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Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups. Change in convolutional neural network-based risk score was not statistically significantly different between vitamin D and placebo groups at 12 months (0.005 vs 0.002, P = .875) or at 24 months (0.020 vs 0.001, P = .563). The findings are consistent with the primary analysis of S0812, which did not demonstrate statistically significant changes in mammographic density with vitamin D supplementation compared with placebo. There is an ongoing need to evaluate biomarkers of response to novel breast cancer chemopreventive agents.

BACKGROUND: Excision is routinely recommended for atypical ductal hyperplasia (ADH) found on core biopsy given cancer upstage rates of near 20%. Identifying a cohort at low-risk for upstage may avoid low-value surgery. Objectives were to elucidate factors predictive of upstage in ADH, specifically near-complete core sampling, to potentially define a group at low upstage risk. PATIENTS AND METHODS: This retrospective, cross-sectional, multi-institutional study from 2015 to 2019 of 221 ADH lesions in 216 patients who underwent excision or active observation (≥ 12 months imaging surveillance, mean follow-up 32.6 months) evaluated clinical, radiologic, pathologic, and procedural factors for association with upstage. Radiologists prospectively examined imaging for lesional size and sampling proportion. RESULTS: Upstage occurred in 37 (16.7%) lesions, 25 (67.6%) to ductal carcinoma in situ (DCIS) and 12 (32.4%) to invasive cancer. Factors independently predictive of upstage were lesion size ≥ 10 mm (OR 5.47, 95% CI 2.03-14.77, p < 0.001), pathologic suspicion for DCIS (OR 12.29, 95% CI 3.24-46.56, p < 0.001), and calcification distribution pattern (OR 8.08, 95% CI 2.04-32.00, p = 0.003, regional; OR 19.28, 95% CI 3.47-106.97, p < 0.001, linear). Near-complete sampling was not correlated with upstage (p = 0.64). All three significant predictors were absent in 65 (29.4%) cases, with a 1.5% upstage rate. CONCLUSIONS: The upstage rate among 221 ADH lesions was 16.7%, highest in lesions ≥ 10 mm, with pathologic suspicion of DCIS, and linear/regional calcifications on mammography. Conversely, 30% of the cohort exhibited all low-risk factors, with an upstage rate < 2%, suggesting that active surveillance may be permissible in lieu of surgery.

BACKGROUND: Following a breast cancer diagnosis, it is uncertain whether womens breast density knowledge influences their willingness to undergo pre-operative imaging to detect additional cancer in their breasts. We evaluated womens breast density knowledge and their willingness to delay treatment for pre-operative testing. METHODS: We surveyed women identified in the Breast Cancer Surveillance Consortium aged ≥ 18 years, with first breast cancer diagnosed within the prior 6-18 months, who had at least one breast density measurement within the 5 years prior to their diagnosis. We assessed womens breast density knowledge and correlates of willingness to delay treatment for 6 or more weeks for pre-operative imaging via logistic regression. RESULTS: Survey participation was 28.3% (969/3,430). Seventy-two percent (469/647) of women with dense and 11% (34/322) with non-dense breasts correctly knew their density (p

The breasts undergo marked physiologic changes during lactation that can make conventional imaging evaluation with mammography and US challenging. MRI can be a valuable diagnostic aid to differentiate physiologic and benign processes from malignancy in patients who are lactating. In addition, MRI may allow more accurate delineation of disease involvement than does conventional imaging and assists in locoregional staging, screening of the contralateral breast, assessment of response to neoadjuvant chemotherapy, and surgical planning. Although the American College of Radiology recommends against patients undergoing contrast-enhanced MRI during pregnancy because of fetal safety concerns, contrast-enhanced MRI is safe during lactation. As more women delay childbearing, the incidence of pregnancy-associated breast cancer (PABC) and breast cancer in lactating women beyond the 1st year after pregnancy is increasing. Thus, MRI is increasingly being performed in lactating women for diagnostic evaluation and screening of patients at high risk. PABC is associated with a worse prognosis than that of non-PABCs, with delays in diagnosis contributing to an increased likelihood of advanced-stage disease at diagnosis. Familiarity with the MRI features of the lactating breast and the appearance of various pathologic conditions is essential to avoid diagnostic pitfalls and prevent delays in cancer diagnosis and treatment. The authors review clinical indications for breast MRI during lactation, describe characteristic features of the lactating breast at MRI, and compare MRI features of a spectrum of benign and malignant breast abnormalities. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Chikarmane in this issue.

According to the survey of World Health Organization (WHO), in 2020 there are 2.3 million women found with breast cancer and 685,000 deaths in world wide. 81% women get affected with cancer over the age of 50 at the time of detection. Breast cancer is the world's number 2 cancer and number 1 cancer in India and 66% survival rate in India is very low if compare to 90% in U.S and 90.2% in Australia. However, treatment for this cancer has possibility of 90% or more. So that, it need to be detect the cancer at very early stage to overcome the death rate. Main objective of this research to design a Breast Cancer diagnose system using image processing and deep learning which can be helpful for radiologist and physician for treating the diagnosis. Basically, Deep learning is a fast-developing fashion inside the health care enterprise and facilitates medical experts to examine records and pick out trends. And image processing plays vital role for enhancing the quality of image by removing noise which is very helpful for better abnormality classification. Now a days Convolution Neural Networks (CNNs) are very popular due to its better performance. In this work, we have used transfer learning with pre-trained VGG16 model. At initial testing stage, the model shows the over-fitting and after that performance improved. Hence we achieved better results by using this approach on DDSM and UPMC data-sets for breast cancer classification. Classifier classify the images into four classes as asymmetry, calcification, carcinoma and mass. Initially 2276 images were taken and divided into 80%-20% ratio. The accuracy achieved by this approach varied from 92% to 95%. We have also used transfer learning with VGG19 and ResNet50 for comparison and found VGG16 much powerful among them. We found, transfer learning with VGG16 giving better results on DDSM and UPMC data-sets. However, breast cancer divided into different categories according to its type, grade or stage of abnormalities, severity of cancer, aggressiveness of cancerous cells, presence/absence of gene etc. Hence classification can be done basis on other types of abnormalities. [ABSTRACT FROM AUTHOR]

