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BACKGROUND: Invasive lobular carcinoma (ILC) is the second most common subtype of breast cancer. Although mammography is known to have low sensitivity for ILC, there are no data to guide the optimal surveillance after treatment. We explored surveillance strategies after breast-conserving surgery (BCS) for ILC and determined the proportion of imaging-detected recurrences versus interval cancers. METHODS: From an institutional database of 813 women, we retrospectively identified patients who underwent BCS for stage I-III ILC and subsequently had a recurrence. We categorized patients by surveillance strategy and determined the modality of recurrence detection. Interval cancer rates for local recurrences were compared across surveillance strategies using the Chi-square test. We evaluated overall survival with the log-rank test and a Cox proportional hazards model. RESULTS: We included 58 patients with ILC who had a recurrence after BCS. Of these, 22 (37.9%) had local recurrence, 27 (46.6%) had distant recurrence, and 9 (15.5%) had both local and distant recurrence. Most patients underwent routine mammographic surveillance (65.2%), with 19.6% having supplemental breast magnetic resonance imaging (MRI) and 15.2% having no surveillance. The interval cancer rate was significantly higher in the mammographic surveillance group compared with the MRI surveillance group (61.9% vs. 16.7%; p

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

BACKGROUND: Examining screening outcomes by breast density for breast magnetic resonance imaging (MRI) with or without mammography could inform discussions about supplemental MRI in women with dense breasts. METHODS: We evaluated 52 237 women aged 40-79 years who underwent 2611 screening MRIs alone and 6518 supplemental MRI plus mammography pairs propensity score-matched to 65 810 screening mammograms. Rates per 1000 examinations of interval, advanced, and screen-detected early stage invasive cancers and false-positive recall and biopsy recommendation were estimated by breast density (nondense = almost entirely fatty or scattered fibroglandular densities; dense = heterogeneously/extremely dense) adjusting for registry, examination year, age, race and ethnicity, family history of breast cancer, and prior breast biopsy. RESULTS: Screen-detected early stage cancer rates were statistically higher for MRI plus mammography vs mammography for nondense (9.3 vs 2.9; difference = 6.4, 95% confidence interval [CI] = 2.5 to 10.3) and dense (7.5 vs 3.5; difference = 4.0, 95% CI = 1.4 to 6.7) breasts and for MRI vs MRI plus mammography for dense breasts (19.2 vs 7.5; difference = 11.7, 95% CI = 4.6 to 18.8). Interval rates were not statistically different for MRI plus mammography vs mammography for nondense (0.8 vs 0.5; difference = 0.4, 95% CI = -0.8 to 1.6) or dense breasts (1.5 vs 1.4; difference = 0.0, 95% CI = -1.2 to 1.3), nor were advanced cancer rates. Interval rates were not statistically different for MRI vs MRI plus mammography for nondense (2.6 vs 0.8; difference = 1.8 (95% CI = -2.0 to 5.5) or dense breasts (0.6 vs 1.5; difference = -0.9, 95% CI = -2.5 to 0.7), nor were advanced cancer rates. False-positive recall and biopsy recommendation rates were statistically higher for MRI groups than mammography alone. CONCLUSION: MRI screening with or without mammography increased rates of screen-detected early stage cancer and false-positives for women with dense breasts without a concomitant decrease in advanced or interval cancers.

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.

Purpose: Women with a personal history of breast cancer have an increased risk of subsequent breast malignancy and may benefit from more sensitive surveillance than conventional mammography (MG). We previously reported outcomes for first surveillance episode using contrast-enhanced mammography (CEM), demonstrating higher sensitivity and comparable specificity to MG. We now report CEM performance for subsequent surveillance. Methods: A retrospective study of 1,190 women in an Australian hospital setting undergoing annual surveillance following initial surveillance CEM between June 2016 and December 2022. Outcome measures were recall rate, cancer detection rate, contribution of contrast to recalls, false positive rate, interval cancer rate and characteristics of surveillance detected and interval cancers. Results: 2,592 incident surveillance episodes were analysed, of which 93% involved contrast-based imaging. Of 116 (4.5%) recall episodes, 40/116 (34%) recalls were malignant (27 invasive; 13 ductal carcinoma in situ), totalling 15.4 cancers per 1000 surveillance episodes. 55/116 (47%) recalls were contrast-directed including 17/40 (43%) true positive recalls. Tumour features were similar for contrast-directed recalls and other diagnoses. 8/9 (89%) of contrast-directed invasive recalls were Grade 2–3, and 5/9 (56%) were triple negative breast cancers. There were two symptomatic interval cancers (0.8 per 1000 surveillance episodes, program sensitivity 96%). Conclusion: Routine use of CEM in surveillance of women with PHBC led to an increase in the detection of clinically significant malignant lesions, with a low interval cancer rate compared to previous published series. Compared to mammographic surveillance, contrast-enhanced mammography increases the sensitivity of surveillance programs for women with PHBC. [ABSTRACT FROM AUTHOR]

Background: This study investigates breast cancer screening disparities among Mexican immigrant women in rural and urban U.S. communities, focusing on cultural beliefs, healthcare access, and geographical differences. Methods: A cross-sectional study of 350 Mexican immigrant women aged 40+ without prior cancer diagnosis was conducted in rural (<2000 population) and urban parts of the U.S. in 2015. Culturally tailored surveys during household visits, clinic appointments, and community meetings assessed cultural beliefs, mammography engagement, and family support. Comparative analyses t -tests and chi-square tests were conducted, with significance set at P <.05. Results: Rural women demonstrated higher adherence to spiritualismo (M = 4.31, SD = 1.13) compared to their urban counterparts (M = 3.91, SD = 1.36), marianismo (M = 3.33, SD = 0.45 vs M = 3.21, SD = 0.48), and machismo (M = 3.02, SD = 0.52 vs M = 2.80, SD = 0.61). The rural women also reported higher mammography rates (54.4% vs 45.6%), with 60.4% undergoing four or more mammograms. Despite barriers such as doctor non-recommendation (55.0% rural vs 45.0% urban) and embarrassment (67.9% rural vs 32.1% urban), rural women engaged more in family discussions about cancer screenings (61.7% vs 38.3%) and received more family assistance in finding health information (59.8% vs 40.2%). Conclusions: This study elucidates significant rural-urban disparities in cultural adherence and family support among Mexican immigrant women, underscoring the necessity for culturally tailored interventions to enhance breast cancer screening rates and health outcomes. [ABSTRACT FROM AUTHOR]

