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Purpose: Controversy continues regarding the effect of screening mammography on breast cancer outcomes. We evaluated late-stage cancer rate and overall survival (OS) for different screening intervals using a real-world institutional research data mart., Methods: Patients having both a cancer registry record of new breast cancer diagnosis and prediagnosis screening history between 2004 and 2019 were identified from our institutional research breast data mart. Time interval between the two screening mammograms immediately preceding diagnosis and the time to cancer diagnosis were determined. Screening interval was deemed annual if ≤15 months, biennial if >15 and ≤27 months, intermittent if >27 months, and baseline if only one prediagnosis screen was known. The primary end point was late-stage cancer (TNM stage IIB or worse), and the secondary end point was OS. The association of screening interval and late-stage cancer was analyzed using multivariable logistic regression adjusting for prediagnosis characteristics. Proportional hazards regression was used for survival analysis. Potential lead time was analyzed using survival from a uniform fixed time point., Results: In total, 8,145 patients with breast cancer had prediagnosis screening mammography in the timeframe. The percentage of late-stage cancers diagnosed increased significantly with screening interval with 9%, 14%, and 19% late stages for annual, biennial, and intermittent groups ( P < .001), respectively. The trend persisted regardless of age, race, and menopausal status. Biennial and intermittent groups had substantially worse OS than the annual screened group, with relative hazards of 1.42 (95% CI, 1.11 to 1.82) and 2.69 (95% CI, 2.11 to 3.43), respectively, and 1.39 (95% CI, 1.08 to 1.78) and 2.01 (95% CI, 1.58 to 2.55) after adjustment for potential lead time., Conclusion: Annual mammographic screening was associated with lower risk of late-stage cancer and better OS across clinical and demographic subgroups. Our study suggests benefit of annual screening for women 40 years and older.

Objective: Changes in a patient's reported breast density status (dense vs nondense) trigger modifications in their cancer risk profile and supplemental screening recommendations. This study tracked the frequency and longitudinal sequence of breast density status changes among patients who received serial mammograms., Methods: This IRB-approved, HIPAA-compliant retrospective cohort study tracked breast density changes among patients who received at least 2 mammograms over an 8-year study period. BI-RADS density assessment categories A through D, visually determined at the time of screening, were abstracted from electronic medical records and dichotomized into either nondense (categories A or B) or dense (categories C or D) status. A sequence analysis of longitudinal changes in density status was performed using Microsoft SQL., Results: A total of 58 895 patients underwent 231 997 screening mammograms. Most patients maintained the same BI-RADS density category A through D (87.35% [51 444/58 895]) and density status (93.35% [54 978/58 859]) throughout the study period. Among patients whose density status changed, the majority (97% [3800/3917]) had either scattered or heterogeneously dense tissue, and over half (57% [2235/3917]) alternated between dense and nondense status multiple times., Conclusion: Our results suggest that many cases of density status change may be attributable to intra- and interradiologist variability rather than to true underlying changes in density. These results lend support to consideration of automated density assessment because breast density status changes can significantly impact cancer risk assessment and supplemental screening recommendations., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Significance: Optical mammography as a promising tool for cancer diagnosis has largely fallen behind expectations. Modern machine learning (ML) methods offer ways to improve cancer detection in diffuse optical transmission data., Aim: We aim to quantitatively evaluate the classification of cancer-positive versus cancer-negative patients using ML methods on raw transmission time series data from bilateral breast scans during subjects' rest., Approach: We use a support vector machine (SVM) with hyperparameter optimization and cross-validation to systematically explore a range of data preprocessing and feature-generation strategies. We also apply an automated ML (AutoML) framework to validate our findings. We use receiver operating characteristics and the corresponding area under the curve (AUC) to quantify classification performance., Results: For the sample group available ( N = 63 , 18 cancer patients), we demonstrate an AUC score of up to 93.3% for SVM classification and up to 95.0% for the AutoML classifier., Conclusions: ML offers a viable strategy for clinically relevant breast cancer diagnosis using diffuse-optical transmission measurements. The diagnostic performance of ML on raw data can outperform traditional statistical biomarkers derived from reconstructed image time series. To achieve clinically relevant performance, our ML approach requires simultaneous bilateral scanning of the breasts with spatially dense channel coverage., (© 2024 The Authors.)

Objectives: To explore the prevalence of dense breast tissue among screened postmenopausal women and identify the factors influencing breast density in this population., Methods: A retrospective analysis of data from postmenopausal women screened for breast cancer in Jeddah, Saudi Arabia, between April 2017 and June 2021 was carried out. Breast density was subjectively assessed, and influencing factors were retrieved from the hospital information system. Proportions were used for descriptive analysis, and binary logistic regression was used to identify the determinants of dense breast tissue., Results: Only 12.7% of the postmenopausal women had dense breast tissue. Non-Saudi women (odds ratio [OR]=1.95, 95% confidence interval [CI]: [1.07-3.54], p =0.02) and those who did not breastfeed (OR=2.75, 95% CI: [1.33-5.53], p =0.006) had a greater likelihood of having dense breast tissue. Women who had never been pregnant (nulliparous) were 4 times more likely to have dense breast tissue than those who had been pregnant (parous; p <0.001). Additionally, women with fewer children had a higher chance of dense breast tissue (OR=2.58, 95% CI: [1.23-5.40], p =0.01)., Conclusion: The prevalence of dense breast tissue among screened postmenopausal women was low. However, certain factors increase the risk of having dense tissue in this population, including not being Saudi Arabian, never having breastfed, being nulliparous, and having fewer children., (Copyright: © Saudi Medical Journal.)