Background: Taiwan implemented the Cancer Screening Quality Improvement Program (CAQIP) in 2010. The program sought to enhance mass breast cancer screening accessibility. This study aimed to examine socioeconomic disparities in outreach screening utilization pre-CAQIP (2005–2009) and post-CAQIP (2010–2014). Method: We conducted a nationwide population-based observational study in Taiwan, analyzing four population databases to evaluate socioeconomic disparities among women aged 50 to 69 years undergoing their first mammography screening pre-CAQIP. Multivariate logistic regression was used to examine changes in utilization of outreach screening pre- and post-CAQIP implementation, and to estimate the Slope Index of Inequity (SII) and Relative Index of Inequity (RII) values. Results: Utilization of outreach screening through mobile mammography units (MMUs) increased from 6.12 to 32.87% between the two periods. Following CAQIP, a higher proportion of screened women were older, less educated, and from suburban or rural areas. The SII and RII for age, income, and urbanization levels decreased post-CAQIP. However, regarding education level, SII was − 0.592 and RII was 0.392 in the pre-CAQIP period, increasing to -0.173 and 0.804 post-CAQIP, respectively. Conclusions: Our study observed that utilization of outreach screening through MMUs increased after CAQIP. The MMUs made outreach screening services more accessible in Taiwan. Expanding outreach screening services and educational programs to promote mammography uptake in local communities could help reduce the potential effect of socioeconomic disparities, and thus may enhance early detection of breast cancer. Further study could focus on the accessibility of outreach screening and breast cancer outcomes. [ABSTRACT FROM AUTHOR]

Background: Risk-stratified approaches to breast screening show promise for increasing benefits and reducing harms. But the successful implementation of such an approach will rely on public acceptability. To date, research suggests that while increased screening for women at high risk will be acceptable, any de-intensification of screening for low-risk groups may be met with less enthusiasm. We report findings from a population-based survey of women in England, approaching the age of eligibility for breast screening, to compare the acceptability of current age-based screening with two hypothetical risk-adapted approaches for women at low risk of breast cancer. Methods: An online survey of 1,579 women aged 40–49 with no personal experience of breast cancer or mammography. Participants were recruited via a market research panel, using target quotas for educational attainment and ethnic group, and were randomised to view information about (1) standard NHS age-based screening; (2) a later screening start age for low-risk women; or (3) a longer screening interval for low-risk women. Primary outcomes were cognitive, emotional, and global acceptability. ANOVAs and multiple regression were used to compare acceptability between groups and explore demographic and psychosocial factors associated with acceptability. Results: All three screening approaches were judged to be acceptable on the single-item measure of global acceptability (mean score > 3 on a 5-point scale). Scores for all three measures of acceptability were significantly lower for the risk-adapted scenarios than for age-based screening. There were no differences between the two risk-adapted scenarios. In multivariable analysis, higher breast cancer knowledge was positively associated with cognitive and emotional acceptability of screening approach. Willingness to undergo personal risk assessment was not associated with experimental group. Conclusion: We found no difference in the acceptability of later start age vs. longer screening intervals for women at low risk of breast cancer in a large sample of women who were screening naïve. Although acceptability of both risk-adapted scenarios was lower than for standard age-based screening, overall acceptability was reasonable. The positive associations between knowledge and both cognitive and emotional acceptability suggests clear and reassuring communication about the rationale for de-intensified screening may enhance acceptability. [ABSTRACT FROM AUTHOR]

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client–server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model. [ABSTRACT FROM AUTHOR]

Objectives: Insight into the aggressiveness of potential breast cancers found in screening may optimize recall decisions. Specific growth rate (SGR), measured on mammograms, may provide valuable prognostic information. This study addresses the association of SGR with prognostic factors and overall survival in patients with invasive carcinoma of no special type (NST) from a screened population. Methods: In this historic cohort study, 293 women with NST were identified from all participants in the Nijmegen screening program (2003–2007). Information on clinicopathological factors was retrieved from patient files and follow-up on vital status through municipalities. On consecutive mammograms, tumor volumes were estimated. After comparing five growth functions, SGR was calculated using the best-fitting function. Regression and multivariable survival analyses described associations between SGR and prognostic factors as well as overall survival. Results: Each one standard deviation increase in SGR was associated with an increase in the Nottingham prognostic index by 0.34 [95% confidence interval (CI): 0.21–0.46]. Each one standard deviation increase in SGR increased the odds of a tumor with an unfavorable subtype (based on histologic grade and hormone receptors; odds ratio 2.14 [95% CI: 1.45–3.15]) and increased the odds of diagnosis as an interval cancer (versus screen-detected; odds ratio 1.57 [95% CI: 1.20–2.06]). After a median of 12.4 years of follow-up, 78 deaths occurred. SGR was not associated with overall survival (hazard ratio 1.12 [95% CI: 0.87–1.43]). Conclusions: SGR may indicate prognostically relevant differences in tumor aggressiveness if serial mammograms are available. A potential association with cause-specific survival could not be determined and is of interest for future research. [ABSTRACT FROM AUTHOR]