Aim: Early diagnosis and screening of breast cancer are of vital importance in the fight against the disease. It is crucial to identify the underlying barriers that prevent women from undergoing mammography and to develop necessary strategies to overcome these obstacles. The purpose of this study was to adapt "The Perceived Barriers of Mammography Scale (PBMS‐23)" into Turkish and to conduct a validity and reliability study. Methods: This study used a methodological approach. The study was conducted with 233 women admitted to the Gynecology Outpatient Clinic of a university hospital. Data were collected by using the Introductory Information Form, PBMS‐23, Champion's Health Belief Model Scale for Mammography Screening (CHBMS‐MS). Psychometric properties of the scale were tested with content validity, confirmatory factor analysis, convergent validity, and test–retest reliability coefficient. Results: Content validity confirmatory factor analysis resulted in 휒2/SD = 1.67; CFI = 0.97, NFI = 0.92, GFI = 0.86, RMSEA = 0.054. The scale had a Cronbach's alpha of 0.87, while the subscales had Cronbach's alpha values of 0.34 to 0.80. The intraclass correlation coefficient values calculated for the test–retest reliability were between 0.83 and 0.96 for the subscales and 0.96 for the overall scale. There is a positive and statistically significant relationship (p < 0.01) between the CHBMS‐MS barriers dimension and prejudices dimension scores and all subdimensions and the total of the PBMS‐23. Conclusion: The PBMS‐23 was accepted as a valid and reliable tool with 23 items and eight‐factor structure that can be useful in measuring Turkish women's barriers to mammography. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To identify barriers to surveillance mammography adherence in Korean breast cancer survivors (BCSs), which is crucial for early detection of recurrence and new cancers. SAMPLE & SETTING: 195 BCSs were recruited from a breast cancer clinic and its support groups at a South Korean hospital. METHODS & VARIABLES: This descriptive study used a cross-sectional design. Participants completed a self-administered multi-instrument survey based on a comprehensive framework for adherence, including individual characteristics, symptoms, quality of life, cognitive appraisal, social support, and healthcare system factors. RESULTS: Having had a mammogram within the past year was considered adherent (n = 177), and no mammography within the past year was considered nonadherent (n = 18). Logistic regression revealed that longer time since diagnosis (p < 0.001), greater depression (p = 0.024), and higher health services utilization (p < 0.001) were predictors of lower mammography adherence (c² = 76.618, p < 0.001, R² = 58%). IMPLICATIONS FOR NURSING: This is the first study to identify depression as a barrier to surveillance mammography in BCSs, suggesting that depression screening and treatment may be important for increasing adherence. [ABSTRACT FROM AUTHOR]

Objective: To investigate the features and outcomes of breast cancer in high-risk subgroups. Materials and Methods: REB approved an observational study of women diagnosed with breast cancer from 2010 to 2019. Three radiologists, using the BI-RADS lexicon, blindly reviewed mammogram and MRI screenings without a washout period. Consensus was reached with 2 additional reviewers. Inter-rater agreement was measured by Fleiss Kappa. Statistical analysis included Mann-Whitney U, Chi-square tests for cohort analysis, and Kaplan-Meier for survival rates, with a Cox model for comparative analysis using gene mutation as a reference. Results: The study included 140 high-risk women, finding 155 malignant lesions. Significant age differences noted: chest radiation therapy (median age 44, IQR: 37.0-46.2), gene mutation (median age 49, IQR: 39.8-58.0), and familial risk (median age 51, IQR: 44.5-56.0) (P =.007). Gene mutation carriers had smaller (P =.01), higher-grade tumours (P =.002), and more triple-negative ER- (P =.02), PR- (P =.002), and HER2- (P =.02) cases. MRI outperformed mammography in all subgroups. Substantial to near-perfect inter-rater agreement observed. Over 10 years, no deaths occurred in chest radiation group, with no significant survival difference between gene mutation and familial risk groups, HR = 0.93 (95% CI: 0.27, 3.26), P =.92. Conclusion: The study highlights the importance of age and specific tumour characteristics in identifying high-risk breast cancer subgroups. MRI is confirmed as an effective screening tool. Despite the aggressive nature of cancers in gene mutation carriers, early detection is crucial for survival outcomes. These insights, while necessitating further validation with larger studies, advocate for a move toward personalized medical care, strengthening the existing healthcare guidelines. [ABSTRACT FROM AUTHOR]

Mammography and MRI screening typically occur in combination or in alternating sequence. We compared multimodality screening performance accounting for the relative timing of mammography and MRI and overlapping follow-up periods. We identified 8,260 screening mammograms performed 2005 to 2017 in the Breast Cancer Surveillance Consortium, paired with screening MRIs within ±90 days (combined screening) or 91 to 270 days (alternating screening). Performance for combined screening (cancer detection rate [CDR] per 1,000 examinations and sensitivity) was calculated with 1-year follow-up for each modality, and with a single follow-up period treating the two tests as a single test. Alternating screening performance was calculated with 1-year follow-up for each modality and also with follow-up ending at the next screen if within 1 year (truncated follow-up). For 3,810 combined screening pairs, CDR per 1,000 screens was 6.8 (95% confidence interval [CI]: 4.6-10.0) for mammography and 12.3 (95% CI: 9.3-16.4) for MRI as separate tests compared with 13.1 (95% CI: 10.0-17.3) as a single combined test. Sensitivity of each test was 48.1% (35.0%-61.5%) for mammography and 79.7% (95% CI: 67.7%-88.0%) for MRI compared with 96.2% (95% CI: 85.9%-99.0%) for combined screening. For 4,450 alternating screening pairs, mammography CDR per 1,000 screens changed from 3.6 (95% CI: 2.2-5.9) to zero with truncated follow-up; sensitivity was incalculable (denominator = 0). MRI CDR per 1,000 screens changed from 12.1 (95% CI 9.3-15.8) to 11.7 (95% CI: 8.9-15.3) with truncated follow-up; sensitivity changed from 75.0% (95% CI 63.8%-83.6%) to 86.7% (95% CI 75.5%-93.2%). Updating auditing approaches to account for combined and alternating screening sequencing and to address outcome attribution issues arising from overlapping follow-up periods can improve the accuracy of multimodality screening performance evaluation. [Display omitted] [ABSTRACT FROM AUTHOR]

