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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.

Background: Theoretically, endocrine fluctuations occurring during infertility treatments, including ovulation induction (OI) and assisted reproductive techniques (ART), could be associated with an increased risk of benign breast diseases (BBDs). To date, no studies have been conducted on this association. Therefore, the present study investigated the association between different types of infertility treatments and BBDs., Methods: This case‒control study was conducted in Arash Women's Hospital, Tehran, Iran. The case group included infertile women diagnosed with BBDs without atypia, and the control group included infertile women without breast disease. Breast imaging studies (mammography/ultrasound) were performed for BBD screening, and the diagnosis was confirmed by histopathological examination. Study variables were collected retrospectively from medical records, hospital databases, and questionnaires., Results: Finally, 154 infertile women, including 50 cases (BBDs) and 104 controls (no BBDs), were compared. Our data showed that 66% of cases and 61.4% of controls had undergone at least one course of infertility treatment. There was no association between BBD risk and previous infertility treatments (OR = 1.21; 95% CI = 0.59-2.46), ART (OR = 1.14; 95% CI = 0.90-1.44), or OI cycles (OR = 1.13; 95% CI = 0.98-1.32). Stratification by confounding variables did not change these results., Conclusions: It seems that there is no association between BBDs in infertile women and the type, duration, or number of prior infertility treatments; however, considering the small sample size of the study, the clinical significance of this finding should not be neglected. Therefore, we consider it essential to carry out more extensive, detailed, and prospective studies to distinguish the association of BBDs with different infertility treatments and medications., (© 2024. The Author(s).)

Purpose: To examine the discrepancy in breast density assessments by radiologists, LIBRA software, and AI algorithm and their association with breast cancer risk., Methods: Among 74,610 Korean women aged ≥ 34 years, who underwent screening mammography, density estimates obtained from both LIBRA and the AI algorithm were compared to radiologists using BI-RADS density categories (A-D, designating C and D as dense breasts). The breast cancer risks were compared according to concordant or discordant dense breasts identified by radiologists, LIBRA, and AI. Cox-proportional hazards models were used to determine adjusted hazard ratios (aHRs) [95% confidence intervals (CIs)]., Results: During a median follow-up of 9.9 years, 479 breast cancer cases developed. Compared to the reference non-dense breast group, the aHRs (95% CIs) for breast cancer were 2.37 (1.68-3.36) for radiologist-classified dense breasts, 1.30 (1.05-1.62) for LIBRA, and 2.55 (1.84-3.56) for AI. For different combinations of breast density assessment, aHRs (95% CI) for breast cancer were 2.40 (1.69-3.41) for radiologist-dense/LIBRA-non-dense, 11.99 (1.64-87.62) for radiologist-non-dense/LIBRA-dense, and 2.99 (1.99-4.50) for both dense breasts, compared to concordant non-dense breasts. Similar trends were observed with radiologists/AI classification: the aHRs (95% CI) were 1.79 (1.02-3.12) for radiologist-dense/AI-non-dense, 2.43 (1.24-4.78) for radiologist-non-dense/AI-dense, and 3.23 (2.15-4.86) for both dense breasts., Conclusion: The risk of breast cancer was highest in concordant dense breasts. Discordant dense breast cases also had a significantly higher risk of breast cancer, especially when identified as dense by either AI or LIBRA, but not radiologists, compared to concordant non-dense breast cases., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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.)

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).)

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.