Objective: Many population-based breast screening programmes temporarily suspended routine screening following the COVID-19 pandemic onset. This study aimed to describe screening mammography utilisation and the pattern of screen-detected breast cancer diagnoses following COVID-19-related screening disruptions in Ireland. Methods: Using anonymous aggregate data from women invited for routine screening, three time periods were examined: (1) January–December 2019, (2) January–December 2020, and (3) January–December 2021. Descriptive statistics were conducted and comparisons between groups were performed using chi-square tests. Results: In 2020, screening mammography capacity fell by 67.1% compared to 2019; recovering to 75% of mammograms performed in 2019, during 2021. Compared to 2019, for screen-detected invasive breast cancers, a reduction in Grade 1 (14.2% vs. 17.2%) and Grade 2 tumours (53.4% vs. 58.0%) and an increase in Grade 3 tumours (32.4% vs. 24.8%) was observed in 2020 (p = 0.03); whereas an increase in Grade 2 tumours (63.3% vs. 58.0%) and a reduction in Grade 3 tumours (19.6% vs. 24.8%) was found in 2021 (p = 0.02). No changes in oestrogen receptor-positive or nodal-positive diagnoses were observed; however the proportion of oestrogen/progesterone receptor-positive breast cancers significantly increased in 2020 (76.2%; p < 0.01) and 2021 (78.7%; p < 0.001) compared to 2019 (67.8%). Conclusion: These findings demonstrate signs of a grade change for screen-detected invasive breast cancers early in the pandemic, with recovery evident in 2021, and without an increase in nodal positivity. Future studies are needed to determine the COVID-19 impact on long-term breast cancer outcomes including mortality. [ABSTRACT FROM AUTHOR]

Objectives: To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. Methods: In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. Results: All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. Conclusion: AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. Clinical relevance statement: In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. Key Points: • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. Materials and methods: The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. Results: The AI software achieved an AUC of 89.6% (86.1–93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. Conclusion: This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. Clinical relevance statement: The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. Key Points: • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. Methods: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. Results: The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. Conclusion: The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. Clinical relevance statement: The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. Key Points: • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI. [ABSTRACT FROM AUTHOR]

The Supplemental Nutrition Assistance Program (SNAP) addresses food insecurity for low-income households, which is associated with access to care. Many US states expanded SNAP access through policies eliminating the asset test (ie, restrictions based on SNAP applicant assets) and/or broadening income eligibility. The objective of this study was to determine whether state SNAP policies were associated with the use of mammography among women eligible for breast cancer screening. Data for income-eligible women 40 to 79 years of age were obtained from the 2006 to 2019 Behavioral Risk Factor Surveillance System. Difference-in-differences analyses were conducted to compare changes in the percentage of mammography in the past year from pre- to post-SNAP policy adoption (asset test elimination or income eligibility increase) between states that and did not adopt policies expanding SNAP eligibility. In total, 171,684 and 294,647 income-eligible female respondents were included for the asset test elimination policy and income eligibility increase policy analyses, respectively. Mammography within 1 year was reported by 58.4%. Twenty-eight and 22 states adopted SNAP asset test elimination and income increase policies, respectively. Adoption of asset test elimination policies was associated with a 2.11 (95% confidence interval [CI], 0.07-4.15; P =.043) percentage point increase in mammography received within 1 year, particularly for nonmetropolitan residents (4.14 percentage points; 95% CI, 1.07-7.21 percentage points; P =.008), those with household incomes <$25,000 (2.82 percentage points; 95% CI, 0.68-4.97 percentage points; P =.01), and those residing in states in the South (3.08 percentage points; 95% CI, 0.17-5.99 percentage points; P =.038) or that did not expand Medicaid under the Patient Protection and Affordable Care Act (3.35 percentage points; 95% CI, 0.36-6.34; P =.028). There was no significant association between mammography and state-level policies broadening of SNAP income eligibility. State policies eliminating asset test requirements for SNAP eligibility were associated with increased mammography among low-income women eligible for breast cancer screening, particularly for those in the lowest income bracket or residing in nonmetropolitan areas or Medicaid nonexpansion states. [Display omitted] [ABSTRACT FROM AUTHOR]

Breast cancer screening trends of transgender and gender diverse (TGD) people remain largely unknown. This is concerning, as data are necessary to inform recommendations made by clinicians to their patients and by national guidelines to clinicians. The aim of this review is to explore the state of existing research literature and provide a summary report of current breast cancer screening rates in TGD adults. All articles were identified using Medical Subject Headings terms. Inclusion criteria were all the following: (1) documents inclusion of at least one participant who identifies as a TGD person; (2) at least one TGD participant with top surgery or currently receiving estrogen-based gender-affirming hormone therapy; (3) results that report rates of breast cancer screening or mammogram referral. Twelve articles met inclusion criteria, six cross-sectional studies and six retrospective chart reviews. Three studies conducted secondary analysis of the Behavioral Risk Factor Surveillance System national dataset, and nine articles recruited their own sample with number of TGD participants ranging from 30 to 1,822 and number of cisgender women ranging from 242 to 18,275. Three studies found lower rates of screening in transfeminine persons receiving gender-affirming care compared with cisgender women; two studies found lower rates among TGD people compared with cisgender women; and three studies found no differences between the breast cancer screening rates of TGD and cisgender participants. Limited studies recruit and report trends in breast cancer screening of TGD people. Those that do include TGD participants have mixed results, but overall TGD people had lower rates of breast cancer screening. More research is needed regarding breast cancer screening of TGD people to inform the development of protocols that ensure equitable access to preventative care. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. Methods: This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). Results: A total of 200 women (median age 56.8 years, interquartile range 50.5−65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482–0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581–0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847–20.281, p = 0.003) and 2.416 (95% CI 1.095–5.332, p = 0.029) respectively. Conclusion: BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status. [ABSTRACT FROM AUTHOR]

Purpose of Review: Ductal carcinoma in situ (DCIS) accounts for roughly 25% of all new breast cancer diagnoses. Mortality from DCIS is low and has not significantly changed despite modern, aggressive care. This review will highlight the multiple strategies which are being proposed to de-escalate care, including foregoing sentinel lymph node biopsy (SLNB). Recent Findings: Under 5% of patients undergoing SLNB for DCIS have a positive lymph node, therefore the use of SLNB has been questioned and may be able to be foregone. In addition, recent genomic assays evaluating the benefit of radiation (Oncotype DCIS®, DCISionRT®), have elucidated a group of patients who may not need radiotherapy after breast conservation for DCIS. Finally, the option of foregoing all local treatment and instead focusing on active surveillance is being evaluated in multiple randomized clinical trials including LORIS, LORD and COMET. Summary: Data regarding whether SLNB can be safely omitted and the outcomes of the growing utilization of genomic assays and "watchful waiting" clinical trials remain forthcoming. [ABSTRACT FROM AUTHOR]