To share the experience and results of the first cohort of the ACR Mammography Positioning Improvement Collaborative, in which participating sites aimed to increase the mean percentage of screening mammograms meeting the established positioning criteria to 85% or greater and show at least modest evidence of improvement at each site by the end of the improvement program. The sites comprising the first cohort of the collaborative were selected on the basis of strength of local leadership support, intra-organizational relationships, access to data and analytic support, and experience with quality improvement initiatives. During the improvement program, participating sites organized their teams, developed goals, gathered data, evaluated their current state, identified key drivers and root causes of their problems, and developed and tested interventions. A standardized image quality scoring system was also established. The impact of the interventions implemented at each site was assessed by tracking the percentage of screening mammograms meeting overall passing criteria over time. Six organizations were selected to participate as the first cohort, beginning with participation in the improvement program. Interventions developed and implemented at each site during the program resulted in improvement in the average percentage of screening mammograms meeting overall passing criteria per week from a collaborative mean of 51% to 86%, with four of six sites meeting or exceeding the target mean performance of 85% by the end of the improvement program. Afterward, all respondents to the postprogram survey indicated that the program was a positive experience. Using a structured improvement program within a learning network framework, the first cohort of the collaborative demonstrated that improvement in mammography positioning performance can be achieved at multiple sites simultaneously and validated the hypothesis that local sites' shared experiences, insights, and learnings would not only improve performance but would also build a community of improvers collaborating to create the best experience for technologists, staff, and patients. [Display omitted] [ABSTRACT FROM AUTHOR]

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The nipple–areolar complex (NAC) is an anatomically unique region from which several normal variants and pathologies arise. Understanding its anatomy is crucial for accurate clinical and imaging assessments, aiding with differential diagnosis, and ensuring radiologic–pathologic concordance. Mammography and US are commonly used for NAC evaluation; however, these are susceptible to technical limitations such as tissue superimposition and artifacts, compromising visualization of abnormalities in this area. Although MRI offers higher sensitivity, it is not the initial evaluation modality. A comprehensive clinical inspection is necessary because it may reveal abnormalities not apparent on imaging. This article offers an anatomical approach to the NAC evaluation, simplifying differential diagnoses by reviewing imaging techniques and clinical features of common NAC abnormalities. [ABSTRACT FROM AUTHOR]

Background & Objective: Breast cancer threatens the lives of many women around the world each year. Although mammography is considered the gold standard for early detection of early-stage breast cancer, different diagnostic methods such as the Breastlight device have been proposed for cancer screening in recent years. This study aimed to compare the diagnostic accuracy of Breastlight and mammography in women referred to clinics in Khorramabad, Iran in 2018. Materials & Methods: In this clinical trial, 252 women older than 40 years of age who were eligible for breast cancer screening or had breast complaints were included. The breast tissue was first assessed using a Breastlight device in a dark room. Then, all participants were re-examined by mammography. The results of Breastlight and mammography methods including true positive rate, true negative rate, false positive rate, and false negative rate were compared. The main bases of comparison were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of mammography and Breastlight methods. The collected data were analyzed by SPSS software version 22. Results: Compared to mammography, the Breastlight method showed a sensitivity of 78.3%, specificity of 99.5%, PPV of 97%, NPV of 95%, and diagnostic accuracy of 95.6%. Conclusion: Although mammography is more effective than other common methods in diagnosing early-stage breast cancer, Breastlight is a simple and cost-effective device that can be considered a valid tool in breast cancer screening. [ABSTRACT FROM AUTHOR]

Breast cancer has been a significant contributor to cancer-related mortality, but advancements in early detection through regular mammography and improvements in treatment modalities have contributed to declining mortality rates in several regions. This study presents a novel approach to cancer diagnosis utilizing Full-Field Digital Mammography images through predictive analysis methods. By using predictive analytic techniques and mammography images, this study offers a novel way to cancer detection. The research involves the application of deep learning techniques to extract valuable insights from cancer images captured by mammography devices. The CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) dataset including images from patients with varying types and stages of cancer, is collected and pre-processed to ensure uniformity and quality. Relevant features, including color, texture, and shape characteristics, are extracted, and a rigorous feature selection process is employed to identify discriminative markers. The Residual Network (ResNet) model is selected and trained on the dataset, with a focus on classification accuracy and robust predictive performance. Validation metrics, such as accuracy, IoU (Intersection over Union) score, dice score, and ROC (Receiver Operating Characteristic) curve are employed to evaluate the model's efficiency. After analysis, the proposed method had the best degree of mass lesion detection accuracy, at 99.24%. This research contributes to the advancement of non-invasive and efficient diagnostic tools, potentially enhancing early detection and intervention in cancer patients. The proposed method not only demonstrates promising results in terms of diagnostic accuracy but also emphasizes interpretability, seamless integration into clinical workflows, and adherence to ethical standards. [ABSTRACT FROM AUTHOR]

