Breast imaging in focus: A bibliometric overview of visual quality, modality innovations, and diagnostic performance.

INTRODUCTION: Breast imaging plays a crucial role in the early detection and management of breast cancer, with visual quality, modality innovation and diagnostic performance being key factors in achieving accurate diagnoses and optimal patient outcomes. This paper presents a comprehensive bibliometric analysis of the literature on the three above elements focusing on breast imaging, aiming to uncover publication trends, identify influential works and authors, and highlight future research directions. METHODS: We employed a methodical bibliometric approach, making use of Scopus and Web of Science (WoS) databases for gathering literatures. We planned our search strategy, concentrating on terms linked to "breast imaging," "image quality," and "diagnostic accuracy" to ensure a systematic examination of the subject. The enhanced search functions in these databases enabled us to narrow down and improve our findings, choosing only the articles, conference papers, and book sections that are most relevant. After conducting a thorough screening process to remove duplicates and evaluate significance, we utilized ScientoPy and VOSviewer software for an in-depth bibliometric analysis. This helped to explore trends in publications, patterns of citations, and thematic groups, giving us a better understanding of how the field has changed and where it currently stands. Our approach prioritized assessing methodological quality and bias in the studies we included, guaranteeing the reliability of our findings. RESULTS: We reviewed 2984 relevant publications, revealing a consistent annual growth rate of 2.8% in breast imaging research, with the United States and Europe leading in contributions. The study found that advancements in radiological technologies and international collaboration are driving forces behind the field's expansion. Key subject areas such as 'Radiology, Nuclear Medicine, and Medical Imaging' dominated, underscoring their impact on diagnostic quality. Notable authors and institutions have been identified for their influential research, characterized by high citation metrics and significant scholarly impact. CONCLUSION: The study shows a continuous increase in research on breast imaging, considered by new technologies and teamwork defining the present time. The assessment highlights a key move towards utilizing digital imaging methods and computational analysis, affecting the improvement of future diagnostic procedures and patients' results. The study highlights the importance of continued international collaborations to tackle the new barriers in breast imaging and make the most of technological progress. IMPLICATIONS FOR PRACTICE: This study shows a focus on using interdisciplinary methods and cutting-edge technology in breast imaging to help healthcare professionals improve their performance and accuracy in diagnosis. Recognizing vital research and emerging trends should guide clinical guidelines, radiology training, and patient care plans to encourage the use of effective techniques and stimulate innovation in diagnostic approaches.

Can Hounsfield units on chest CT characterize breast nodules as cystic or solid?

We report the results of our retrospective analysis of the ability of standard chest computed tomography (CT) scans to correctly differentiate cystic from solid lesions. MModal Catalyst identified 27 women who had an ultrasound of the breast that was recommended because of a chest CT finding between January 1, 2010, and December 31, 2017. All images were reviewed by a radiologist fellowship trained in both breast imaging and cardiothoracic radiology (MS). Ultrasound characterization of lesion density as cystic or solid was considered the gold standard for this study. Analysis of CT scans was performed to identify lesions of interest corresponding to ultrasound abnormality; average, minimum, and maximum Hounsfield units (HUs) were measured. If masses had any solid component, they were considered solid. Twenty masses were solid, and 7 masses were cystic on ultrasound. Thirteen studies were performed without contrast and 14 were performed with contrast. On non-contrast studies, the average HU for cystic lesions was 19 compared to 38 HU for solid (P=0.007). On contrast studies, the average HU for cystic lesions was 16 compared to 53 HU for solid (P=0.002). Cystic lesions did not change with contrast significantly. Solid lesions enhanced with contrast; average HU 38 without contrast to 53 HU with contrast. Chest CT accurately diagnosed breast masses as cystic or solid with or without contrast.

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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Cost-Effectiveness Analysis of Digital Breast Tomosynthesis and Mammography in Breast Cancer Screening: A Markov Modeling Study.