Breast cancer remains a substantial public health challenge, marked by a rising prevalence. Accurate early detection is paramount for effective treatment and improved patient outcomes in breast cancer. The diversity of breast tumors and the complexity of their microenvironment present significant challenges. Establishing a reliable breast calcification and micro-calcification detection approach is an ongoing issue that researchers must continue to investigate. The goal is to develop an effective methodology that contributes to increased patient survival. Therefore, this paper introduces a novel approach for classifying breast calcifications in mammography, aiming to distinguish between benign and malignant cases. Aiming to address these challenges, we proposed our hybrid approach for breast calcification classification in mammogram images. The proposed approach starts with an image pre-processing phase that includes noise reduction and enhancement filters. Afterward, we proposed our hybrid classification architecture. It includes two branches: First, the vision transformer (ViT++) branch for contextual features. Secondly, a CNN branch based on transfer learning techniques for visual features. Using the CBIS-DDSM dataset, the application of our proposed ViT++ architecture reached the maximum accuracy of 96.12%. Further, the application of the VGG16 as a single feature extractor had a much lower accuracy of 61.96%. Meanwhile, the combination of these techniques in the same architecture improved the accuracy to 99.22%. Three different pre-trained feature extractors were applied in the CNN branch: Xception, VGG16, and RegNetX002. However, the best-obtained outcomes were from the combination of the ViT++ and the VGG16. The experimental findings indicate that the proposed strategy for breast calcification detection has the potential to surpass the performance of currently top-ranked methods, particularly in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

In medical diagnosis, mammographic imaging is mainly concerned with the breast parenchymal patterns (counterbalance of glandular tissue and fatty tissue) by which an expert radiologist can easily determine the abnormalities in the breast of cancer patients and if the interpretation of mammogram and the quality of mammogram both are well provided. Accordingly, improved mammographic view via an efficient image processing algorithm plays a significant role in the medical diagnosis of mammograms. This study introduces a sharpening method based on the modified Laplacian filter (MLF) on compute unified device architecture (CUDA) to improve the visibility and detection of pernicious lesions in a mammogram. To process considerably large mammograms on CPU, the conventional Laplacian sharpening is more time-consuming due to the processing of all pixels with serial execution manner. Although this type of image sharpening is well established for improved image quality, its effect on a larger image for use in the GPU environment has not been extensively studied. The proposed framework is successfully devised and implemented in an efficient parallel execution manner on a computing platform of graphic processing units (GPU). To examine the impact of mammograms and filter size on performance along with the comparative processing time between serial execute on CPU and parallel computing on GPU (except data transfer time). To accelerate the performance of the proposed model, we adopt both global and shared memory in GPU to realize further improvements of the execution speed. The proposed framework applies a new nonlinear filter constraints module in the sharping stage while the Laplacian filter attenuate noise sensitivity and leads to achieving visually improved results in comparison with formal sharping. The proposed framework has been extensively compared with other recent baseline methods showing to improvement in the computational cost of the image sharping approach. Experimental results establish that the two proposed sharping methods outperform the state-of-the-art methods with respect to execution speed. [ABSTRACT FROM AUTHOR]

Diagnostic reference level (DRL) for mammography for 2015 and 2020 has been published by J-RIME. More new dose studies are needed to revise the next DRL. In preparation for the next revision of the DRL for mammography, this study investigated data from the Japan Central Organization on Quality Assurance of Breast Cancer Screening on the mean average glandular dose (AGD) for institutional image accreditation in 2019–2023 and the relationship between the average at eligible institutions to date and the type of breast X-ray system. The 95th percentile values of the AGD distributions for the Computed Radiography (CR) and Flat Panel Detector (FPD) systems were 2.5 mGy and 2.0 mGy, respectively. Moreover, it is assumed that AGD is decreasing due to the spread of FPD systems, and it is expected that the further spread of FPD systems and systems with W/Rh target/filter will reduce AGD in future. [ABSTRACT FROM AUTHOR]

Breast cancer remains a prevalent and serious health issue, leading to high mortality rates among women worldwide. Early detection of breast cancer is pivotal in improving patient outcomes. This study introduces an innovative approach for breast cancer segmentation by integrating Support Vector Machine (SVM) with Harris Hawks Optimization (HHO) and Cuckoo Search (CS) algorithms. HHO, a metaheuristic optimization algorithm inspired by the cooperative behavior of Harris Hawks, is employed for effective exploration and exploitation within the search space, thereby enhancing the accuracy of image segmentation. The CS algorithm, incorporating Cuckoo Search principles, ensures a balanced exploration of local and global search spaces, contributing to a comprehensive optimization strategy. The hybrid HHO-CS SVM algorithm is instrumental in fine-tuning hyperparameters, resulting in superior performance and improved segmentation outcomes for breast cancer detection. This innovative amalgamation of techniques significantly elevates the accuracy and efficiency of breast cancer detection through image segmentation. [ABSTRACT FROM AUTHOR]

Assessing a woman's risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1–4) and a deep-learning texture risk model, with scores categorized into four quartiles (1–4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43–4.82)−4.57 (95% CI: 3.66–5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77–13.45)−16.94 (95% CI: 9.93–30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31–6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening. [ABSTRACT FROM AUTHOR]

Background Early detection of breast cancer significantly improves treatment outcomes and survival rates. While mammography is the standard screening tool, breast MRI is increasingly being used due to its higher sensitivity. This study aims to compare the diagnostic accuracy of mammography and breast MRI in detecting early breast cancer. Methods A total of 100 women aged 35-70 years (mean age: 52) participated in this study. Each participant underwent both mammography and breast MRI. Diagnostic measures calculated included true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). From these, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were derived for both diagnostic methods. Results Mammography identified 60 TP, 25 TN, 10 FP, and 5 FN cases. The sensitivity of mammography was 92.3%, specificity was 71.4%, PPV was 85.7%, NPV was 83.3%, and accuracy was 85.0%. Breast MRI identified 62 TP, 30 TN, 5 FP, and 3 FN cases. The sensitivity of breast MRI was 95.4%, specificity was 85.7%, PPV was 92.5%, NPV was 90.9%, and accuracy was 92.0%. Comparative analysis showed that breast MRI had higher sensitivity, specificity, PPV, NPV, and overall accuracy than mammography. Conclusion Breast MRI outperforms mammography in detecting early breast cancer, exhibiting superior sensitivity, specificity, PPV, NPV, and accuracy. Breast MRI identified more true positive cases and had fewer false negatives and false positives. These results suggest that breast MRI may be a more reliable diagnostic tool for early breast cancer detection. However, factors such as cost, availability, and patient-specific circumstances should be considered when choosing the diagnostic method. [ABSTRACT FROM AUTHOR]