The extent and determinants of supplemental screening among women with dense breasts are unclear. We evaluated a retrospective cohort of 498,855 women aged 40-74 years with heterogeneously or extremely dense breasts who obtained 1,176,251 negative screening mammography examinations during 2011-2019 in the United States. Overall, 2.8% and 0.3% of mammograms had supplemental ultrasound or MRI within one year, respectively. Onsite availability was associated with ultrasound (odds ratio [OR]=4.35; 95%CI : 4.21-4.49) but not MRI (OR = 0.94; 95%CI : 0.85-1.04). Facility academic affiliation and for-profit status were inversely associated with supplemental ultrasound (OR = 0.53; 95%CI : 0.49-0.57 and OR = 0.83; 95%CI : 0.81-0.86, respectively) and positively associated with supplemental MRI (OR = 3.04; 95%CI : 2.86-3.46 and OR = 1.88; 95%CI : 1.66-2.12, respectively). Supplemental screening was more likely to occur after passage of state-specific density notification laws than before passage (OR = 3.56; 95%CI 3.30-3.84 and OR = 1.79; 95%CI 1.60-2.00, respectively). These results show that supplemental breast imaging utilization has been uncommon and was related to facility factors and density legislation., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented., Methods: This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared., Results: The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments., Conclusion: Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Breast Imaging.)

Background: Breast cancer morbidity has been increasing worldwide, but treatments are improving. The therapeutic response depends on the stage at which the disease is diagnosed. Therefore, early diagnosis has never been more essential for successful treatment and a reduction in mortality rates. Radiology plays a pivotal role in cancer detection, and advances in ultrasound (US) palpation have shown promising results for breast cancer imaging. The addition of two-dimensional-shear wave elastography (2D-SWE) US in the routine breast imaging exam can increase early cancer detection and promote better surveillance., Aim: To evaluate the clinical applications of 2D-SWE US in breast cancer detection and its combination with other imaging modalities., Methods: The 200 consecutive female patients aged 50-80 were examined to evaluate palpable breast lesions. All patients underwent mammography, bright mode (B-mode) US, and 2D-SWE followed by US-guided biopsy in two consecutive sessions., Results: Combining B-mode and shear wave US imaging with X-ray mammography revealed 100% of the suspicious lesions, resulting in greater sensitivity, specificity, and negative predictive value. The result improves compared to either B-mode or 2D-SWE alone ( P = 0.02)., Conclusion: Combining 2D-SWE with conventional US and X-ray techniques improves the chance of early cancer detection. Including 2D-SWE in regular breast imaging routines can reduce the need for biopsies and improve the chance of early cancer detection and survivability with the proper line of therapy., Competing Interests: Conflict-of-interest statement: The authors declare no conflicts of interest., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Aims: To investigate the potential lead time bias of the evaluation model (extended follow-up for women diagnosed with breast cancer) used to evaluate mammography screening in a recent Danish study. This model was compared with two traditional models., Methods: We retrieved data on women diagnosed with breast cancer in each county of Norway from 1986 to 2016. In a population-based open cohort study, the change in incidence-based mortality (IBM) was estimated by relative rate ratios comparing a screening period with a historical period for each of three age groups: women eligible for screening and younger and older ineligible women. We applied the evaluation model, and for comparison two traditional IBM models from a recent Norwegian study: one without extended follow-up and no possibility of lead time bias and one with extended follow-up irrespective of diagnosis, possibly diluting any screening effect., Results: The evaluation model estimated an extra 11% reduction in breast cancer mortality among the screening eligible relative to ineligible women. However, this result could largely be ascribed to lead time bias inflated by overdiagnosis and a decreasing mortality from other causes among eligible women. A reduction in breast cancer mortality was observed for both eligible and younger and older ineligible women across models, and relative rate ratios close to 1 were obtained using the two traditional IBM models, indicating no effect of screening on breast cancer mortality., Conclusions: Two models without lead time bias found no reduction in breast cancer mortality, whereas the evaluation model estimated a reduction attributable to lead time bias ., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC)., Materials and Methods: Women who underwent both mammography and thoracic computed tomography (CT) from 2009 to 2018 were retrospectively included in this single-center study. Deep learning-based software was used to automatically detect and quantify BAC with a BAC AI score ranging from 0 to 10-points. Results were compared using Spearman correlation test with a previously described BAC manual score based on radiologists' visual quantification of BAC on the mammogram. Coronary artery calcification (CAC) score was manually scored using a 12-point scale on CT. The diagnostic performance of the marked BAC AI score (defined as BAC AI score ≥ 5) for the detection of marked CAC (CAC score ≥ 4) was analyzed in terms of sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUC)., Results: A total of 502 women with a median age of 62 years (age range: 42-96 years) were included. The BAC AI score showed a very strong correlation with the BAC manual score (r = 0.83). Marked BAC AI score had 32.7 % sensitivity (37/113; 95 % confidence interval [CI]: 24.2-42.2), 96.1 % specificity (374/389; 95 % CI: 93.7-97.8), 71.2 % positive predictive value (37/52; 95 % CI: 56.9-82.9), 83.1 % negative predictive value (374/450; 95 % CI: 79.3-86.5), and 81.9 % accuracy (411/502; 95 % CI: 78.2-85.1) for the diagnosis of marked CAC. The AUC of the marked BAC AI score for the diagnosis of marked CAC was 0.64 (95 % CI: 0.60-0.69)., Conclusion: The automated BAC AI score shows a very strong correlation with manual BAC scoring in this external validation cohort. The automated BAC AI score may be a useful tool to promote the integration of BAC into mammography reports and to improve awareness of a woman's cardiovascular risk status., Competing Interests: Declaration of competing interest The deep learning software have been developed by iCAD. No financial support was received to conduct the study. A research agreement for the use of the software was signed prior to the study. Data collection and data analysis were performed by the authors. The final version of the submitted manuscript was read by iCAD representatives before submission, to ensure that it did not contain any confidential information., (Copyright © 2024. Published by Elsevier Masson SAS.)