BACKGROUND: Mammography (MG) has demonstrated its effectiveness in diminishing mortality and advanced-stage breast cancer incidences in breast screening initiatives. Notably, research has accentuated the superior diagnostic efficacy and cost-effectiveness of digital breast tomosynthesis (DBT). However, the scope of evidence validating the cost-effectiveness of DBT remains limited, prompting a requisite for more comprehensive investigation. The present study aimed to rigorously evaluate the cost-effectiveness of DBT plus MG (DBT-MG) compared to MG alone within the framework of Taiwan's National Health Insurance program. METHODS: All parameters for the Markov decision tree model, encompassing event probabilities, costs, and utilities (quality-adjusted life years, QALYs), were sourced from reputable literature, expert opinions, and official records. With 10,000 iterations, a 2-year cycle length, a 30-year time horizon, and a 2% annual discount rate, the analysis determined the incremental cost-effectiveness ratio (ICER) to compare the cost-effectiveness of the two screening methods. Probabilistic and one-way sensitivity analyses were also conducted to demonstrate the robustness of findings. RESULTS: The ICER of DBT-MG compared to MG was US$5971.5764/QALYs. At a willingness-to-pay (WTP) threshold of US$33,004 (Gross Domestic Product of Taiwan in 2021) per QALY, more than 98% of the probabilistic simulations favored adopting DBT-MG versus MG. The one-way sensitivity analysis also shows that the ICER depended heavily on recall rates, biopsy rates, and positive predictive value (PPV2). CONCLUSION: DBT-MG shows enhanced diagnostic efficacy, potentially diminishing recall costs. While exhibiting a higher biopsy rate, DBT-MG aids in the detection of early-stage breast cancers, reduces recall rates, and exhibits notably superior cost-effectiveness.

Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

Purpose. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions.Methods. Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained.Results. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases.Conclusions. Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms.

Strategies for Accessible Breast Screening for People With Intellectual Disability.

INTRODUCTION: People with intellectual disability are less likely to participate in breast screening than people without intellectual disability. They experience a range of barriers to accessing breast screening, however, there is no consensus on strategies to overcome these barriers. Our objective was to reach consensus on the strategies required for accessible breast screening for people with intellectual disability. METHODS: Fourteen experts participated in a modified on-line Delphi that used Levesque's model of health care access as the theoretical framework. At the end of each round descriptive and thematic analyses were completed. Data was then triangulated to determine if consensus was reached. RESULTS: After 3 rounds, 9 strategies were modified, 24 strategies were added and consensus was reached for 52 strategies across the 5 dimensions of access. Key areas of action related to (i) decision making and consent, (ii) accessible information, (iii) engagement of peer mentors, (iv) service navigators, and (v) equipping key stakeholders. CONCLUSIONS: The resulting strategies are the first to articulate how to make breast screening accessible and can be used to inform health policy and quality improvement practices.

Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review.

The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes.

Pseudocalcifications: Radiologic-Pathologic Correlation of Postsurgical Gauze Artifacts.

Artifacts and foreign bodies can mimic microcalcifications. We report a series of 17 postsurgical women in whom mammograms showed fine linear radiodensities at the surgical bed. Vacuum-assisted biopsy histopathology of one of the lesions showed foreign bodies of different sizes with macrophage reaction. After discussion with the surgeons, we ascertained that a particular type of gauze was used that had fragmented, and we reproduced the mammographic appearance in a chicken breast. Furthermore, we showed the same pathology was reproduced in mice implanted with the gauze threads. It is important to be aware of this entity to avoid unnecessary examinations and even biopsy. The presence of foreign body linear gauze fragments at the surgical site can pose challenges in the mammographic follow-up of these patients.

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

RATIONALE AND OBJECTIVES: The 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 METHODS: Forty-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. RESULTS: In 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). CONCLUSION: The 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.

Screening the Implant-Augmented Breast with Digital Breast Tomosynthesis: Is Tomosynthesis Necessary for Non-implant-Displaced Views?