Background: Breast cancer remains a pervasive threat to women worldwide, with increasing incidence rates necessitating effective screening strategies. Timely detection with mammography has emerged as the primary tool for mass screening. This retrospective study, which is part of the Chiraiya Project, aimed to evaluate breast lesion patients identified during opportunistic mammography screening camps in Jammu Province, India. Methods: A total of 1505 women aged 40 years and older were screened using a mobile mammographic unit over a five-year period, excluding 2020 and 2021 due to the COVID-19 pandemic. The inclusion criterion was women in the specified age group, while the exclusion criterion was women with open breast wounds, history of breast cancer or a history of breast surgery. The screening process involved comprehensive data collection using a detailed Proforma, followed by mammographic assessments conducted within strategically stationed mobile units. Radiological interpretations utilizing the BI-RADS system were performed, accompanied by meticulous documentation of patient demographics, habits, literacy, medical history, and breastfeeding practices. Participants were recruited through collaborations with NGOs, army camps, village panchayats, and urban cooperatives. Screening camps were scheduled periodically, with each camp accommodating 90 patients or fewer. Results: Among the 1505 patients, most were aged 45–50 years. The number of screenings increased yearly, peaking at 441 in 2022. The BI-RADS II was the most common finding (48.77%), indicating the presence of benign lesions, while the BI-RADS 0 (32.96%) required further evaluation. Higher-risk categories (BI-RADS III, IV, V) were less common, with BI-RADS V being the rarest. Follow-up adherence was highest in the BI-RADS III, IV, and V categories, with BI-RADS V achieving 100% follow-up. However, only 320 of 496 BI-RADS 0 patients were followed up, indicating a gap in continuity of care. The overall follow-up rate was 66.89%. Compared to urban areas, rural areas demonstrated greater screening uptake but lower follow-up rates, highlighting the need for tailored interventions to improve follow-up care access, especially in rural contexts. Conclusion: This study underscores the efficacy of a mobile mammographic unit in reaching marginalized populations. Adherence to screening protocols has emerged as a linchpin for early detection, improved prognosis, and holistic public health enhancement. Addressing misconceptions surrounding mammographic screenings, especially in rural settings, is crucial. These findings call for intensified efforts in advocacy and education to promote the benefits of breast cancer screening initiatives. Future interventions should prioritize improving access to follow-up care and addressing screening to enhance breast cancer management in Jammu Province. [ABSTRACT FROM AUTHOR]

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer. [ABSTRACT FROM AUTHOR]

Objectives: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. Methods: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). Results: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784–0.844) in the training cohort, 0.776 (95% CI, 0.727–0.825) in the testing cohort, and 0.702 (95% CI, 0.614–0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810–0.866) in the training cohort, 0.788 (95% CI, 0.741–0.835) in the testing cohort, and 0.722 (95% CI, 0.637–0.811) in the external validation cohort. Conclusions: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. Clinical relevance statement: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. Key Points: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data. [ABSTRACT FROM AUTHOR]

Objectives: To perform a survey among members of the European Society of Breast Imaging (EUSOBI) regarding the use of contrast-enhanced mammography (CEM). Methods: A panel of nine board-certified radiologists developed a 29-item online questionnaire, distributed to all EUSOBI members (inside and outside Europe) from January 25 to March 10, 2023. CEM implementation, examination protocols, reporting strategies, and current and future CEM indications were investigated. Replies were exploratively analyzed with descriptive and non-parametric statistics. Results: Among 434 respondents (74.9% from Europe), 50% (217/434) declared to use CEM, 155/217 (71.4%) seeing less than 200 CEMs per year. CEM use was associated with academic settings and high breast imaging workload (p < 0.001). The lack of CEM adoption was most commonly due to the perceived absence of a clinical need (65.0%) and the lack of resources to acquire CEM-capable systems (37.3%). CEM protocols varied widely, but most respondents (61.3%) had already adopted the 2022 ACR CEM BI-RADS® lexicon. CEM use in patients with contraindications to MRI was the most common current indication (80.6%), followed by preoperative staging (68.7%). Patients with MRI contraindications also represented the most commonly foreseen CEM indication (88.0%), followed by the work-up of inconclusive findings at non-contrast examinations (61.5%) and supplemental imaging in dense breasts (53.0%). Respondents declaring CEM use and higher CEM experience gave significantly more current (p = 0.004) and future indications (p < 0.001). Conclusions: Despite a trend towards academic high-workload settings and its prevalent use in patients with MRI contraindications, CEM use and progressive experience were associated with increased confidence in the technique. Clinical relevance statement: In this first survey on contrast-enhanced mammography (CEM) use and perspectives among the European Society of Breast Imaging (EUSOBI) members, the perceived absence of a clinical need chiefly drove the 50% CEM adoption rate. CEM adoption and progressive experience were associated with more extended current and future indications. Key Points: • Among the 434 members of the European Society of Breast Imaging who completed this survey, 50% declared to use contrast-enhanced mammography in clinical practice. • Due to the perceived absence of a clinical need, contrast-enhanced mammography (CEM) is still prevalently used as a replacement for MRI in patients with MRI contraindications. • The number of current and future CEM indications marked by respondents was associated with their degree of CEM experience. [ABSTRACT FROM AUTHOR]