Objective: To measure the effectiveness of localisation and removal of impalpable target lesions without compromising patient safety in a resource-limited setup using preoperative ultrasound and mammography with peroperative use of C-arm image intensifier., Study Design: Descriptive study. Place and Duration of the Study: Department of Breast Surgery, Ittefaq Hospital (Trust), Lahore, Pakistan, from 25th October 2011 to 17th February 2023., Methodology: All the breast cancer patients who achieved complete clinical response after neoadjuvant systemic treatment and underwent breast conservation surgery during the study period were included. Tumour / clip localisation was done using preoperative ultrasound or image-guided marking, a 2-view mammogram in all cases and the use of an image intensifier to confirm the presence of clips in the excised specimen. The primary outcome was the accurate localisation and removal of the index lesion, while the secondary outcome included the reoperation rate for positive margins and early local recurrence., Results: Data from 144 patients were reviewed. Successful localisation was done in all the patients; only one patient had a positive margin for ductal carcinoma-in situ (DCIS), achieving a 99.3% clear margin rate. Local recurrence within two years after primary operation was seen in one patient only.  Conclusion: By a combined approach of preoperative ultrasound-guided marking, a 2-view mammogram, and the use of image intensifier, successful localisation of an impalpable breast lesion is possible without compromising oncological and aesthetic principles., Key Words: Breast conservation surgery, Localisation, Non-palpable, Margins, Image intensifier.

Objective: The aim of this study was to investigate postoperative imaging findings of patients who underwent breast-conserving surgery for cancer and reconstruction with MegaDerm ® (sheet-type and pellet-type), analyzing false positives and recurrences, using multi-modality images., Materials and Methods: This study included 201 women (age range: 28-81 years, mean age ± standard deviation: 53.2 ± 8.6 years) who underwent breast-conserving surgery and immediate reconstruction with MegaDerm ® . Post-surgery, each patient underwent at least one mammography (MG), ultrasonography (US), and MRI, totaling 713 MG, 1063 US, and 607 MRI examinations. Postoperative images were reviewed separately for the two types of MegaDerm ® , and suspicious imaging findings (false positives and recurrences) were analyzed, with a particular focus on the findings in direct contact with MegaDerm ® ., Results: MegaDerm ® appeared as a circumscribed mass with homogeneous iso- or high density on MG, posterior shadowing on US, and no enhancement on MRI. Calcification was more common and increased in size in sheet-type MegaDerm ® , while pellet-type often exhibited irregular margins. Nine out of 17 false positives had suspicious findings in direct contact with MegaDerm ® , and six out of nine recurrences showed similar findings. Common suspicious findings included calcifications, asymmetries, and MegaDerm ® irregularities on MG; masses and MegaDerm ® irregularities on US; and enhancing masses and MegaDerm ® irregularities with enhancement on MRI. Notably, MegaDerm ® irregularity with calcification was observed on MG and US in only one recurrence case. In 44.4% (4/9) of false-positives in direct contact with MegaDerm ® , suspicious findings showed no change or resolution on follow-up., Conclusion: Suspicious imaging findings in direct contact with MegaDerm ® may be associated with false positives or recurrences. Therefore, it is essential to recognize these characteristic findings and review the patient's history of MegaDerm ® insertion when in doubt., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2024 The Korean Society of Radiology.)

Background/aim: Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer., Patients and Methods: In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant. The AI model learned the malignancy as well as margins and shapes of mass lesions through multi-label training, similar to the diagnostic process of a radiologist. We used the Curated Breast Imaging Subset of Digital Database for Screening Mammography. This dataset includes annotations for mass lesions, and we developed an algorithm to determine the exact location of the lesions for accurate classification. A multi-label classification approach enabled the model to recognize malignancy and lesion attributes., Results: Our multi-label classification model, trained on both lesion shape and margin, demonstrated superior performance compared with models trained solely on malignancy. Gradient-weighted class activation mapping analysis revealed that by considering the margin and shape, the model assigned higher importance to border areas and analyzed pixels more uniformly when classifying malignant lesions. This approach improved diagnostic accuracy, particularly in challenging cases, such as American College of Radiology Breast Imaging-Reporting and Data System categories 3 and 4, where the breast density exceeded 50%., Conclusion: This study highlights the potential of AI in improving the diagnosis of breast cancer. By integrating advanced techniques and modern neural network designs, we developed an AI model with enhanced accuracy for mammographic image analysis., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Opinion Statement: Breast cancer does not wait until a woman reaches her 50's to strike. One in six cases occurs in women between the ages of 40 and 49 and breast cancer is the most prevalent cancer and the leading cause of cancer-related deaths among women under 50 in the United States (10% of breast cancer deaths), emphasizing the urgency of early detection (American Society. 2024). Duffy et al. highlight the vital role of mammography screening in younger women, showing that starting screening at 40 reduces breast cancer mortality, with a consistent absolute reduction over time (Duffy et al. Health Technol Assess. 24(55):1-24, 2020). By starting yearly mammograms at 40, we could see a remarkable 40% reduction in breast cancer deaths (Monticciolo et al. J Am Coll Radiol. 18(9):1280-8, 2021). Screening at age 40 also adds little to the burden of overdiagnosis that already arises from screening at age 50 and older. Comparing this to biennial screening between ages 50-74, yearly screening at 40 saves approximately 13,770 more lives annually according to a report by the American Cancer Society published in JAMA in 2015 (Oeffinger et al. JAMA. 314(15):1599-614, 2015). But it's not just about saving lives; it's also about preserving quality of life. Between ages 40 and 49, 12-15% of years of life lost are attributed to breast cancer, highlighting the impact on women's lives. Early detection through screening can minimize these losses, ensuring more years spent with loved ones. It's clear: starting mammograms at age 40 saves lives. We must prioritize early detection and make screening accessible to all women, regardless of age. This proactive approach can reduce the burden of breast cancer and pave the way for a healthier future for women everywhere., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The following is an overview of the numerous efforts to reduce access for women to breast cancer screening. Misinformation has been promoted over the many years to suggest that screening only works for women aged 50 years and over. In fact, there are no, scientifically derived data, to support the use of the age of 50 years as a threshold for screening. The randomized, controlled trials have proved that screening saves lives for women aged 40 to 74 years (the age of the women who participated)., Competing Interests: Disclosure Royalties from IZI Medical for Kopans localization system. Advisor to DART Imaging in China. Advisor to Malcova Imaging developing CT for the breast., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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 𝜒 2 /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., (© 2024 Japan Society of Obstetrics and Gynecology.)

Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AI first ), the second reader (scenario 2: integrated AI second ), or both readers for triaging of low- and high-risk cases (scenario 3: integrated AI triage ). AI threshold values were chosen based partly on previous validation and setting the screen-read volume reduction at approximately 50% across scenarios. Detection accuracy measures were calculated. Compared with standard double reading, integrated AI first showed no evidence of a difference in accuracy metrics except for a higher arbitration rate (+0.99%, P < .001). Integrated AI second had lower sensitivity (-1.58%, P < .001), negative predictive value (NPV) (-0.01%, P < .001), and recall rate (-0.06%, P = .04) but a higher positive predictive value (PPV) (+0.03%, P < .001) and arbitration rate (+1.22%, P < .001). Integrated AI triage achieved higher sensitivity (+1.33%, P < .001), PPV (+0.36%, P = .03), and NPV (+0.01%, P < .001) but lower arbitration rate (-0.88%, P < .001). Replacing one or both readers with AI seems feasible; however, the site of application in the workflow can have clinically relevant effects on accuracy and workload. Keywords: Mammography, Breast, Neoplasms-Primary, Screening, Epidemiology, Diagnosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. Published under a CC BY 4.0 license.

Purpose: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination., Methods: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype., Results: There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI., Conclusion: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes., (© 2024. The Author(s).)

Background: Height and mammographic breast density are well-known risk factors for breast cancer. This study aims to investigate the association between height and mammographic density with breast cancer risk in a large population-based cohort of Korean women., Methods: This retrospective cohort study included 4,851,115 women ages 40 and older who underwent screening mammography through the Korean National Cancer Screening Program between 2009 and 2014 and were followed up until 2016. Multivariable Cox proportional hazard models were used to estimate adjusted HRs and 95% confidence intervals (CI) for the associations among height, mammographic density, and breast cancer risk., Results: A taller stature was associated with an increased risk of breast cancer, with women in the highest quintile of height (Q5) having a 1.54-fold (95% CI, 1.49-1.59) greater risk than those in the lowest quintile (Q1). When analyzing breast cancer risk based on height and mammographic density, women in the highest quintile for height (Q5) with extremely dense breasts had a 4.51-fold (95% CI, 4.24-4.79) greater risk than those in the lowest quintile (Q1) with almost entirely fatty breasts (Ptrend < 0.001)., Conclusions: This first study to simultaneously examine these two variables found that height and mammographic density were independently associated with breast cancer risk in this large cohort of Korean women., Impact: Height and mammographic density can help stratify risk in screening populations for breast cancer. Careful consideration of screening strategies may be beneficial for taller women with dense breasts., (©2024 American Association for Cancer Research.)

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., (© 2024. The Author(s).)

Purpose/objective: 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., Methods: 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., Results: 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., Conclusion: 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., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background: In the United States, breast cancer remains one of the most diagnosed cancers among females and remains the second leading cause of cancer death. In addition, breast cancer is most likely diagnosed at an advanced stage among Hispanic females in the United States due to lower mammogram utilization., Aims: The objective of this study was to determine the effectiveness of a multilevel, multicomponent community-based breast cancer screening intervention called the Breast Cancer Education Screening and NavigaTion (BEST) program. The primary outcome was the completion of a screening mammogram 4 months post-intervention., Method: We used a pragmatic approach for evaluation, utilizing a quasi-experimental delayed intervention design. We recruited women from the community aged between 50 and 75, uninsured or underinsured, and overdue for screening., Results: Six hundred participants were recruited (300 intervention and 300 control). Among completers, the screening rate was 97% in the intervention group and 4.4% in the control group (RR = 22.2, 95% CI: 12.5-39.7, p < .001). In multivariable analysis, age ≥ 65 (RR = 1.29, p = .047), perceived benefits (RR = 1.04, p = .026), curability (RR = 1.24, p < .001), subjective norms (RR = 1.14, p = .014), and fatalism (RR = .96, p = .004) remained significantly associated with screening outcome., Conclusion: A multicomponent, bilingual, and culturally tailored intervention effectively facilitated breast cancer screening completion in an underserved population of Hispanic women. Individuals with improved screening outcomes were more likely to have higher positive beliefs. Our study has important implications regarding using multicomponent interventions in increasing breast cancer screening completion in poorly screened populations. It also highlights differences in health belief motivation for breast cancer screening completion.