OBJECTIVE: To determine cancer visualization utility and radiation dose for non-implant-displaced (ID) views using standard protocol with digital breast tomosynthesis (DBT) vs alternative protocol with 2D only when screening women with implant augmentation. METHODS: This retrospective cohort study identified women with implants who underwent screening DBT examinations that had abnormal findings from July 28, 2014, to December 31, 2021. Three fellowship-trained breast radiologists independently reviewed examinations retrospectively to determine if the initially identified abnormalities could be visualized on standard protocol (DBT with synthesized 2D (S2D) for ID and non-ID views) and alternate protocol (DBT with S2D for ID and only the S2D images for non-ID views). Estimated exam average glandular dose (AGD) and associations between cancer visualization with patient and implant characteristics for both protocols were evaluated. RESULTS: The study included 195 patients (mean age 55 years ± 10) with 223 abnormal findings. Subsequent biopsy was performed for 86 abnormalities: 59 (69%) benign, 8 (9%) high risk, and 19 (22%) malignant. There was no significant difference in malignancy visualization rate between standard (19/223, 8.5%) and alternate (18/223, 8.1%) protocols (P =.92), but inclusion of the DBT for non-ID views found one additional malignancy. Total examination AGD using standard protocol (21.9 mGy ± 5.0) was significantly higher than it would be for estimated alternate protocol (12.6 mGy ± 5.0, P <.001). This remained true when stratified by breast thickness: 6.0-7.9 cm, 8.0-9.9 cm, >10.0 cm (all P <.001). CONCLUSION: The inclusion of DBT for non-ID views did not significantly increase the cancer visualization rate but did significantly increase overall examination AGD.

Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge.

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical Schools' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location.

Sonographic Evaluation of Nigerian Women with Mammographic Focal Asymmetric Density.

BACKGROUND AND OBJECTIVE: Focal asymmetric breast densities (FABD) present a diagnostic challenge concerning the need for a further histologic workup to rule out malignancy. We therefore aim to correlate ultrasonography and mammographic findings in women with FABD and evaluate the use of ultrasonography as a workup tool. METHODOLOGY: This is a retrospective study of women who underwent targeted breast sonography due to FABD with a mammogram in a private diagnostic centre in Abuja over three years (2016-2018). Demographic details, clinical indication, mammographic and ultrasonography features were documented and statistical analysis was done using SAS software version 9.3 with the statistical level of significance set at 0.05. RESULT: The age range of 44 patients was 32-69 years with a majority (79.5%) presenting for screening mammography. The predominant breast density pattern in those <60 years was heterogeneous (ACR C). FABD in mammography was noted mostly in the upper outer quadrant and retro-areolar regions (34.1 and 38.6%). Ultrasonography findings were normal breast tissue (56.8%), 4 simple cysts, 1 abscess, 4 solid masses, 2 focal fibrocystic changes, and 4 cases of duct ectasia. Twenty-nine (65.9%) of the abnormal cases were on the same side as the mammogram, while all the incongruent cases were recorded in heterogeneously dense breasts (ACR C). Final BIRADS Scores on USS showed that 41(93.2%) of mammographic FABD had normal and benign findings while only 2(4.6%) had sonographic features of malignancy. CONCLUSION: Breast ultrasonography allows for optimal lesion characterization and is a veritable tool in the workup of patients with focal asymmetric breast densities with the majority presenting as normal breast tissue and benign pathologies.