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.)

Background: 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., Methods: 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., Results: 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., Conclusions: 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., (© 2024 The American Geriatrics Society.)

Objective: To identify patients with atypical ductal hyperplasia (ADH) at low risk of upgrading to carcinoma. This study aims to assess the performance of radiomics combined with clinical factors to predict occult breast cancer among women diagnosed with ADH., Methods: This study retrospectively included microcalcification clusters of patients who underwent Mx and VABB with a diagnosis of ADH at a tertiary center from January 2015 to May 2023. Clinical and radiological data (age, cluster size, BI-RADS classification, mammographic density, breast cancer history, residual microcalcifications) were collected. Surgical outcomes were used to determine upgrade. Four logistic regression models were developed to predict the risk of upgrade. The performance was evaluated using the area under the receiver operating characteristic curve (AUC) and performance scores., Results: A total of 143 patients with 153 clusters were included. Twelve radiomic features and six clinical factors were selected for model development. The sample was split into 107 training and 46 test cases. Clinical features achieved an AUC of 0.72 (0.60-0.84), radiomic features an AUC of 0.73 (0.61-0.85). Radiomic features with "cluster size" and "age" improved the AUC to 0.79 (0.67-0.91). The best model, incorporating all data, achieved an AUC of 0.82 (0.71-0.92), a specificity of 0.89 (0.75, 0.97), and NPV of 0.92 (0.78-0.98)., Conclusion: This study demonstrates the potential of radiomic as a valuable tool for reducing unnecessary treatments for patient classified as "low risk of ADH upgrade". Combining radiomic information with clinical data improved the accuracy of risk prediction., 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. Published by Elsevier B.V.)

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, and MRI. Independent predictors were identified using LASSO logistic regression, and a predictive risk model 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xie and Zhang.)

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.