Purpose: This work aims to compare the interval cancer rate and interval cancer characteristics between women screened with digital breast tomosynthesis (DBT) + digital mammography (DM) and those screened with DM alone. Methods: The interval cancer rate and interval cancer characteristics of the study population included in the Córdoba Breast Tomosynthesis Screening Trial (CBTST) were compared to a contemporary control population screened with DM. The tumour characteristics of screen-detected and interval cancers were also compared. Contingency tables were used to compare interval cancer rates. The chi-square test and Fisher's exact test were used to compare the qualitative characteristics of the cancers whereas Student's t test and the Mann–Whitney U test were used to analyse quantitative features. Results: A total of 16,068 screening exams with DBT + DM were conducted within the CBTST (mean age 57.59 ± 5.9 [SD]) between January 2015 and December 2016 (study population). In parallel, 23,787 women (mean age 58.89 ± 5.9 standard deviation [SD]) were screened with DM (control population). The interval cancer rate was lower in the study population than in the control population (15 [0.93‰; 95% confidence interval (CI): 0.73, 1.14] vs 43 [1.8‰; 95% CI: 1.58, 2.04] respectively; p = 0.045). The difference in rate was more marked in women with dense breasts (0.95‰ in the study population vs 3.17‰ in the control population; p = 0.031). Interval cancers were smaller in the study population than in the control population (p = 0.031). Conclusions: The interval cancer rate was lower in women screened with DBT + DM compared to those screened with DM alone. These differences were more pronounced in women with dense breasts. Clinical relevance statement: Women screened using tomosynthesis and digital mammography had a lower rate of interval cancer than women screened with digital mammography, with the greatest difference in the interval cancer rate observed in women with dense breasts. Key Points: • The interval cancer rate was lower in the study population (digital breast tomosynthesis [DBT] + digital mammography [DM]) than in the control population (DM). • The difference in interval cancer rates was more pronounced in women with dense breasts. • Interval cancers were smaller in the study population (DBT + DM) than in the control population (DM). [ABSTRACT FROM AUTHOR]

Objectives: The aim of our study was to examine how breast radiologists would be affected by high cancer prevalence and the use of artificial intelligence (AI) for decision support. Materials and method: This reader study was based on selection of screening mammograms, including the original radiologist assessment, acquired in 2010 to 2013 at the Karolinska University Hospital, with a ratio of 1:1 cancer versus healthy based on a 2-year follow-up. A commercial AI system generated an exam-level positive or negative read, and image markers. Double-reading and consensus discussions were first performed without AI and later with AI, with a 6-week wash-out period in between. The chi-squared test was used to test for differences in contingency tables. Results: Mammograms of 758 women were included, half with cancer and half healthy. 52% were 40–55 years; 48% were 56–75 years. In the original non-enriched screening setting, the sensitivity was 61% (232/379) at specificity 98% (323/379). In the reader study, the sensitivity without and with AI was 81% (307/379) and 75% (284/379) respectively (p < 0.001). The specificity without and with AI was 67% (255/379) and 86% (326/379) respectively (p < 0.001). The tendency to change assessment from positive to negative based on erroneous AI information differed between readers and was affected by type and number of image signs of malignancy. Conclusion: Breast radiologists reading a list with high cancer prevalence performed at considerably higher sensitivity and lower specificity than the original screen-readers. Adding AI information, calibrated to a screening setting, decreased sensitivity and increased specificity. Clinical relevance statement: Radiologist screening mammography assessments will be biased towards higher sensitivity and lower specificity by high-risk triaging and nudged towards the sensitivity and specificity setting of AI reads. After AI implementation in clinical practice, there is reason to carefully follow screening metrics to ensure the impact is desired. Key Points: • Breast radiologists' sensitivity and specificity will be affected by changes brought by artificial intelligence. • Reading in a high cancer prevalence setting markedly increased sensitivity and decreased specificity. • Reviewing the binary reads by AI, negative or positive, biased screening radiologists towards the sensitivity and specificity of the AI system. [ABSTRACT FROM AUTHOR]

The channels and content of communication play an integral role in creating breast cancer screening awareness. Although breast cancer screening awareness campaigns are increasing in Ghana, no study has been conducted to investigate the communication channels used by these campaigns. This study aimed to identify the most effective source of breast cancer screening awareness information among women presenting for mammography in Ghana. Ethical approval was sought before data collection. A cross-sectional quantitative approach was adopted for the study and involved 192 women who visited two mammography centers in October 2020 for mammography screening. A self-administered closed-ended questionnaire was used for data collection. Descriptive and inferential statistics were carried out using SPSS version 26. A total of 192 responses were obtained. Of them, 72 (37.5%) participants had diploma/Higher National Diploma/degree education, with 105 (54.7%) of them being traders or nonprofessionals. All participants had heard of mammography screening and examination before this study. Mass media was the most common source of information on mammography screening (86 [44.8%]), of which radio was the highest subcategory (34 [39.5%]). Moreover, women presenting for mammography in Ghana demonstrated a high level of knowledge of breast cancer screening. Mass media is the most common source of information on breast cancer screening awareness in Ghana and has the potential to positively impact sensitization programs by reaching out to more women. There is a need to engage the Ghanaian population using mass media and health facilities to maximize the impact of breast cancer screening awareness campaigns. [Display omitted] [ABSTRACT FROM AUTHOR]

Mammography can reduce breast cancer incidence and mortality. Studies on the utilization of mammography among migrant and non-migrant women are inconsistent. Many of these studies do not take the heterogeneity of migrants in terms of ethnicity and country of origin into account. The aim of the present study was to examine disparities in the use of mammography between non-migrant women and the five largest migrant groups in Austria. The study used data from a nationwide population-based survey of 5118 women aged 45 years and older and analyzed the participation in mammography as a dependent variable. Multivariable logistic regression was used to compare mammography uptake between the aforementioned groups of women, while adjusting for socioeconomic and health variables. The study shows that all migrant groups involved tended to use mammography less frequently than non-migrant women; statistically significant differences, however, were only observed for Hungarian migrant women (adjusted OR = 0.36; 95%-CI: 0.13, 0.95; p = 0.038) and women from a Yugoslavian successor state (adjusted OR = 0.55; 95%-CI: 0.31, 0.99; p = 0.044). These findings are consistent with other studies in Europe and beyond, highlighting the heterogeneity of migrant populations and emphasizing the need for a diversity-sensitive approach to health care. [ABSTRACT FROM AUTHOR]