Objectives: Given the inevitable trend of domestic imaging center mergers and the current lack of comprehensive imaging evaluation guidelines for non-mass breast lesions, we have developed a novel BI-RADS risk prediction and stratification system for non-mass breast lesions that integrates clinical characteristics with imaging features from ultrasound, mammography, and MRI, with the aim of assisting clinicians in interpreting imaging reports. Methods: This study enrolled 350 patients with non-mass breast lesions (NMLs), randomly assigning them to a training set of 245 cases (70%) and a test set of 105 cases (30%). Radiologists conducted comprehensive evaluations of the lesions using ultrasound, mammography, andMRI. Independent predictors were identified using LASSO logistic regression, and a predictive riskmodel was constructed using a nomogram generated with R software, with subsequent validation in both sets. Results: LASSO logistic regression identified a set of independent predictors, encompassing age, clinical palpation hardness, distribution and morphology of calcifications, peripheral blood supply as depicted by color Doppler imaging, maximum lesion diameter, patterns of internal enhancement, distribution of non-mass lesions, time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values. The predictive model achieved area under the curve (AUC) values of 0.873 for the training group and 0.877 for the testing group. The model's positive predictive values were as follows: BI-RADS 2 = 0%, BI-RADS 3 = 0%, BI-RADS 4A = 6.25%, BI-RADS 4B = 26.13%, BI-RADS 4C = 80.84%, and BI-RADS 5 = 97.33%. Conclusion: The creation of a risk-predictive BI-RADS stratification, specifically designed for non-mass breast lesions and integrating clinical and imaging data from multiple modalities, significantly enhances the precision of diagnostic categorization for these lesions. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Prostate and breast cancer screening are prevalent among older adults, even among those unlikely to benefit. We aimed to evaluate why older adults stop cancer screening, including the role of physician recommendations.We used nationally representative data from the 2019 Medicare Current Beneficiary Survey (MCBS). We included women aged 76 and older without a history of breast cancer and men aged 71 and older without a history of prostate cancer. The primary outcome was reason for discontinuing screening, categorized as follows: (1) physician recommendation against screening; (2) absence of a recommendation to screen; and (3) patient‐driven reason, such as patient preferences or beliefs. We evaluated reasons for screening discontinuation by health status and educational attainment using age‐stratified multinomial logistic regression.The sample included 7350 participants representing a weighted population of 22,498,715. Overall, 53% of women underwent screening mammography in the past year or intended to continue screening. Among those who stopped screening, 5% reported a recommendation to stop screening from their doctor, 48% reported no recommendation, and 32% reported a patient‐driven reason for cessation. Findings did not differ by educational attainment or health status, including among the oldest patients. For men, 61% were screened with PSA in the past year or intended to continue. Among those who stopped, 3% reported a recommendation against screening, 54% reported no recommendation, and 27% reported a patient‐driven reason for cessation. Men with higher educational attainment were more likely to report that their physician recommended against screening (4% vs. 1%, p = 0.01) and that their doctor did not recommend screening (58% vs. 47%, p = 0.01). Reasons for screening cessation did not differ by health status, including among the oldest patients.Cancer screening remains common, even among those with limited potential for benefit, but discussions around screening cessation are rare. Improving communication between patients and physicians may improve screening decision quality. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Purpose Method Results Conclusion Breast cancer is one of the most diagnosed cancers in Arab countries. Lack of knowledge and awareness regarding breast cancer screening has increased the breast cancer‐related morbidity and mortality.This literature review aimed to assess published research papers with a focus on the levels of knowledge, attitude, practice, and barriers of women in Arab countries of the Middle East toward breast cancer and its screening.SCOPUS, MEDLINE, and Google Scholar were searched using specific terms for relevant, quantitative, original studies published between 2017 and 2022. All English articles that matched the inclusion criteria were included in this review. Fourteen studies focusing on knowledge, attitudes, and barriers regarding breast cancer were included. Two independent reviewers performed screening and extraction.Among the reviewed studies, a range from 19.6% in Oman to 67% in Saudi Arabia had poor knowledge of breast cancer. Past personal or family history was a well‐recognized risk factor (n = 5), and being worried about the results was the most common barrier to screening. Although most women were aware of screening methods, the majority did not practice screening. Social media and the internet were the most used sources of information used by women to obtain knowledge regarding breast cancer and its screening (n = 6).Most of the Arab female population had low levels of knowledge, attitudes, and practices regarding breast cancer and its screening. Programs designed to raise awareness are necessary, and more policy changes must take place on the national level in Arab, Middle Eastern countries to address the low knowledge, the negative attitudes, and the limited access to breast cancer screening. [ABSTRACT FROM AUTHOR]

Background: Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom. Methods: A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric. Results: On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively. Conclusion: One system outperformed others in DES algorithms, providing higher CNR at lower doses. Relevance statement: This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose. Key Points: DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose. [ABSTRACT FROM AUTHOR]

Objectives: To validate the performance of Mirai, a mammography-based deep learning model, in predicting breast cancer risk over a 1–5-year period in Mexican women. Methods: This retrospective single-center study included mammograms in Mexican women who underwent screening mammography between January 2014 and December 2016. For women with consecutive mammograms during the study period, only the initial mammogram was included. Pathology and imaging follow-up served as the reference standard. Model performance in the entire dataset was evaluated, including the concordance index (C-Index) and area under the receiver operating characteristic curve (AUC). Mirai's performance in terms of AUC was also evaluated between mammography systems (Hologic versus IMS). Clinical utility was evaluated by determining a cutoff point for Mirai's continuous risk index based on identifying the top 10% of patients in the high-risk category. Results: Of 3110 patients (median age 52.6 years ± 8.9), throughout the 5-year follow-up period, 3034 patients remained cancer-free, while 76 patients developed breast cancer. Mirai achieved a C-index of 0.63 (95% CI: 0.6–0.7) for the entire dataset. Mirai achieved a higher mean C-index in the Hologic subgroup (0.63 [95% CI: 0.5–0.7]) versus the IMS subgroup (0.55 [95% CI: 0.4–0.7]). With a Mirai index score > 0.029 (10% threshold) to identify high-risk individuals, the study revealed that individuals in the high-risk group had nearly three times the risk of developing breast cancer compared to those in the low-risk group. Conclusions: Mirai has a moderate performance in predicting future breast cancer among Mexican women. Critical relevance statement: Prospective efforts should refine and apply the Mirai model, especially to minority populations and women aged between 30 and 40 years who are currently not targeted for routine screening. Key Points: The applicability of AI models to non-White, minority populations remains understudied. The Mirai model is linked to future cancer events in Mexican women. Further research is needed to enhance model performance and establish usage guidelines. [ABSTRACT FROM AUTHOR]