Objective: 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., Methods: 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)., Results: 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%)., Discussion: 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., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background: Population-based genetic screening and testing programmes have substantial potential to improve cancer-related outcomes through early detection and cancer prevention. Yet, genetic testing for cancer risk remains largely underused. This study aimed to describe barriers and facilitators to patient engagement at each stage of a California-based genetic screening programme, from completing the electronic screener to receiving the test and to identify potential improvements that could support precision medicine-based approaches to patient care., Methods: We conducted 26 semistructured interviews among programme participants who did not complete the screener (n=9), those who did not receive the recommended test (n=7) and those who received a genetic test (n=10). Interviewees were selected from patients who recently received a mammogram through one of the participating Southern California clinics. Interviews were transcribed and coded using Atlas.ti. The study used a qualitative descriptive approach to identify similar and contrasting themes among the participant groups., Results: This study found that barriers and facilitators to engagement were largely the same regardless of how far participants had moved through the process towards getting a genetic test. We identified four overarching themes: participants wanted clear communication of personal benefits at each stage; participants needed additional information and knowledge to navigate genetic screening and testing; a trusted provider could be instrumental in participants following a recommendation; and repetition and timing strongly impacted participants' likelihood to engage., Conclusions: Providing education about the benefits of genetic screening and testing to patients and their families, as well as clear communication about what each step entails may help patients engage with similar programmes. Strategies aimed at increasing coordination among a patient's healthcare team can also help ensure information reaches patients in multiple ways, from multiple providers, to increase the likelihood that recommendations for testing come from trusted sources, which supports the uptake of genetic testing., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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 (chi square = 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.

Background: With the rising global incidence and mortality rates of breast cancer, early diagnosis is becoming increasingly crucial. The World Health Organization (WHO) recommends mammography as a primary screening tool. However, despite its clinical benefits, mammography has potential risks including radiation exposure, unnecessary follow-up, and overdiagnosis due to false positives, particularly in cases of early cancer or dense breast tissue. In this study, we aimed to address these concerns by introducing an innovative diagnostic method that employs circulating biomarkers to enhance the existing screening techniques Methods: Breast cancer-derived extracellular vesicles (BEVs) were isolated from the bloodstream using advanced immunoaffinity capture techniques. Subsequently, we analyzed the microRNA (miRNA) profiles of BEVs in plasma samples from 120 patients with breast cancer, 46 with benign tumors, and 45 healthy controls. Results: This retrospective study identified a distinct signature of five EV miRNAs (miR-21, miR-106b, miR-181a, miR-484, and miR-1260b) that effectively differentiated patients with breast cancer from healthy controls. This signature provides essential insights into tumor progression, metastasis, and the risk of recurrence. Notably, overexpression of this signature correlated with poorer survival outcomes. Conclusions: Our novel gene signature-based approach not only complements existing diagnostic methods with high accuracy but also provides a deeper understanding of the molecular aspects of breast cancer, heralding a significant advancement in precision medicine and personalized cancer care., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Objective: To develop a nomogram that integrates clinical-pathologic and imaging variables to predict ipsilateral breast tumor recurrence (IBTR) in women with ductal carcinoma in situ (DCIS) treated with breast-conserving surgery (BCS)., Materials and Methods: This retrospective study included consecutive women with DCIS who underwent BCS at two hospitals. Patients who underwent BCS between 2003 and 2016 in one hospital and between 2005 and 2013 in another were classified into development and validation cohorts, respectively. Twelve clinical-pathologic variables (age, family history, initial presentation, nuclear grade, necrosis, margin width, number of excisions, DCIS size, estrogen receptor, progesterone receptor, radiation therapy, and endocrine therapy) and six mammography and ultrasound variables (breast density, detection modality, mammography and ultrasound patterns, morphology and distribution of calcifications) were analyzed. A nomogram for predicting 10-year IBTR probabilities was constructed using the variables associated with IBTR identified from the Cox proportional hazard regression analysis in the development cohort. The performance of the developed nomogram was evaluated in the external validation cohort using a calibration plot and 10-year area under the receiver operating characteristic curve (AUROC) and compared with the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram., Results: The development cohort included 702 women (median age [interquartile range], 50 [44-56] years), of whom 30 (4%) women experienced IBTR. The validation cohort included 182 women (48 [43-54] years), 18 (10%) of whom developed IBTR. A nomogram was constructed using three clinical-pathologic variables (age, margin, and use of adjuvant radiation therapy) and two mammographic variables (breast density and calcification morphology). The nomogram was appropriately calibrated and demonstrated a comparable 10-year AUROC to the MSKCC nomogram (0.73 vs. 0.66, P = 0.534) in the validation cohort., Conclusion: Our nomogram provided individualized risk estimates for women with DCIS treated with BCS, demonstrating a discriminative ability comparable to that of the MSKCC nomogram., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2024 The Korean Society of Radiology.)