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Background: Breast and cervical cancer are major public health issues globally. The reduction in incidence and mortality rates of these cancers is linked to effective prevention, early detection, and appropriate treatment measures. This study aims to analyze the temporal trends in the prevalence of mammography and Papanicolaou test coverage among women living in Brazilian state capitals between 2007 and 2023, and to compare the coverage of these tests before and during the Covid-19 pandemic. Methods: A time series study was conducted using data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey from 2007 to 2023. The variables analyzed included mammography and Papanicolaou test coverage according to education level, age group, race/skin color, regions, and Brazilian capitals. The Prais-Winsten regression model was used to analyze the time series, and Student's t-test was employed to compare the prevalence rates between 2019 and 2023. Results: Between 2007 and 2023, mammography coverage showed a stationary trend (71.1% in 2007 and 73.1% in 2023; p-value = 0.75) with a declining trend observed among women with 12 years or more of education (APC= -0.52% 95%CI -1.01%; -0.02%). Papanicolaou test coverage for all women aged between 25 and 64 exhibited a downward trend from 82% in 2007 to 76.8% in 2023 (APC= -0.45% 95%CI -0.76%; -0.13%). This decline was also noticed among those with 9 years or more of education; in the 25 to 44 age group; among women with white and mixed race; and in the Northeast, Central-West, Southeast, and South regions. When comparing coverage before and during Covid-19 pandemic, a reduction was noted for both tests. Conclusions: Over the years, there has been stability in mammography coverage and a decline in Papanicolaou test. The COVID-19 pandemic negatively impacted the number of these tests carried out among women, highlighting the importance of actions aimed at increasing coverage, especially among the most vulnerable groups. [ABSTRACT FROM AUTHOR]

Background: Internationally, non-participation in breast screening increased with decreasing level of education indicating importance of information campaigns to enhance awareness of screening. However, in Denmark in the 1990s the association between education and non-participation was U-shaped. We therefore analyzed recent Danish data. Methods: Data derived from the Capital Region of Denmark, biennial, organized breast screening program 2008–2020, where women aged 50–69 were personally invited to screening. Non-participation was measured as number of women with no participation out of women eligible for at least three invitations. Sociodemographic determinants were identified by linkage to public registers. Results were reported as age adjusted odds ratios (OR) of non-participation including 95% confidence intervals (CI). Results: Among 196,085 women, 86% participated. Using women with low education as baseline, the OR for professional bachelors was 0.64; and for academics 0.75. The strongest determinants of non-participation were being non-married OR 2.03; born outside Denmark OR 2.04; being self-employed OR 1.67; retired OR 3.12; on public support OR 3.66; or having co-morbidity OR 1.56. Conclusion: The U-shaped association between education and non-participation in breast screening prevailed. The data further indicated that screening participation was low in women with pertinent health and social problems. [ABSTRACT FROM AUTHOR]

Introduction: Patients with clinically node-negative breast cancer have a negative sentinel lymph node status (pN0) in approximately 75% of cases and the necessity of routine surgical nodal staging by sentinel lymph node biopsy (SLNB) has been questioned. Previous prediction models for pN0 have included postoperative variables, thus defeating their purpose to spare patients non-beneficial axillary surgery. We aimed to develop a preoperative prediction model for pN0 and to evaluate the contribution of mammographic breast density and mammogram features derived by artificial intelligence for de-escalation of SLNB. Materials and methods: This retrospective cohort study included 755 women with primary breast cancer. Mammograms were analyzed by commercially available artificial intelligence and automated systems. The additional predictive value of features was evaluated using logistic regression models including preoperative clinical variables and radiological tumor size. The final model was internally validated using bootstrap and externally validated in a separate cohort. A nomogram for prediction of pN0 was developed. The correlation between pathological tumor size and the preoperative radiological tumor size was calculated. Results: Radiological tumor size was the strongest predictor of pN0 and included in a preoperative prediction model displaying an area under the curve of 0.68 (95% confidence interval: 0.63-0.72) in internal validation and 0.64 (95% confidence interval: 0.59-0.69) in external validation. Although the addition of mammographic features did not improve discrimination, the prediction model provided a 21% SLNB reduction rate when a false negative rate of 10% was accepted, reflecting the accepted false negative rate of SLNB. Conclusion: This study shows that the preoperatively available radiological tumor size might replace pathological tumor size as a key predictor in a preoperative prediction model for pN0. While the overall performance was not improved by mammographic features, one in five patients could be omitted from axillary surgery by applying the preoperative prediction model for nodal status. The nomogram visualizing the model could support preoperative patient-centered decision-making on the management of the axilla. [ABSTRACT FROM AUTHOR]

Objectives: To explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC). Methods: Data from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis. Results: In the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p<0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p>0.05). Conclusion: The effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities. [ABSTRACT FROM AUTHOR]

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM. [ABSTRACT FROM AUTHOR]

Contrast-enhanced mammography (CEM) is a relatively new imaging technique that allows morphologic, anatomic and functional imaging of the breast. The aim of our study was to validate contrast-enhanced mammography (CEM) compared to mammography (MMG) and digital breast tomosynthesis (DBT) in daily clinical practice. This retrospective study included 316 consecutive patients who underwent MMG, DBT and CEM at the Centre for Prevention and Diagnosis of Chronic Diseases of Primorsko-goranska County. Two breast radiologists independently analyzed the image data, without available anamnestic information and without the possibility of comparison with previous images, to determine the presence of suspicious lesions and their morphological features according to the established criteria of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The diagnostic value of MMG, DBT and CEM was assessed by ROC analysis. The interobserver agreement was excellent. CEM showed higher diagnostic accuracy in terms of sensitivity and specificity compared to MMG and DBT, the reporting time for CEM was significantly shorter, and CEM findings resulted in a significantly lower proportion of equivocal findings (BI-RADS 0), suggesting fewer additional procedures. In conclusion, CEM achieves high diagnostic accuracy while maintaining simplicity, reproducibility and applicability in complex clinical settings. [ABSTRACT FROM AUTHOR]