Purpose: Neoadjuvant chemotherapy is now a common first line therapy for breast cancer. International guidelines recommend placement of a clip before commencement of therapy to assist with localizing the tumor bed in the event of excellent response-this takes up time and resources. The microcalcifications associated usually persist after chemotherapy and could serve as an alternative marker. We investigated to determine prognostic criteria to avoid the need for a marker clip before neoadjuvant chemotherapy for breast tumors associated with microcalcifications., Methods: We performed a 7 year single-center bi-site retrospective analytical observational study of 88 women with calcified breast carcinoma treated by neoadjuvant chemotherapy at our bi-site institution between September 2015 and September 2022. This study includied two groups (clip-free tumor localization vs. clip-free tumor non-localization), and investigating quantitative and qualitative predictive factors. The clip-free tumor localization after neoadjuvant chemotherapy was defined by the visibility of residual calcifications on both views of the pre-operative mammogram on the day of or the day prior to surgery., Results: The mean age of the 88 women included in our population was 52.8 years (± 12.7 years standard deviation). Of the 90 tumors with microcalcifications, 64 carcinomas (71.1%) were localizable with no marker clip after neoadjuvant chemotherapy. The main predictive factors significantly associated with clip-free tumor localization were number of calcifications > 10 (P < 0.0001), grade 2 tumor (P = 0.003) with a probability of locating tumor after neoadjuvant chemotherapy of 97.9%, 95% CI [95.6; 99.0]., Conclusion: More than 10 microcalcifications in a grade 2 breast tumor at the initial diagnosis may obviate the need for a marker clip., (© 2024. The Author(s).)

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.

Background: 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., Purpose: 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., Method: 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., Results: 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)., Conclusion: 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., (© 2024 Wiley Periodicals LLC.)

This study aims to investigate whether global mammographic radiomic features (GMRFs) can distinguish hardest- from easiest-to-interpret normal cases for radiology trainees (RTs). Data from 137 RTs were analysed, with each interpreting seven educational self-assessment test sets comprising 60 cases (40 normal and 20 cancer). The study only examined normal cases. Difficulty scores were computed based on the percentage of readers who incorrectly classified each case, leading to their classification as hardest- or easiest-to-interpret based on whether their difficulty scores fell within and above the 75th or within and below the 25th percentile, respectively (resulted in 140 cases in total used). Fifty-nine low-density and 81 high-density cases were identified. Thirty-four GMRFs were extracted for each case. A random forest machine learning model was trained to differentiate between hardest- and easiest-to-interpret normal cases and validated using leave-one-out-cross-validation approach. The model's performance was evaluated using the area under receiver operating characteristic curve (AUC). Significant features were identified through feature importance analysis. Difference between hardest- and easiest-to-interpret cases among 34 GMRFs and in difficulty level between low- and high-density cases was tested using Kruskal-Wallis. The model achieved AUC = 0.75 with cluster prominence and range emerging as the most useful features. Fifteen GMRFs differed significantly (p < 0.05) between hardest- and easiest-to-interpret cases. Difficulty level among low- vs high-density cases did not differ significantly (p = 0.12). GMRFs can predict hardest-to-interpret normal cases for RTs, underscoring the importance of GMRFs in identifying the most difficult normal cases for RTs and facilitating customised training programmes tailored to trainees' learning needs., (© 2024. Crown.)

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., (© 2024. The Author(s).)