Background/aim: Ductal carcinoma in situ is considered a local disease with no metastatic potential, thus sentinel lymph node biopsy (SLNB) may be deemed an overtreatment. SLNB should be reserved for patients with invasive cancer, even though the risk of upstaging rises to 25 %. We aimed to identify clinicopathological predictors of post-operative upstaging in invasive carcinoma., Methods: We retrospectively analyzed patients with a pre-operative diagnosis of DCIS subjected to breast surgery between January 2017 to December 2021, and evaluated at the Breast Unit of PTV (Policlinico Tor Vergata, Rome)., Results: Out of 267 patients diagnosed with DCIS, 33(12.4 %) received a diagnosis upstaging and 9(3.37 %) patients presented with sentinel lymph node (SLN) metastasis. In multivariate analysis, grade 3 tumor (OR 1.9; 95 % CI 1.2-5.6), dense nodule at mammography (OR 1.3; 95 % CI 1.1-2.6) and presence of a solid nodule at ultrasonography (OR 1.5; 95 % CI 1.2-2.6) were independent upstaging predictors. Differently, the independent predictors for SLNB metastasis were: upstaging (OR 2.1.; 95 % CI 1.2-4.6; p = 0.0079) and age between 40 and 60yrs (OR 1.4; 95 % CI 1.4-2.7; p = 0.027). All 9 patients with SLN metastasis received a diagnosis upstaging and were aged between 40 and 60 years old., Conclusion: We identified pre-operative independent predictors of upstaging to invasive ductal carcinoma. The combined use of different predictors in an algorithm for surgical treatments of DCIS could reduce the numbers of unnecessary SLNB., Competing Interests: Declaration of competing interest All the authors declare that they have no potential conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: False-positive results on screening mammography may affect women's willingness to return for future screening., Objective: To evaluate the association between screening mammography results and the probability of subsequent screening., Design: Cohort study., Setting: 177 facilities participating in the Breast Cancer Surveillance Consortium (BCSC)., Patients: 3 529 825 screening mammograms (3 184 482 true negatives and 345 343 false positives) performed from 2005 to 2017 among 1 053 672 women aged 40 to 73 years without a breast cancer diagnosis., Measurements: Mammography results (true-negative result or false-positive recall with a recommendation for immediate additional imaging only, short-interval follow-up, or biopsy) from 1 or 2 screening mammograms. Absolute differences in the probability of returning for screening within 9 to 30 months of false-positive versus true-negative screening results were estimated, adjusting for race, ethnicity, age, time since last mammogram, BCSC registry, and clustering within women and facilities., Results: Women were more likely to return after a true-negative result (76.9% [95% CI, 75.1% to 78.6%]) than after a false-positive recall for additional imaging only (adjusted absolute difference, -1.9 percentage points [CI, -3.1 to -0.7 percentage points]), short-interval follow-up (-15.9 percentage points [CI, -19.7 to -12.0 percentage points]), or biopsy (-10.0 percentage points [CI, -14.2 to -5.9 percentage points]). Asian and Hispanic/Latinx women had the largest decreases in the probability of returning after a false positive with a recommendation for short-interval follow-up (-20 to -25 percentage points) or biopsy (-13 to -14 percentage points) versus a true negative. Among women with 2 screening mammograms within 5 years, a false-positive result on the second was associated with a decreased probability of returning for a third regardless of the first screening result., Limitation: Women could receive care at non-BCSC facilities., Conclusion: Women were less likely to return to screening after false-positive mammography results, especially with recommendations for short-interval follow-up or biopsy, raising concerns about continued participation in routine screening among these women at increased breast cancer risk., Primary Funding Source: National Cancer Institute., Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M24-0123.

Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular and readily available imaging modality for breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammogram. Processed mammograms are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 - 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95%CI 0.998 - 0.998] . Finally, for a subset of 100 mammograms with a malignant mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95%CI 0.73 - 0.87] for consistency and 0.78 [95%CI 0.66 - 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth. The algorithm may play a role in lesion characterization and breast cancer prognostication on mammograms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Rationale and Objectives: Integrating learning spacing in medicine has shown promise in enhancing knowledge retention and diagnostic proficiency. While studies demonstrate the effectiveness of spaced learning in various fields, limited research exists on its application in radiological training. This study aims to investigate the impact of intervals in spaced training on radiologists' and trainees' diagnostic performance via mammogram test sets., Methods: 54 radiologists and 101 radiology trainees completed 207 and 458 first-time readings of 9 mammogram test sets between 2019 and 2023. Each test set comprised of 60 mammograms (20 cancer and 40 normal), sourced retrospectively from BreastScreen Australia. Each radiologist evaluated mammograms using the BIRADS lexicon. Readers' performance was compared with truth data and evaluated in terms of specificity, case sensitivity, lesion sensitivity, ROC AUC and JAFROC FOM. The progress of readers' performances in following test sets after the first one was analyzed using Wilcoxon Signed Rank test. The association of participants' performances and the intervals among test sets' completions was investigated using Pearson's test., Results: Significant positive correlations were found between intervals and radiologists' improvement in specificity and JAFROC FOM (P < 0.05). The separation of 4 to 10 days showed the most improvement among radiologists across all metrics, while intervals exceeding 90 days related to highest increase in case sensitivity (5.15%), lesion sensitivity (6.55%), ROC AUC (3.05%) and JAFROC FOM (6.3%). Trainees completing test sets in one day showed positive correlations with their ROC AUC (R=0.45; P = 0.008) and JAFROC FOM (R=0.43; P = 0.02), while those taking a longer time to complete showed negative impacts on case sensitivity (P = 0.009) and ROC AUC (P = 0.02). Remarkable progress in trainees was found in case sensitivity (6.15%), lesion sensitivity (11.6%), ROC AUC (3.5%) and JAFROC FOM (4.35%) with test set intervals of 31-90 days., Conclusions: Radiologists demonstrated superior performance when the training test sets were spaced over longer intervals, whereas trainees exhibited proficiency with shorter time separations. By optimizing the spacing of reviewing and practicing radiological concepts, mammogram readers can bolster memory retention and diagnostic decision-making skills., Competing Interests: Declaration of Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing Interests All authors have no conflicts of interest related to the work described in the study., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: To explore the association between radiologists' interpretation scores, early performance measures and cumulative reading volume in mammographic screening., Method: We analyzed 1,689,731 screening examinations (3,379,462 breasts) from BreastScreen Norway 2012-2020, all breasts scored 1-5 by two independent radiologists. Score 1 was considered negative/benign and score ≥2 positive in this scoring system. We performed descriptive analyses of recall, screen-detected cancer, positive predictive value (PPV) 1, mammographic features and histopathological characteristics by breast-based interpretation scores, and cumulative reading volume by examination-based interpretation scores., Results: Counting breasts and not women, 3.9 % (132,570/3,379,462) had a score of ≥2 by one or both radiologists. Of these, 84.8 % (112,440/132,570) were given a maximum score 2. Total recall rate was 1.6 % (53,735/3,379,462), 69.3 % (37,220/53,735) given maximum score 2. Among the 0.3 % (9733/3,379,462) diagnosed with screen-detected cancer, 34.6 % (3369/9733) had maximum score 3. The percentages of recall, screen-detected cancer and PPV-1 increased by increasing the sum of scores assigned by two radiologists (p < 0.001 for trend). Higher proportions of masses were observed among recalls and screen-detected cancers with low scores, and higher proportions of spiculated masses were observed for high scores (p < 0.001). Proportions of invasive carcinoma, histological grade 3 and lymph node positive tumors were higher for high versus low scores (p < 0.001). The proportion of examinations scored 1 increased by cumulative reading volume., Conclusions: We observed higher rates of recall and screen-detected cancer and less favorable histopathological tumor characteristics for high versus low interpretation scores. However, a considerable number of recalls and screen-detected cancers had low interpretation scores., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tone Hovda: None. Silje Sagstad: None. Nataliia Moshina: None. Einar Vigeland: None. Solveig Hofvind: Head of BreastScreen Norway, but has permanent employment as a researcher at the Cancer Registry of Norway, independent of her job as administrative leader of BreastScreen Norway., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

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., (© 2024. The Author(s).)

Purpose: Breast cancer is an important health problem, like obesity and dyslipidemia, with a strong association between body mass index (BMI) and breast cancer incidence and mortality. The risk of breast cancer is also high in women with high mammographic breast density (MBD). The purpose of this study was to analyze the association between BMI and MBD according to breast cancer molecular subtypes., Methods: This transversal, descriptive, multicenter study was conducted at three Spanish breast cancer units from November 2019 to October 2020 in women with a recent diagnosis of early breast cancer. Data were collected at the time of diagnosis., Results: The study included 162 women with a recent diagnosis of early breast cancer. The median age was 52 years and 49.1% were postmenopausal; 52% had normal weight, 32% overweight, and 16% obesity. There was no association between BMI and molecular subtype but, according to menopausal status, BMI was significantly higher in postmenopausal patients with luminal A (p = 0.011) and HER2-positive (p = 0.027) subtypes. There was no association between MBD and molecular subtype, but there were significant differences between BMI and MBD (p < 0.001), with lower BMI in patients with higher MBD. Patients with higher BMI had lower HDL-cholesterol (p < 0.001) and higher insulin (p < 0.001) levels, but there were no significant differences in total cholesterol or vitamin D., Conclusions: This study showed higher BMI in luminal A and HER2-positive postmenopausal patients, and higher BMI in patients with low MBD regardless of menopausal status., (© 2024. The Author(s).)