CONTEXTE ET OBJECTIF: Les densités asymétriques mammographiques focales mammographiques, FABD présentent un défi diagnostique en ce qui concerne la nécessité d'un examen histologique supplémentaire pour exclure une tumeur maligne. Nous visons donc à corréler les résultats échographiques et mammographiques chez les femmes ayant une densité mammaire focale asymétrique et à établir la nécessité d'un bilan plus approfondi. METHODOLOGIE: Une étude rétrospective de 44 femmes ayant subi une échographie ciblée du sein en raison de FABD à la mammographie dans un centre de diagnostic privé à Abuja sur trois ans (2016-2018) Les détails démographiques, les présentations cliniques, les caractéristiques mammographiques et échographiques ont été documentés et analysés statistiquement fait à l'aide du logiciel SAS version 9.3 avec un niveau de signification statistique fixé à 0,05. RESULTAT: La tranche d'âge des patients était de 32 à 69 ans (SD 1), la majorité (79,5%) se présentant pour une mammographie de dépistage. Le schéma de densité mammaire prédominant chez les moins de 60 ans était hétérogène (ACR C). FABD en mammographie a presque la même distribution dans le quadrant externe supérieur et les régions rétroaréolaires (38,4 vs 36,8%). Les résultats échographiques étaient: tissu mammaire normal (65,9%), 4 kystes simples, 1 kyste complexe, 4 masses solides, 2 fibrokystiques focales et 4 cas d'ectasie canalaire.29 (65,9%) des cas anormaux étaient du même côté que la mammographie, alors que tous les cas incongruents ont été enregistrés dans des seins denses de manière hétérogène (ACR C). Les scores finaux BIRADS sur USS ont montré que 41 (93,2%) des FABD mammographiques avaient des résultats normaux et bénins, tandis que seulement 2 (4,6%) avaient des caractéristiques échographiques de malignité. CONCLUSION: L'échographie mammaire permet une caractérisation optimale des lésions et constitue un véritable outil dans le bilan des patientes présentant des densités mammaires asymétriques focales dont la majorité se présente comme un tissu mammaire normal et des pathologies bénignes. MOTS CLES: Sein, Asymétrie focale, Échographie, Mammographie.

A Review of Artificial Intelligence in Breast Imaging.

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Digital Breast Tomosynthesis for Upgraded BIRADS Scoring towards the True Pathology of Lesions Detected by Contrast-Enhanced Mammography.

OBJECTIVE: To determine the added value of digital breast tomosynthesis (DBT) in the assessment of lesions detected by contrast-enhanced mammography (CEM). MATERIAL AND METHODS: A retrospective study was conducted in a tertiary university medical center. All CEM studies including DBT performed between January 2016 and December 2020 were included. Lesions were categorized and scored by four dedicated breast radiologists according to the recent CEM and DBT supplements to the Breast Imaging Reporting and Data System (BIRADS) lexicon. Changes in the BIRADS score of CEM-detected lesions with the addition of DBT were evaluated according to the pathology results and 1-year follow-up imaging study. RESULTS: BIRADS scores of CEM-detected lesions were upgraded toward the lesion's pathology with the addition of DBT (p > 0.0001), overall and for each reader. The difference in BIRADS scores before and after the addition of DBT was more significant for readers who were less experienced. The reason for changes in the BIRADS score was better lesion margin visibility. The main BIRADS descriptors applied in the malignant lesions were spiculations, calcifications, architectural distortion, and sharp or obscured margins. CONCLUSIONS: The addition of DBT to CEM provides valuable information on the enhancing lesion, leading to a more accurate BIRADS score.

Breast arterial calcification is associated with sarcopenia in peri- and post-menopausal women.

PURPOSE: Breast arterial calcification (BAC) refers to medial calcium deposition in breast arteries and is detectable via mammography. Sarcopenia, which is characterised by low skeletal muscle mass and quality, is associated with several serious clinical conditions, increased morbidity, and mortality. Both BAC and sarcopenia share common pathologic pathways, including ageing, diabetes, and chronic kidney disease. Therefore, this study evaluated the relationship between BAC and sarcopenia as a potential indicator of sarcopenia. METHODS: This study involved women aged >40. BAC was evaluated using digital mammography and was defined as vascular calcification. Sarcopenia was assessed using abdominal computed tomography. The cross-sectional skeletal mass area was measured at the third lumbar vertebra level. The skeletal mass index was obtained by dividing the skeletal mass area by height in square meters(m2). Sarcopenia was defined as a skeletal mass index of ≤38.5 cm2/m2. A multivariable model was used to evaluate the relationship between BAC and sarcopenia. RESULTS: The study involved 240 participants. Of these, 36 (15 %) were patients with BAC and 204 (85 %) were without BAC. Sarcopenia was significantly higher among the patients with BAC than in those without BAC (72.2 % vs 17.2 %, P < 0.001). The multivariable model revealed that BAC and age were independently associated with sarcopenia (odds ratio[OR]: 7.719, 95 % confidence interval[CI]: 3.201-18.614, and P < 0.001 for BAC and OR: 1.039, 95 % CI: 1.007-1.073, P = 0.01 for age). CONCLUSION: BAC is independently associated with sarcopenia. BAC might be used as an indicator of sarcopenia on screening mammography.