Background: Conventional mammography remains the primary imaging modality for state-of-the-art breast imaging practice and its benefit (both on diagnostic and screening) was largely reported. In mammography, the typical Mean Glandular Dose (MGD) from X-ray radiation to the breast spans, on average, from 1 to 10 mGy, depending on breast thicknesses, percentage of fibroglandular tissue, and on the examination purpose., Methods: The aim of this narrative review is to describe the extent of radiation risk in X-ray breast imaging and discuss the main steps and parameters (e.g. MGD, screening frequency and number of examination views) that may have an influence on the radiation risk assessment., Results: Even though the radiation doses used with these examinations are very low, as compared to other medical or natural radiation exposures, there is a non-negligible associated risk of radiation-induced cancer. Accurate radiation risk assessment permits to better balance the overall estimation of the benefit-to-risk ratio in X-ray breast imaging., Conclusions: It is expected that a better knowledge about radiation-induced cancer risk among population could improve the communications skills between patients and clinicians and could help to increase the awareness in women about radiation risk perception for a transparent and proper informed choice of imaging exam., 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: The Kaiser score (KS) is a simple and intuitive machine-learning derived decision rule for characterizing breast lesions in a clinical setting and screening for breast cancer. The present study aims to investigate the applicability of the KS for contrast-enhanced mammography (CEM) in breast masses, and to compare its diagnostic accuracy with magnetic resonance imaging (MRI). CEM may provide an alternative option for patients with breast masses, especially for those with MRI contraindications., Materials and Methods: Two hundred and seventy-five patients with breast enhanced masses were included in the study from May 2019 to September 2022. Patients were further divided into benign and malignant groups based on pathological diagnosis. The CEM and MRI imaging characteristics of these two groups were analyzed statistically. The paired chi-square and Cohen's kappa coefficient (κ) analysis were used to compare imaging characteristics between CEM and MRI. The Breast Imaging Reporting and Data System (BI-RADS) and KS for CEM and MRI were evaluated based on imaging characteristics. The diagnostic performance of BI-RADS and KS for CEM and MRI was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test., Results: The imaging characteristics of root sign, time-signal intensity curve (TIC/mTIC), margin, internal enhancement pattern (IEP), edema, apparent diffusion coefficient (ADC) values, and suspicious malignant microcalcifications showed significant differences between benign and malignant lesions (all p ≤ 0.011). The detection rate of root sign and margin showed substantial agreement between CEM and MRI (κ = 0.656, κ = 0.640), but IEP, TIC/mTIC, and edema showed poor agreement (κ = 0.380, κ = 0.320, κ = 0.324). For all lesion analyses, the area under the curves (AUCs) of the KS (0.897 ∼ 0.932) were higher than that of BI-RADS (0.691) in CEM (all p < 0.001). The AUC of KS (calcification)-CEM (0.932) was higher than those of both KS-CEM and KS (edema)-CEM (0.897 and 0.899) (all p < 0.001). For subgroup analyses, the AUCs of the KS (0.875 ∼ 0.876) were higher than that of BI-RADS (0.740) in MRI (all p < 0.001). The AUCs of KS-MRI (0.876) and KS (ADC)-MRI (0.875) were similar to those of KS-CEM (0.878) and KS (edema)-CEM (0.870) (all p > 0.100). The AUC of KS (calcification)-CEM (0.934) was slightly higher than those of both KS-MRI (0.876) and KS (ADC)-MRI (0.875), but no significant difference was observed (p = 0.051; p = 0.071)., Conclusion: The KS for CEM provided high diagnostic accuracy in distinguishing breast masses, comparable to that of MRI. The application of KS (calcification)-CEM combined with suspicious malignant microcalcifications can improve diagnostic efficiency with an AUC of 0.932 ∼ 0.934. However, edema did not significantly improve performance when using the KS for CEM., 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 Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

ChatGPT3.5 and ChatGPT4 were released publicly in late November 2022 and March 2023, respectively, and have emerged as convenient sources of patient health education and information, including for screening mammography. ChatGPT4 offers enhanced capabilities; however, it is only available by paid subscription. The purported benefits of ChatGPT for health education need to be objectively evaluated. To assess performance differences, ChatGPT3.5 and GPT4 were used between 13 April and 29 May 2023 to generate breast screening patient information sheets, which were evaluated using the Patient Education Materials Assessment Tool for printed materials (PEMAT-P) and the CDC Clear Communication Index (CDC Index) Score Sheet; and benchmarked against gold standard content in BreastScreen NSW's patient information sheet. Mean scores were reported for comparison. GPT3.5 provided the appropriate tone and currency of information but lacked accuracy, omitting key insights: PEMAT-P understandability 68.0% (SD = 6.56) and actionability 36.7% (SD=20.4); CDC Index 58.8% (SD = 15.3). GPT4 was deemed superior to GPT3.5 but included several key omissions: PEMAT-P understandability 75.0% (SD = 17) and actionability 53.3% (SD = 11.54); CDC Index 66.0% (SD = 4.1). Both ChatGPT versions exhibited poor understandability and actionability and were unclear in their messaging. Those with poor health literacy will not benefit from accessing current versions of ChatGPT and may be further disadvantaged if they do not have access to a paid subscription. ChatGPT is evidenced to be an unreliable and inaccurate source of information concerning breast screening that may undermine participation and risk increased morbidity and mortality from breast cancer. ChatGPT may increase the demand on health care educators to rectify misinformation.