Introduction: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs. Material and methods: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations. Results: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images. Conclusion: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources. Relevance statement: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs. Key points: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic performance of breast MRI for BI-RADS 4A microcalcifications on mammography and propose a potential clinical pathway to avoid unnecessary biopsies. Methods: Bibliometrics analysis of breast MRI and BI-RADS 4 was provided. A retrospective analysis was conducted on 139 women and 142 cases of BI-RADS 4A microcalcifications on mammography from Fudan University Shanghai Cancer Center. The mammographic BI-RADS level and the MRI reports were compared with the final pathological diagnosis. Results: Much attention has been given to breast MRI and BI-RADS 4 in the literature. However, studies on BI-RADS 4A are limited. Pathological results showed 117 cases (82.4%) were benign lesions, malignant cases of 25 (17.6%) in our study. The positive predictive values (PPV), specificity, sensitivity and negative predictive values (NPV) of MRI were 44.2% (23/52), 75.2% (88/117), 92.0% (23/25), and 97.8% (88/90), respectively. Therefore, 75.2% (88/117) of biopsies for benign lesions could potentially be avoided. There were 2.2% (2/90) malignant lesions missed. Logistic regression indicated that patients who are postmenopausal (HR = 2.655, p = 0.012), have a history of breast cancer (family history) (HR = 2.833, p = 0.029), and exhibit clustered microcalcifications (HR = 2.179, p = 0.046) are more likely to have a higher MRI BI-RADS level. Conclusions: Breast MRI has the potential to improve the diagnosis of BI-RADS 4A microcalcifications on mammography. We propose a potential clinical pathway that patients with BI-RADS 4A on mammography who are premenopausal, have no personal history of breast cancer (family history) or have non-clustered distribution of calcifications can undergo MRI to avoid unnecessary biopsies. [ABSTRACT FROM AUTHOR]

Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for automating this identification process. This study explored the impact of semi-supervised learning on identifying mammographic calcifications by including 712 mammographic images from 252 patients in public datasets. Initially, 212 mammogram images were segmented into patches and classified visually for calcification presence. A subset of these patches, derived from 169 mammogram images, was used to train a ResNet50-based classifier. The classifier was evaluated using patches generated from 43 mammograms as a test data set. Additionally, 500 more mammogram images were processed into patches and analyzed using the trained ResNet50 model, with semi-supervised learning applied to patches exceeding certain classification probabilities. This process aimed to enhance the classifier's accuracy and achieve improvements over the initial model. The findings indicated that semi-supervised learning significantly benefits the accuracy of calcification detection in mammography, underscoring its utility in enhancing diagnostic methodologies. [ABSTRACT FROM AUTHOR]

The most common and rapidly spreading disease in the world is breast cancer. Most cases of breast cancer are observed in females. Breast cancer is controlled with early detection. Therefore, early detection and categorization of breast cancer are essential to enable patients to take the right course of treatment. Early discovery helps to manage many cases and lower the death rate. In this study, a brand-new Spider Monkey-based Recurrent Neural System (SMbRNS) is created for predicting breast cancer cells in an early stage. Breast mammography images are used in this instance as the dataset for the system. The breast dataset is also analyzed using the established SMbRNS function to detect and segregate the breast cancer-afflicted region efficiently. The developed model aims to enhance the segmented breast cancer results using spider monkey fitness. The developed method computes the chance of breast cancer using the dataset; segmented images are used for monitoring. Additionally, the Python code used to perform this strategy allows for evaluating the created model parameters against earlier research. The experimental results are validated with other prevailing models regarding the accuracy, precision, sensitivity, specificity, and F1-score to prove the efficiency. The designed model gained 99.82% accuracy and 99.12% precision for segmenting breast cancer. The current study model produces mammography with better accuracy for the segmentation of breast cancer. [ABSTRACT FROM AUTHOR]

To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI. A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications. The constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model. A combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. Purpose: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. Material and Methods: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. Results: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64–0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45–0.58) with matched rates of 56.7%–67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. Conclusion: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG). [ABSTRACT FROM AUTHOR]

Background Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. Purpose To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. Methods The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. Results A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919–0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. Conclusion While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour. [ABSTRACT FROM AUTHOR]

Objective: To improve breast radiographers' individual performance by using automated software to assess the correctness of breast positioning and compression in tomosynthesis screening. Materials and methods: In this retrospective longitudinal analysis of prospective cohorts, six breast radiographers with varying experience in the field were asked to use automated software to improve their performance in breast compression and positioning. The software tool automatically analyzes craniocaudal (CC) and mediolateral oblique (MLO) views for their positioning quality by scoring them according to PGMI classifications (perfect, good, moderate, inadequate) and checking whether the compression pressure is within the target range. The positioning and compression data from the studies acquired before the start of the project were used as individual baselines, while the data obtained after the training were used to test whether conscious use of the software could help the radiographers improve their performance. The percentage of views rated perfect or good and the percentage of views in target compression were used as overall metrics to assess changes in performance. Results: Following the use of the software, all radiographers significantly increased the percentage of images rated as perfect or good in both CCs and MLOs. Individual improvements ranged from 7 to 14% for CC and 10 to 16% for MLO views. Moreover, most radiographers exhibited improved compression performance in CCs, with improvements up to 16%. Conclusion: Active use of a software tool to automatically assess the correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers. Clinical relevance statement: This study suggests that the use of a software tool for automatically evaluating correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers on these metrics, which may ultimately lead to improved screening outcomes. Key Points: • Proper breast positioning and compression are critical in breast cancer screening to ensure accurate diagnosis. • Active use of the software increased the quality of craniocaudal and mediolateral oblique views acquired by all radiographers. • Improved performance of radiographers is expected to improve screening outcomes. [ABSTRACT FROM AUTHOR]