Disparities in Mammography Screening: Analyzing Barriers to Access Using Individual Patient Perspectives and the Health Belief Model.

OBJECTIVE: The objective of our study is to explore Nepali women's beliefs about access to mammography screening, and motivations to get screened or not. This work was intended to be hypothesis generating for subsequent quantitative analysis and to inform policy and decision-making to improve access. METHODS: We conducted structured qualitative interviews among nine Nepali women in the Northeast of the United States receiving care at a local community health center and among nine white women receiving mammography care at a large academic medical center in the Northeast. We analyzed the transcripts using a mixed deductive (content analysis) and inductive (grounded theory) approach. Deductive codes were generated from the Health Belief Model which states that a person's belief in the real threat of a disease with their belief in the effectiveness of the recommended health service or behavior or action will predict the likelihood the person will adopt the behavior. We compared and contrasted qualitative results from both groups. RESULTS: We found that eligible Nepali women who had not received mammography screening had no knowledge of its availability and its importance. Primary care physicians emerged as a critical link in addressing this disparity: trust was found to be high among Nepali women with their established primary care provider. CONCLUSION: The findings of this study suggest that the role of primary care practitioners in conversations around the importance and eligibility for mammography screening is of critical importance, especially for underserved groups with limited health knowledge of screening opportunities and potential health benefits. Follow-up research should focus on primary care practices.

In this study, we interviewed Nepali women in a small, rural state in in the Northeast of the United States who are eligible for breast cancer screening yet do not seek it to better understand their motivations f. We also interviewed women who did get mammography screening to understand their motivations. We found that eligible Nepali women who had not received mammography screening had no knowledge of its availability and its importance. Primary care physicians emerged as a critical link in addressing this disparity: trust was found to be high among Nepali women with their established primary care provider. The findings of this study suggest that the role of primary care practitioners in conversations around the importance and eligibility for mammography screening is of critical importance.

Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model.

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets-both private and public-attest to the method's robustness, flexibility, and generalization capabilities.

The incidence of breast cancer in Egyptian females in correlation to different mammographic ACR densities.

INTRODUCTION: The density of breast tissue, radiologically referred to as fibroglandular mammary tissue, was found to be a predisposing factor for breast cancer (BC). However, the stated degree of elevated BC risk varies widely in the literature.

Artificial Intelligence for Breast Cancer Risk Assessment.

Breast cancer risk prediction models based on common clinical risk factors are used to identify women eligible for high-risk screening and prevention. Unfortunately, these models have only modest discriminatory accuracy with disparities in performance in underrepresented race and ethnicity groups. The field of artificial intelligence (AI) and deep learning are rapidly advancing the field of breast cancer risk prediction with the development of mammography-based AI breast cancer risk models. Early studies suggest mammography-based AI risk models may perform better than traditional risk factor-based models with more equitable performance.

Interval Cancers in Understanding Screening Outcomes.

Interval breast cancers are not detected at routine screening and are diagnosed in the interval between screening examinations. A variety of factors contribute to interval cancers, including patient and tumor characteristics as well as the screening technique and frequency. The interval cancer rate is an important metric by which the effectiveness of screening may be assessed and may serve as a surrogate for mortality benefit.