Objectives: To investigate the relationship between the mammography-detected breast arterial calcification (BAC) and orbital color Doppler ultrasonography (CDUS) results., Methods: Our single-center study, included female patients who applied to our hospital between January and May 2022 and underwent mammography and orbital CDUS examinations. Two radiologists evaluated the mammograms, grouped the patients as BAC (+) and BAC (-), and performed orbital CDUS. Continuous variables obtained from CDUS were compared according to the presence of BAC. Also, receiver operating characteristics (ROC) analysis was used to determine the orbital CDUS threshold values for the presence of BAC., Results: A total of 119 women were included with a median age of 62 years, 57 (47.90%) had BAC. Orbital CDUS examination was performed on both eyes of 119 patients (238 eyes in total). Peak systolic velocity (PSV) and end-diastolic velocity (EDV) values of the ophthalmic artery (OA) (P < .001) and EDV of the central retinal artery (CRA) (P < .001) were significantly lower in patients with BAC. Pulsatile index (PI) and resistive index (RI) values of OA (P < .001) and CRA (P < .001) were higher in patients with BAC. In ROC analysis, the cut-off values for the presence of BAC were calculated as OA PI ≥1.415 and OA RI ≥0.755 (P < .001); CRA PI ≥1.135 and CRA RI ≥0.655 (P < .001)., Conclusions: Orbital perfusion disorders may be observed in patients with vascular calcification detected on routine mammography. Therefore, a more detailed evaluation of patients with BAC detected on mammography with orbital CDUS may enable early detection and treatment of ocular vascular problems., (© 2024 American Institute of Ultrasound in Medicine.)

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., (© 2024. The Author(s).)

Breast cancer, with its increasing incidence and high mortality rates, remains a major global health challenge, significantly impacting individuals, families, and societies. Understanding the multifactorial risk factors contributing to its development is crucial for effective prevention and management. Hormonal factors play a significant role in breast cancer development. Given that ovarian steroid hormones influence breast function, any gonadotropin hormone or fertility drug that stimulates ovulation may also impact breast tissue. Contrary to the findings of studies with smaller sample sizes, concerns have emerged regarding the potential increased risk of breast cancer following in vitro fertilization (IVF) treatments. This article explores the potential risk of breast cancer associated with hormonal cycles during IVF, supported by a literature review and a case study conducted in a tertiary hospital in Bucharest, Romania. The case involves a 38-year-old patient with a history of hormonally treated endometriosis and five IVF cycles, who presented for mammographic and ultrasound screening. The screening revealed multicentric and multifocal BIRADS-5 lesions, with histopathological and immunohistochemical analysis confirming invasive breast carcinoma of no special type with ductal carcinoma in situ, HER2 positive (3+), estrogen receptor and progesterone receptor negative, and a Ki-67 proliferation index of 50%., Competing Interests: None.

