). A retrospective analysis was performed with 150 consecutive patients who underwent CEM at our institute between November 2020 and February 2021, fulfilling the inclusion criteria. The first round of analysis included a review of FFDM with an interpretation of findings as per the Breast Imaging Reporting and Data System (BIRADS) lexicon and the assignment of the BIRADS category to the detected abnormalities. After this documentation, a second round of analysis included a review of recombined subtracted images of CEM. The diagnostic accuracy of FFDM and CEM was calculated with histopathology as the gold standard. Among the 150 cases assessed, 202 lesions were detected with histopathological correlation, of which 42 were benign and 160 were malignant. The sensitivity of FFDM was 90.6% compared to 98.12% for CEM. The specificity of FFDM was 66.7% compared to 76.19% for CEM. The negative predictive value (NPV) of FFDM was low, at 65.12%; CEM showed a better NPV, at 91.43%. The positive predictive value (PPV) was almost the same, at 94.01% for CEM and 91.19% for FFDM. The area under the curve (AUC) was superior for CEM compared to that of FFDM, with a value of 0.87. FFDM had a low sensitivity, especially in dense breast parenchyma, at 88.79% and a specificity of 70%, whereas CEM showed a higher sensitivity, specificity, and NPV, measuring 99.14%, 76.67%, and 95.83%, respectively. Superior sensitivity and high NPV for CEM make it a preferable modality compared with FFDM, especially in dense breast parenchyma, where CEM overcomes the limitations of FFDM. We conclude that CEM is superior to FFDM in evaluating the extent of disease, additional satellite lesion detection, and ruling out ambiguous findings.

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.

Effects of Left Internal Mammary Artery (LIMA) Harvesting During Coronary Artery By-Pass Grafting Surgery on Benign Breast Diseases

Introduction: Coronary artery bypass surgery of the primary conduits employed in coronary artery bypass grafting surgery (CABG) is the left internal mammary artery (LIMA), which has garnered considerable attention due to its favorable characteristics and outcomes. There are not enough studies examining the relationship between benign breast diseases (BBD) and LIMA removal. This study is one of the pioneering studies investigating the effects of LIMA removal after bypass surgery in patients with benign breast diseases. This study aims to evaluate the effect of LIMA removal on benign breast diseases in female patients undergoing bypass surgery by analyzing pre- and post-operative breast imaging. Material and Methods: A total of 47 of 452 female patients who underwent bypass surgery between February 18, 2019, and December 31, 2021, were included in the study. All patients’ LIMA were harvested and evaluated, regarding any progression of benign breast diseases. Results: There was a significant difference between the Breast Imaging Reporting and Data System (BI-RADS) classes of the patients before and after CABG (p