Background: Numerous underlying causes can lead to inflammatory breast disorders. A wide range of non-specific symptoms may be presenting symptoms, which could cause a delay in diagnosis and thus improper therapy. Studies on artificial intelligence (AI) are rapidly developing and offer a wide range of possible uses in breast imaging. Artificial intelligence-based computer-assisted diagnosis (AI-CAD) holds promise in the field of mammography. It demonstrated diagnostic performances that are equivalent to or even better than those achieved by stand-alone methods. The current work aimed to identify whether AI can improve the performance of mammography in diagnosing inflammatory breast diseases and excluding the underlying malignancy in cases resistant to treatment that may reduce the need for interventional procedures such as biopsy. Methods: Our study was a retrograde one done on 34 patients with pathologically proven inflammatory breast lesions. Results: Suppurative breast lesions gave high false positive results. This was also the case with granulomatous mastitis; while simple inflammatory lesions gave true negative results on AI interrogation. Conclusions: Artificial intelligence can be of great value in diagnosing simple inflammatory breast lesions thus following up on such lesions can usually be sufficient without asking for unneeded biopsies. On the other hand, our study showed that AI had high false positive results in suppurative lesions and granulomatous mastitis. Consequently, ultrasonography can be more reliable in their diagnosis. [ABSTRACT FROM AUTHOR]

Objective: Determine the prevalence of breast cancer in male patients treated at the Breast Cancer Detection and Diagnosis Center (CDDCM) of the ISSSTE. Method: Observational, cross-sectional, retrospective study. All men aged between 18 and 90 years with palpable breast and/or axillary nodule, increased breast volume, nipple discharge, mastalgia, and changes in the skin of the breast who underwent mammography and/or ultrasound studies and biopsy at the CDDCM from January 1, 2019, to March 31, 2023, were included. Descriptive statistical methods were used for data analysis. Results: Forty-two patients were studied. The palpable nodule was the most frequent symptom in 95.2% of the patients. Twenty-eight-point six percent of patients required image-guided biopsy for histopathological correlation. The prevalence of breast cancer was 4%. Conclusion: The prevalence of breast cancer in men was higher than that reported by national reference centers. Breast image studies in men are performed in 100% of cases for diagnostic purposes. Spreading awareness of breast pathology in men will facilitate timely cancer detection in this population. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to evaluate the correlation between background parenchymal enhancement (BPE) and various patient-related and technical factors in recombined contrast-enhanced spectral mammography (CESM) images. Material and Methods: We assessed CESM images from 62 female patients who underwent CESM between May 2017 and October 2019, focusing on factors influencing BPE. A total of 235 images, all acquired using the same mammography machine, were analyzed. A region of interest (ROI) with a standard size of 0.75 to 1 cm2 was used to evaluate the minimal, maximal, and average pixel intensity enhancement. Additionally, the images were qualitatively assessed on a scale from 1 (minimal BPE) to 4 (marked BPE). We examined correlations with body mass index (BMI), age, hematocrit, hemoglobin levels, cardiovascular conditions, and the amount of pressure applied during the examination. Results: Our study identified a significant correlation between the amount of pressure applied during the examination and the BPE (Spearman's ρ = 0.546). Additionally, a significant but weak correlation was observed between BPE and BMI (Spearman's ρ = 0.421). No significant associations were found between BPE and menopausal status, cardiovascular preconditions, hematocrit, hemoglobin levels, breast density, or age. Conclusions: Patient-related and procedural factors significantly influence BPE in CESM images. Specifically, increased applied pressure and BMI are associated with higher BPE. [ABSTRACT FROM AUTHOR]

Objective: Mastalgia or breast pain is a very common symptom in women attending breast clinic. The aim of this study was to evaluate whether imaging for mastalgia leads to cancer detection in an area where routine breast cancer screening services are underutilized. Materials and Methods: This prospective study was performed between 1st March 2021 to 31st January 2023 at a tertiary care academic institution of central India. All patients underwent through clinical examination by a surgeon. Then patients were referred for ultrasound and/or X-ray mammography (MMG) depending on age. Cancer detection rate was calculated. Results: The final cohort consisted of 176 patients with mastalgia and without any abnormality on clinical breast examination. Sixteen patients had mass lesion on radiology and core needle biopsy resulted as infiltrating duct carcinoma in 7 patients and benign phylloides tumor in one patient. Overall case detection rate for cancer was 4%. Conclusion: The breast cancer detection rate in patients presenting with mastalgia was low. However, in the absence of routine mammographic screening in the Indian general population, these would have been missed. Hence, diagnostic assessment for mastalgia is an appropriate strategy in countries where routine screening MMG is lacking. [ABSTRACT FROM AUTHOR]