Objectives: This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach., Methods: Thirty-six radiologists from China and Australia read 60 dense mammograms. For each cohort, we identified normal areas that looked suspicious of cancer and the malignant areas containing cancers. Then radiomic features were extracted from these identified areas and random forest models were trained to recognize the areas that were most frequently linked to diagnostic errors within each cohort. The performance of the model and discriminatory power of significant radiomic features were assessed., Results: We found that in the Chinese cohort, the AUC values for predicting false positives were 0.864 (CC) and 0.829 (MLO), while in the Australian cohort, they were 0.652 (CC) and 0.747 (MLO). For false negatives, the AUC values in the Chinese cohort were 0.677 (CC) and 0.673 (MLO), and in the Australian cohort, they were 0.600 (CC) and 0.505 (MLO). In both cohorts, regions with higher Gabor and maximum response filter outputs were more prone to false positives, while areas with significant intensity changes and coarse textures were more likely to yield false negatives., Conclusions: This cohort-based pipeline proves effective in identifying common errors for specific reader cohorts based on image-derived radiomic features., Advances in Knowledge: This study demonstrates that radiomics-based AI can effectively identify and predict radiologists' interpretation errors in dense mammograms, with distinct radiomic features linked to false positives and false negatives in Chinese and Australian cohorts., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Purpose: To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images., Methods: Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses)., Results: The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61 mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25 mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15 mm., Conclusions: Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

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 cm 2 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.

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).)

Objectives: We aimed to synthesise evidence from prospective studies of digital breast tomosynthesis (DBT) screening to assess its effectiveness compared to digital mammography (DM). Specifically, we examined whether DBT reduces interval cancer rates (ICRs) in population breast cancer screening., Materials and Methods: We performed a systematic review and meta-analysis of DBT screening studies (identified from January 2013 to March 2024). We included both RCTs and non-randomised prospective studies that used an independent comparison for our primary outcome ICRs. The risk of bias was assessed with QUADAS-2. We compared the ICR, cancer detection rate (CDR), and recall rate of DBT and DM screening using random effects meta-analysis models. Subgroup analyses estimated outcomes by study design. Sensitivity analyses estimated absolute effects from relative effects., Results: Ten prospective studies (three RCTs, seven non-randomised) were eligible; all had a low risk of bias. There were 205,245 DBT-screened and 306,476 DM-screened participants with follow-up for interval cancer data. The pooled absolute ICR did not significantly differ between DBT and DM: -2.92 per 10,000 screens (95% CI: -6.39 to 0.54); however subsequent subgroup analysis indicated certain study designs may have biased this ICR estimate. Pooled ICR from studies that only sampled groups from the same time and region indicated DBT led to 5.50 less IC per 10,000 screens (95% CI: -9.47 to -1.54). Estimates from subgroup analysis that compared randomised and non-randomised trials did not significantly differ., Conclusion: This meta-analysis provides suggestive evidence that DBT decreases ICR relative to DM screening; further evidence is needed to reduce uncertainty regarding ICR differences between DBT and DM., Key Points: Question Does DBT have long-term benefits over standard DM? Finding We find suggestive evidence in our primary analysis and stronger evidence in a follow-up analysis that DBT reduces interval cancers. Clinical relevance This meta-analysis provides the first indication that DBT may detect additional cancers that are clinically meaningful, based on suggestive evidence of a reduction in ICR. This finding does not preclude the simultaneous possibility of overdiagnosis., (© 2024. The Author(s).)

Background: Pericardial cysts, though rare and benign, can present with various clinical symptoms depending on their size and location in the body. The detection of these cysts typically relies on imaging studies for a conclusive diagnosis, with surgical removal being the definitive treatment., Case Presentation: This case report details the clinical journey of a 32-year-old Iranian woman with a family history of breast and lung cancer, who experienced left-sided chest pain. Utilizing a combination of clinical history review, mammography, echocardiography, and computed tomography, a precise diagnosis of a 10 cm × 3.5 cm pericardial cyst was achieved. The patient underwent median sternotomy for complete cyst excision., Conclusions: While pericardial cysts are often asymptomatic and benign, they can lead to life-threatening complications. Hence, regular follow-up is advised, and in certain instances, minimally invasive interventions or surgery may be necessary., (© 2024. The Author(s).)

Introduction: 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., Methods: 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., Results: 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., Conclusion: 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., Implications for Practice: 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., Competing Interests: Conflict of interest statement None., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.)