Background: High-risk programs provide recommendations for surveillance/risk reduction for women at elevated risk for breast cancer development. This study evaluated the impact of high-risk surveillance program participation on clinicopathologic breast cancer features at the time of diagnosis., Methods: Women followed in the authors' high-risk program (high-risk cohort [HRC]) with a diagnosis of breast cancer from January 2015 to June 2021 were identified and compared with the general population of women undergoing breast cancer surgery at Memorial Sloan Kettering Cancer Center (MSK; general cohort [GC]) during the same period. Patient and tumor factors were collected. Clinicopathologic features were compared between the two cohorts and in a subset of women with a family history of known BRCA mutation., Results: The study compared 255 women in the HRC with 9342 women in the GC. The HRC patients were slightly older and more likely to be white and have family history than the GC patients. The HRC patients also were more likely to present with DCIS (41 % vs 23 %; p < 0.001), to have smaller invasive tumors (pT1: 100 % vs 77 %; p < 0.001), and to be pN0 (95 % vs 81 %; p < 0.001). The HRC patients had more invasive triple-negative tumors (p = 0.01) and underwent less axillary surgery (p < 0.001), systemic therapy (p < 0.001), and radiotherapy (p = 0.002). Among those with a known BRCA mutation, significantly more women in the HRC underwent screening mammography (75 % vs 40 %; p < 0.001) or magnetic resonance imaging (MRI: 82 % vs 9.9 %; p < 0.001) in the 12 months before diagnosis., Conclusions: Women followed in a high-risk screening program have disease diagnosed at an earlier stage and therefore require less-intensive breast cancer treatment than women presenting to a cancer center at the time of diagnosis. Identification of high-risk women and implementation of increased surveillance protocols are vital to improving outcomes., (© 2024. Society of Surgical Oncology.)

Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18-45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting architectural distortion in mammography images by combining preprocessing methods and a novel structure fusion attention model. The proposed structure-focused weighted orientation preprocessing method is composed of the original image, the architecture enhancement map, and the weighted orientation map, highlighting suspicious AD locations. The proposed structure fusion attention model captures the information from different channels and outperforms other models in terms of false positives and top sensitivity, which refers to the maximum sensitivity that a model can achieve under the acceptance of the highest number of false positives, reaching 0.92 top sensitivity with only 0.6590 false positive per image. The findings suggest that the combination of preprocessing methods and a novel network architecture can lead to more accurate and reliable AD detection. Overall, the proposed approach offers a novel perspective on detecting ADs, and we believe that our method can be applied to clinical settings in the future, assisting radiologists in the early detection of ADs from mammography, ultimately leading to early treatment of breast cancer patients., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Introduction: Digital breast tomosynthesis (DBT) may improve sensitivity in population screening. However, evidence is currently limited on the performance of DBT in patients at a higher risk of breast cancer. This systematic review compares the clinical effectiveness and cost-effectiveness of DBT, digital mammography (DM), and ultrasound, for breast cancer detection in women with dense breasts and additional risk factors., Methods: Medline, Embase, and Evidence-Based Medicine Reviews via OvidSP were searched to identify literature from 2010 to August 21, 2023. Selection of studies, data extraction, and quality assessment (using QUADAS-2 and CHEERS) were completed in duplicate. Findings were summarised descriptively and narratively., Results: Twenty-six studies met pre-specified inclusion criteria. In women with breast symptoms or recalled for investigation of screen-detected findings (19 studies), DBT may be more accurate than DM. For example, in symptomatic women, the sensitivity of DBT + DM ranged from 82.8 % to 92.5 % versus 56.8 %-81.3 % for mammography (DM/synthesised images). However, most studies had a high risk of bias due to participant selection. Evidence regarding DBT in women with a personal or family history of breast cancer, for DBT versus ultrasound alone, and cost-effectiveness of DBT was limited., Conclusions: In women with dense breasts and additional risk factors for breast cancer, evidence is limited about the accuracy of DBT compared to other imaging modalities, particularly in those with personal or family history of breast cancer. Future research in this population should consider head-to-head comparisons of imaging modalities to determine the relative effectiveness of these imaging tests., Systematic Review Registration: PROSPERO registration number CRD42021236470., Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2024. The Author(s).)

Previous studies have reported volatile organic compounds (VOCs) in the breath as biomarkers of breast cancer. These biomarkers may be derived from cancer-associated fibroblasts, in which oxidative stress degrades polyunsaturated fatty acids to volatile alkanes and methylated alkane derivatives that are excreted in the breath. We evaluated a rapid point-of-care test for breath VOC biomarkers as predictors of breast cancer and abnormal mammograms. We studied 593 women aged⩾18 yr referred to three sites for mammography for a symptomatic breast-related concern (e.g. breast mass, nipple discharge). A rapid point-of-care breath testing system collected and concentrated alveolar breath VOCs on a sorbent trap and analyzed them with gas chromatography and surface acoustic wave detection in <6 min. Breath VOC chromatograms were randomly assigned to a training set or to a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer and abnormal mammograms in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. Prediction of breast cancer: 50 women had biopsy-proven breast cancer (invasive cancer 41, ductal non-invasive cancer 9) Unsplit data set: breath VOCs identified breast cancer with 83% accuracy (area under curve of receiver operating characteristic), 82% sensitivity and 77.1% specificity. Split data sets: training set breath VOCs identified breast cancer with 80.3% accuracy, 84% sensitivity and 74.3% specificity. Corresponding values in the validation set were 68%% accuracy, 72.4% sensitivity and 61.5% specificity. Prediction of BIRADS 4 and 5 mammograms (versus BIRADS 1, 2 and 3): unsplit data set: breath VOCs identified abnormal mammograms with 76.2% accuracy. Split data sets: breath VOCs identified abnormal mammograms with 74.2% accuracy, 73.3% sensitivity and 60% specificity. Corresponding values in the validation set were 60.5% accuracy, 64.2% sensitivity and 51% specificity. A rapid point-of-care test for breath VOC biomarkers predicted risk of breast cancer and abnormal mammograms in women with breast-related symptoms., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Background: Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse., Main Findings: Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer., Conclusion: This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies., (© 2024. The Author(s).)