Objective: The aim of this retrospective study was to analyze the predictive capabilities of preoperative mammography, dynamic contrast-enhancedmagnetic resonance imaging (DCE-MRI), and diffusion-weighted imaging (DWI) in determining hormone receptor (HRc) status for pure ductal carcinoma in situ (DCIS) lesions. Materials and Methods: The study included a total of 79 patients who underwent preoperative mammography (MG) and MRI between December 2018 and December 2023 and were subsequently diagnosed with pure DCIS after surgery. The correlation between MG, DCE-MRI, and DWI features and estrogen receptor (ER) and progesterone receptor (PR) status was examined. Results: Among the lesions, 44 were double HRc-positive (ER and PR-positive), 13 were single HRc-positive (ER-positive and PR-negative or ERnegative and PR-positive) and 22 were double HRc-negative (ER and PR-negative). The presence of symptom (p = 0.029), the presence of comedo necrosis (p = 0.005) and high histological grade (p<0.001) were found to be associated with ER and PR negativity. Amorphous microcalcifications were more commonly observed in the double HRc-negative group, while linear calcifications were more prevalent in both double and single HRc-positive groups (p = 0.020). Non-mass enhancement (NME) with a linear distribution was significantly more common in double HRc-negative lesions (38%), and NME with a segmental distribution in both double (43%) and single (50%) receptor-positive lesions (p = 0.042). Evaluation of DWI findings revealed that a higher lesion-to-normal breast parenchyma apparent diffusion coefficient (ADC) ratio statistically increased the probability of HRc positivity (p = 0.033). Conclusion: Certain clinicopathological, mammography, and MRI features, along with the lesion-to-normal breast parenchyma ADC ratio, can serve as predictors for HRc status in DCIS lesions. [ABSTRACT FROM AUTHOR]

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

Objectives: Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach. Materials and methods: Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers. Results: For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography. Discussion: A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography. Clinical relevance statement: This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol. Key Points: • In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images. • A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups. • Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections. [ABSTRACT FROM AUTHOR]

Women with a personal history of breast cancer (PHBC) are at an increased risk of either a local recurrence or a new primary breast cancer. Thus, surveillance is essential for the detection of recurrent disease at the earliest possible stage, allowing for prompt treatment, and potentially improving overall survival. Nowadays, mammography follow-up is the only surveillance imaging technique recommended by international guidelines. Nevertheless, sensitivity of mammography is lower after breast cancer treatment, particularly during the first 5 years, due to increased density or post-treatment changes. Contrast-enhanced breast imaging techniques, such as MRI or contrast-enhanced mammography (CEM), are very sensitive to detect malignant enhancement, especially in dense breasts. This Special Report will provide arguments in favor of and against breast cancer follow-up with MRI or CEM, in a debate style between experts in Breast Imaging. Finally, the scientific points of pros and cons arguments will be summarized to help objectively decide the best follow-up strategy for women with a personal history of breast cancer. Clinical relevance statement: A personalized approach to follow-up imaging after conservative breast cancer treatment could optimize patient outcomes, using mammography as a baseline for most patients, and MRI or CEM selectively in patients with higher risks for a recurrence. Key Points: • Women with a personal history of breast cancer are at an increased risk of either a local recurrence or a new primary breast cancer. • Breast cancer survivors may benefit from additional imaging with MRI/CEM, in case of increased risk of a second breast cancer, with dense breasts or a cancer diagnosis before age 50 years. • As survival after local recurrence seems to depend on the initial stage at diagnosis, imaging should be more focused on detecting tumors in the earliest stages. [ABSTRACT FROM AUTHOR]

Objective: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. Materials and method: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013–2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1–4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1–10 were stratified by VDG. Results: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1–91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2–91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9–95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3–99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5–70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8–68.7) for VDG4. Conclusion: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. Clinical relevance statement: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. Key Points: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists. [ABSTRACT FROM AUTHOR]

Objective: Triple-negative breast cancer (TNBC) has an aggressive clinical behaviour, with advanced stages at initial diagnostic evaluation, early recurrences and poor survival, so the purpose was to determine the clinical and radiological manifestations associated with TNBC. Materials and methods: A case-control study in women diagnosed with breast cancer from January 2015 to August 2022 at the 'Instituto Regional de Enfermedades Neoplásicas del Norte'. We classified cases (Triple Negative subtype) and controls (Luminal A, Luminal B and HER2) according to immunohistochemistry ical analysis. Bivariate and multivariate logistic regression models were used to calculate the odds ratio (OR) with their respective 95% confidence intervals (CIs). Results: The medical reports of 88 cases and 236 controls were reviewed. Cases were more likely to report pain (p = 0.001), nodules on ultrasound (p = 0.01) and mammography (p = 0.003), superior median size (p < 0.05), posterior enhancement (p = 0.001) and moderate density (p = 0.003). Multivariate analysis identified that TNBC was more likely to have a nodular type lesion by ultrasound (OR: 9.73, 95% CI: 1.10--86.16; p = 0.04), ultrasound lesion larger than 36 mm (OR: 4.99, 95% CI: 1.75-14.17; p = 0.003) and moderate density (OR: 3.83, 95% CI: 1.44-10.14; p = 0.007). Conclusion: There are particular clinical and imaging manifestations of TNBC, showing that radiological lesions that presented characteristics in ultrasound as nodular type lesions larger than 36 mm and in mammography moderate grade density, were associated with this subtype of breast tumours in a Peruvian population. [ABSTRACT FROM AUTHOR]

Correct alignment of the image receptor (IR) in mammography for the mediolateral oblique (MLO) view of the breast is fundamental to enable the maximum inclusion of breast tissue. This study aims to assess Australian radiographers' knowledge and digital era practice in selecting the IR angle for female body habitus in the MLO view of the breast. An online survey was distributed to all members of the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) through their electronic newsletter and via direct email to radiographers holding the Certificate of Mammographic Practice (CMP). Descriptive analysis was undertaken, and a Pearson's chi-squared test of independence was used to compare associations between academic qualification and IR selection data. A value of p < 0.05 was deemed statistically significant. A total of 107 valid surveys were returned; 67.3 % reported using the posterior lateral margin to select the IR angle. For linear body habitus, 44.9 % reported using 50°; for all other body habitus, participants most commonly used 45° (59.1 %); 85.1 % used a range of angles between 40 and 55°; 16.8 % recognised the link between correct IR angle selection and breast tissue inclusion. The range of angles used in practice has reduced in the digital era; the frequency of the use of 45° across all body habitus may reflect tube angle movement automation. Few radiographers recognised the important link between correct selection of IR angle and breast tissue inclusion on the image. Understanding of the link between IR angle selection and image quality enhances current practice in the digital era to maximise the inclusion of breast tissue and minimise the potential of missed breast cancer. [ABSTRACT FROM AUTHOR]

Breast cancer is the second most prevalent cause of cancer death and the most common malignancy among women, posing a life-threatening risk. Treatment for breast cancer can be highly effective, with a survival chance of 90% or higher, especially when the disease is detected early. This paper introduces a groundbreaking deep U-Net framework for mammography breast cancer images to perform automatic detection of abnormalities. The objective is to provide segmented images that show areas of tumors more accurately than other deep learning techniques. The proposed framework consists of three steps. The first step is image preprocessing using the Li algorithm to minimize the cross-entropy between the foreground and the background, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and median filtering. The second step involves data augmentation to mitigate overfitting and underfitting, and the final step is implementing a convolutional encoder-decoder network-based U-Net architecture, characterized by high precision in medical image analysis. The framework has been tested on two comprehensive public datasets, namely INbreast and CBIS-DDSM. Several metrics have been adopted for quantitative performance assessment, including the Dice score, sensitivity, Hausdorff distance, Jaccard coefficient, precision, and F1 score. Quantitative results on the INbreast dataset show an average Dice score of 85.61% and a sensitivity of 81.26%. On the CBIS-DDSM dataset, the average Dice score is 87.98%, and the sensitivity reaches 90.58%. The experimental results ensure earlier and more accurate abnormality detection. Furthermore, the success of the proposed deep learning framework in mammography shows promise for broader applications in medical imaging, potentially revolutionizing various radiological practices. [ABSTRACT FROM AUTHOR]

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the diagnostic performance of abbreviated breast MRI compared with mammography in women with a family history of breast cancer included in the Croatian National Breast Screening Program. Methods: 178 women with a family history of breast cancer aged 50 to 69 underwent abbreviated breast MRI and mammography. Radiological findings for each method were categorized according to the BI-RADS classification. The gold standard for assessing the diagnostic accuracy of breast MRI and mammography, in terms of suspicious BI-RADS 4 and BI-RADS 5 findings, was the histopathological diagnosis. Performance measures, including cancer detection rates, specificity, sensitivity, and positive and negative predictive values, were calculated for both imaging methods. Results: Twelve new cases of breast cancer were detected, with seven (58.3%) identified only by abbreviated breast MRI, four (33.3%) detected by both mammography and breast MRI, and one (8.3%) diagnosed only by mammography. Diagnostic accuracy parameters for abbreviated breast MRI were 91.67% sensitivity, 94.58% specificity, 55.0% positive predictive value (PPV), and 99.37% negative predictive value (NPV), while for mammography, the corresponding values were 41.67%, 96.39%, 45.46%, and 95.81%, respectively. Conclusions: Abbreviated breast MRI is a useful supplement to screening mammography in women with a family history of breast cancer. Considering the results of the conducted research, it is recommended to assess whether women with a family history of breast cancer have an increased risk and subsequently provide annual abbreviated breast MRI in addition to mammography for early detection of breast cancer. [ABSTRACT FROM AUTHOR]

Breast cancer mortality reduction progress has halted in recent years. The mortality rate was rising, and breast cancer was the leading cause of death among women. Early diagnosis is critical in treatment since it can prevent complications and heavy pathologic therapy. Many Computer-Aided Diagnosis (CAD) systems were developed for this purpose. However, to produce more accurate findings, it must continue to be enhanced by adopting new methodologies. To efficiently handle semantic segmentation in a predicted image, we propose a novel Fully Convolutional Network (FCN) called DEES-Breast that presents an End-to-End system for an early breast cancer detection from mammographic scans. The DEES-Breast uses an encoder-decoder architecture to identify relevant features from scans at several scales and upsample them to generate the best segmentation results. The main advantage of the proposed architecture is the skip connection mode within the decoder and encoder layers, which merges high-level features encoded with low-level features decoded from the decoder. The CNN used at the encoder tries to admit relevant studies having similar contrast values using thirteen convolutional layers and three fully connected layers. Various complex preprocessing methods were carefully used to enhance the model's performance. These methods included various procedures, such as image cropping, CLAHE enhancement, artifact removal, etc., and allowed us to create a well-prepared dataset for training and testing. Geometric data augmentations were carefully integrated into the pipeline to improve generalization capabilities and reduce overfitting. CBIS-DDSM images and a private database were used to test our suggested architecture comprehensively. Quantitative criteria for evaluating segmentation outcomes, such as Dice coefficient, precision, and recall, are all above 90%, demonstrating that the proposed architecture system can differentiate functional tissues in breast mammogram images. As a result, our proposed architecture has the potential to offer the classification required to aid in the clinical detection of breast cancer while also improving imaging in other modalities of medical mammography. [ABSTRACT FROM AUTHOR]

Extremely dense breasts can be an independent risk factor for breast cancer. A new FDA rule requires that patients be notified of their breast density and the possible benefits of additional imaging to screen for breast cancer. Clinicians should be cognizant of the data about breast cancer risk, breast density, and recommendations to change screening techniques if patients, particularly premenopausal females, have extremely dense breasts but no other known risk factors. [ABSTRACT FROM AUTHOR]

Background: Imaging of the postoperative breast is a challenging issue for the interpreting physician with many variable findings that may require additional assessment through targeted ultrasound, more mammography views, or other investigations. Artificial intelligence (AI) is a fast-developing field with various applications in the breast imaging including the detection and classification of lesions, the prediction of therapy response, and the prediction of breast cancer risk. This study aimed to identify whether Artificial Intelligence improves the mammographic detection and diagnosis of breast post-operative changes and hence improves follow-up and diagnostic workflow and reduces the need for additional exposure to extra radiation or contrast material doses as in Contrast Enhanced Mammography, and the need for interventional procedures as biopsy. Methods: This cross-sectional analytic study included 66 female patients following breast-conserving surgeries coming with breast complaints or for follow-up, with mammographically diagnosed changes. Results: Mammography had a sensitivity of 91.7%, a specificity of 94.4%, a positive predictive value (PPV) of 78.6%, a negative predictive value (NPV) of 98.1%, and an accuracy of 93.9%, while the AI method indices were sensitivity 91.7%, specificity 92.6%, (PPV) 73.3%, (NPV) 98%, and accuracy 92.4%. The calculated cut-off point for the quantitative AI (probability of malignancy "POM" score) was 51.5%. There was a statistically significantly higher average in the percentage of POM in malignant cases (76.5 ± 27.3%) compared to benign cases (27.1 ± 19.7%). However, the final indices for the combined use of mammography and (AI) were sensitivity 100%, specificity 88.9%, (PPV) 66.7%, (NPV) 100%, and accuracy 90.9%. Conclusion: Applying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers. [ABSTRACT FROM AUTHOR]