The associations of certain factors, such as age and menopausal hormone therapy, with breast cancer risk are known to differ for interval and screen-detected cancers. However, the extent to which associations of other established breast cancer risk factors differ by mode of detection is unclear. We investigated associations of a wide range of risk factors using data from a large UK cohort with linkage to the National Health Service Breast Screening Programme, cancer registration, and other health records. We used Cox regression to estimate adjusted relative risks (RRs) and 95% confidence intervals (CIs) for associations between risk factors and breast cancer risk. A total of 9421 screen-detected and 5166 interval cancers were diagnosed in 517,555 women who were followed for an average of 9.72 years. We observed the following differences in risk factor associations by mode of detection: greater body mass index (BMI) was associated with a smaller increased risk of interval (RR per 5 unit increase 1.07, 95% CI 1.03-1.11) than screen-detected cancer (RR 1.27, 1.23-1.30); having a first-degree family history was associated with a greater increased risk of interval (RR 1.81, 1.68-1.95) than screen-detected cancer (RR 1.52, 1.43-1.61); and having had previous breast surgery was associated with a greater increased risk of interval (RR 1.85, 1.72-1.99) than screen-detected cancer (RR 1.34, 1.26-1.42). As these differences in associations were relatively unchanged after adjustment for tumour grade, and are in line with the effects of these factors on mammographic density, they are likely to reflect the effects of these risk factors on screening sensitivity., (© 2024 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Secretory carcinoma is a rare, low-grade, special histological type of invasive breast carcinoma. Although it is the most common primary breast cancer in the pediatric population, most cases are diagnosed in adults, with a median age of 48 years (range 3 to 91 years). It most often presents as a painless and slowly growing palpable lump. Imaging findings are nonspecific. Secretory carcinomas have abundant periodic acid-Schiff positive intracytoplasmic and extracellular secretions on histopathology. Nearly all secretory carcinomas have mild to moderate nuclear pleomorphism with low mitotic activity. Over 80% (86/102) of secretory carcinomas display the translocation of t(12;15)(p13;q25), resulting in ETV6::NTRK3 gene fusion. Secretory carcinoma generally has an indolent course and has a better prognosis and overall survival than invasive breast carcinoma of no special type. A good prognosis is associated with age <20 years, tumor size <2 cm, and ≤3 axillary lymph node metastases. Metastases beyond the ipsilateral axillary lymph nodes are rare, with the most common sites involving the lung and liver. Except for the potential addition of targeted drug therapy for NTRK fusion-positive tumors, the treatment approach is otherwise similar to invasive breast carcinomas of similar receptor status., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Breast Imaging.)

Breast radiologists encounter unusual lesions, which may not be well described in the literature. Previously based on histologic and molecular classifications, the World Health Organization (WHO) classification of tumors has become increasingly multidisciplinary. Familiarity with imaging features and basic pathology of infrequent breast lesions, as well as their current classification according to the WHO, may help the radiologist evaluate biopsy results for concordance and help direct the management of uncommon breast lesions. This review article provides a case-based review of imaging features and WHO histologic classification of rare breast tumors., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Dedicated breast CT is an imaging modality that provides true 3D imaging of the breast with many advantages over current conventional breast imaging modalities. The addition of intravascular contrast increases the sensitivity of breast CT substantially. As such, there are immediate potential applications in the clinical workflow. These include using breast CT to replace much of the traditional diagnostic workup when faced with indeterminate breast lesions. Contrast-enhanced breast CT may be appropriate as a supplemental screening tool for women at high risk of breast cancer, similar to breast MRI. In addition, emerging studies are demonstrating the utility of breast CT in neoadjuvant chemotherapy tumor response monitoring as well as planning for surgical treatment options. While short exam times and fully 3D imaging in a noncompressed position are advantages of this modality, limited coverage of chest wall/axilla due to prone positioning and use of ionizing radiation are drawbacks. To date, several studies have reported on the performance characteristics of this promising modality., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

South Asians are a rapidly growing subset of the Asian population in the United States. They comprise people from multiple countries with diverse beliefs, languages, and cultural identities and values. The incidence of breast cancer is rising in South Asian women in the United States, with earlier onset and predilection for HER2-enriched tumors. Despite the rising incidence of breast cancer, participation in screening remains lower than other populations. Health care inequities in South Asian women are multifactorial and may be due to traditional health beliefs and practices, language barriers, cultural differences, and lack of overall awareness. Developing a culturally sensitive environment in breast imaging clinic practice can lead to improved patient care and adherence. Given the scarcity of data specific to the South Asian population in United States, there is a need for health service researchers and practice leaders to obtain more high-quality data to understand the needs of South Asian patient populations., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Objective: To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography., Data Source: Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms "Artificial Intelligence," "Mammography," and their respective MeSH terms. We filtered publications from the past ten years (2014 - 2024) and in English., Study Selection: A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work., Data Collection: The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity., Data Synthesis: It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone., Conclusion: AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals., Competing Interests: Conflicts to interest: none to declare.

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer., (© 2024. The Author(s).)