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]

Introduction: The development and optimization of novel diagnostic imaging prototypes heavily rely on experimental work. In radiology, this experimental work involves the use of phantoms. When testing novel techniques to demonstrate their advantages, anthropomorphic phantoms are utilized. The aim of this study was to investigate seven materials for 3D printing to replicate the radiological properties of breast lesions. Methods: To achieve this objective, we utilized three fused filament fabrication materials, namely, polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PET-G), along with resins such as White v4 Resin, Flexible 80A v1 Resin, Model v2 Resin, and Wax40 v1 Resin, to 3D print seven irregularly shaped lesions. These lesions were used to prepare a set of seven physical phantoms, each filled with either water or liquid paraffin, and one of the printed lesions. The phantoms were then scanned using a mammography unit at 28 kVp. Additionally, six computational breast phantoms, replicating the shape of the physical phantoms, were generated. These computational models were assigned the attenuating properties of various breast tissues, including glandular tissue, adipose tissue, skin, and lesions. Mammography images were generated under the same experimental conditions as the physical scans. Both the simulated and experimental images were evaluated for their contrast-to-noise ratio (CNR) and contrast (C). Discussion: The results indicated that the studied resins and filament-based materials are all suitable for replicating breast lesions. Among these, PLA and White v4 Resin exhibited the densest formations and can effectively approximate breast lesions that are slightly less attenuating than glandular tissue, while ABS and Flexible 80A v1 Resin were the least dense and can represent fat-containing breast lesions. The remaining materials provided good approximations for malignant lesions. These materials can be utilized for constructing phantoms for experimental work, rendering the model a valuable tool for optimizing mammography protocols, ensuring quality control of mammography X-ray equipment, and aiding in the diagnosis and assessment of breast cancer. [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]

Integrating machine learning into medical diagnostics has revolutionized the field, particularly enhancing Computeraided Diagnosis (CAD) systems. These systems assist healthcare professionals by leveraging medical data and machine learning algorithms for more accurate diagnosis and treatment plans. Mammography, an X-ray-based imaging technique, is pivotal in early breast cancer detection, enabling the differentiation between benign and malignant lesions. Recent studies have focused on developing deep learning-enabled mammography CAD systems, which have shown promising results in detecting, segmenting, and classifying anomalies in mammogram images. This comprehensive review presents an innovative system architecture for breast cancer detection, segmentation, and classification using deep learning within mammography CAD systems. It also explores publicly available mammogram datasets and the critical parameters for assessing deep learning system performance. The literature review is meticulously conducted using the PRISMA methodology to evaluate and synthesise novel research findings in this domain. This survey highlights the technological advancements and underlines the potential of deep learning in transforming mammographic analysis for breast cancer detection. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast‐conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post‐operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy. Methods: This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post‐treatment using standardised mean difference (SMD) with Cohen's d, chi‐square, and paired sample t‐tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis. Results: The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3‐ and 15‐months post‐treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post‐treatment for detecting changes in breast density. Conclusions: This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short‐term follow‐up period. However, the results suggest that quantitative density assessment could be valuable for long‐term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the impact of three two-dimensional (2D) mammographic acquisition techniques on image quality and radiation dose in the presence of silicone breast implants (BIs). Then, we propose and validate a new International Atomic Energy Agency (IAEA) phantom to reproduce these techniques. Images were acquired on a single Hologic Selenia Dimensions® unit. The mammography of the left breast of a single clinical case was included. Three methods of image acquisition were identified. They were based on misused, recommended, and reference settings. In the clinical case, image criteria scoring and the signal-to-noise ratio on breast tissue (SNRBT) were determined for two 2D projections and compared between the three techniques. The phantom study first compared the reference and misused settings by varying the AEC sensor position and, second, the recommended settings with a reduced current-time product (mAs) setting that was 13% lower. The signal-difference-to-noise ratio (SDNR) and detectability indexes at 0.1 mm (d' 0.1 mm) and 0.25 mm (d' 0.25 mm) were automatically quantified using ATIA software. Average glandular dose (AGD) values were collected for each acquisition. A statistical analysis was performed using Kruskal–Wallis and corrected Dunn tests (p < 0.05). The SNRBT was 2.6 times lower and the AGD was −18% lower with the reference settings compared to the recommended settings. The SNRBT values increased by +98% with the misused compared to the recommended settings. The AGD increased by +79% with the misused settings versus the recommended settings. The median values of the reference settings were 5.8 (IQR 5.7–5.9), 1.2 (IQR 0.0), 7.0 (IQR 6.8–7.2) and 1.2 (IQR 0.0) mGy and were significantly lower than those of the misused settings (p < 0.03): 7.9 (IQR 6.1–9.7), 1.6 (IQR 1.3–1.9), 9.2 (IQR 7.5–10.9) and 2.2 (IQR 1.4–3.0) mGy for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. A comparison of the recommended and reduced settings showed a reduction of −6.1 ± 0.6% (p = 0.83), −7.7 ± 0.0% (p = 0.18), −6.4 ± 0.6% (p = 0.19) and −13.3 ± 1.1% (p = 0.53) for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. This study showed that the IAEA phantom could be used to reproduce the three techniques for acquiring 2D mammography images in the presence of breast implants for raising awareness and for educational purposes. It could also be used to evaluate and optimize the manufacturer's recommended settings. [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]