Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

The purpose of this study is to assess the usefulness of the novel abbreviated MR (AB-MR) protocol in the screening of women with an intermediate risk of breast cancer. Sixty women with a Tyrer-Cuzick model-determined intermediate risk of breast cancer underwent AB-MR, mammography, and tomosynthesis examinations; as an auxiliary procedure, ultrasound imaging was carried out. Every modality was allocated a final BI-RADS category. Time spent on acquisition and interpretation was also noted. Pathological confirmation was obtained in all cases exhibiting malignant findings. The difference in sensitivity and specificity between the two modalities was evaluated using McNemar's test.

When compared to traditional screening methods, AB-MR demonstrated 100% NPV, 98% specificity, 66.7% PPV, and 100% sensitivity in women with intermediate risk of breast cancer. Comparing mammography/ultrasound to positive malignancies verified by biopsy, the results indicated 100% sensitivity, 96.5% specificity, 60% PPV, and 100% NPV. Complete agreement was observed between abbreviated MR and malignant biopsies (100% sensitivity, specificity, NPV, and PPV). For AB-MR and mammography, the average reading time was 4 min and 5 min, respectively. The average acquisition time for AB-MRI was around 10 min, whereas the average time for complete MR imaging is 17 min.

AB-MR has better sensitivity, specificity, PPV, and NPV in screening of intermediate- and high-risk breast cancer. Acquisition time was shorter than full MR protocol. Reading time was decreased in respect of mammography. MRI screening ought to be more practical with the AB-MR protocol.

Breast cancer (BC) is one of the most common malignancies among women globally, characterized by complex pathogenesis involving various biomarkers present in blood and urine. To enhance understanding of the genetic associations between biomarkers and BC via multidimensional, cross ethnic investigations. Based on GWAS data of 35 blood and urine biomarkers from European populations, we adopted multiple analysis strategies including univariable Mendelian randomization (MR) analysis, reverse MR analysis, sensitivity analysis and multivariate MR to identify potential biomarkers associated with BC risk and survival. Our initial analysis included 122,977 BC and 105,974 controls of European ancestry. Building upon these findings, we conducted cross ethnic validation by applying the same analyses to East Asian populations using data from the IEU GWAS database, which included 5,552 BC and 89,731 controls. This step allowed us to investigate the universality and heterogeneity of our identified biomarkers across different ancestries. Subsequently, utilizing clinical laboratory detection data from multiple regions in China, we performed differential analyses and survival assessments on these potential biomarkers to evaluate their clinical relevance and utility. Notably, we leveraged Luzhou's clinical data to integrate HDL-C with conventional tumor markers (CEA, CA125, CA153) into a machine learning model, comparing its diagnostic efficacy against tumor marker combination. Our study validated associations of ALP, HDL-C, TG, SHBG, and IGF-1 with BC risk, reinforcing the reliability of these findings. Moreover, notable interethnic disparities emerged in the association between HDL-C and BC risk, where in HDL-C demonstrates a contrasting role: acting as a genetic protective agent against BC and suggesting promise as an auxiliary diagnostic marker in East Asian populations, yet inversely, it serves as a genetic dangerous predictor in European populations. Analyzing BC subtypes, we identified associations of HDL-C, TG, SHBG, and CRP with ER+BC, while ER-BC showed associations with GLU, urinary creatinine and microalbuminuria, underscoring subtype-specific genetic characteristics critical for personalized prevention and treatment strategies. Overall, this comprehensive study, by traversing the intricate landscape of genetic associations across ethnic boundaries and employing advanced analytical methodologies, not only uncovers the complex interplay between key biomarkers and BC susceptibility but also highlights the significance of ethnic-specific differences in the role of HDL-C. By enhancing the diagnostic power of a tailored biomarker panel through machine learning, this study contributes to the advancement of precision medicine in BC, offering strategies tailored to the unique genetic profiles and biomarker patterns across diverse populations.

There is limited data on the influence of various factors on mammography accuracy in low- and middle-income regions. In this cross-sectional study using registry data, we examined the sensitivity of screening mammograms by comparing mammography results with biopsies-confirmed breast cancer diagnoses in the state of São Paulo, Brazil. Additionally, we evaluate factors related to the screened women and to the breast lesions that could affect false-positive mammograms results. All screening mammograms conducted from January to December 2012 and biopsy results from January 2012 to December 2013 in the São Paulo State were retrieved from the Brazilian Breast Cancer Information System. We gathered details on women-related factors such as age, hormone therapy usage, prior radiotherapy, skin color, education level, skin type, breast density, and familial history of cancer, as well as on breast lesions, including type, size, characteristics, edges, and topographic site on the breast. To assess the risk effect of these factors on false-positive mammography results, we employed logistic regression analyses. Our results indicate that age under 50 years, use of hormone therapy, dense breasts, lesions smaller than 10 mm with defined edges, and the presence of calcifications were predictors of false-positive mammograms results. Finally, we observed that false-positives lead to longer times to diagnosis. These findings are relevant for the planning and management of organized breast cancer screening programs.

Annual review of false-negative (FN) mammograms is a mandatory and critical component of the Mammography Quality Standards Act (MQSA) annual mammography audit. FN review can help hone reading skills and improve the ability to detect cancers at mammography. Subtle architectural distortion, asymmetries (seen only on one view), small lesions, lesions with probably benign appearance (circumscribed regular borders), isolated microcalcifications, and skin thickening are the most common mammographic findings when the malignancy is visible at retrospective review of FN mammograms. Most FN mammograms are not due to radiologist error. There are common and predictable settings in which FN mammograms occur. Patient factors associated with elevated FN mammograms include dense breasts, elevated lifetime risk of breast cancer, and personal history of breast cancer treated with lumpectomy and radiation therapy. About half of FN cancers are detected by supplemental screening examinations and half manifest clinically. The most common manifesting symptoms for interval cancers are a palpable abnormality, nipple discharge, and skin changes. Interval cancers can have more aggressive pathologic features and higher rates of node positivity. The FN review includes Breast Imaging Reporting and Data System (BI-RADS) 3 cases that develop a cancer diagnosis during surveillance. Nonbreast malignancies diagnosed as interval cancers (most commonly lymphoma and metastatic disease) do not need to be counted as FNs for audit purposes. The FN review and annual audit are confidential processes that protect patient and radiologist information while allowing meaningful quality control and improvement. Although FN mammograms are rare, review of these cases is a valuable educational tool. The slide presentation from the RSNA Annual Meeting is available for this article. ©RSNA, 2025.

Purpose To evaluate the change in digital breast tomosynthesis artificial intelligence (DBT-AI) case scores over sequential screenings. Materials and Methods This retrospective review included 21 108 female patients (mean age ± SD, 58.1 years ± 11.5) with 31 741 DBT screening examinations performed at a single site from February 3, 2020, to September 12, 2022. Among 7000 patients with two or more DBT-AI screenings, 1799 had a 1-year follow-up and were included in the analysis. DBT-AI case scores and differences in case score over time were determined. Case scores ranged from 0 to 100. For each screening outcome (true positive [TP], false positive [FP], true negative [TN], false negative [FN]), mean and median case score change was calculated. Results The highest average case score was seen in TP examinations (average, 75; range, 7-100; n = 41), and the lowest average case score was seen in TN examinations (average, 34; range, 0-100; n = 1640). The largest positive case score change was seen in TP examinations (mean case score change, 21.1; median case score change, 17). FN examinations included mammographically occult cancers diagnosed following supplemental screening and those found at symptomatic diagnostic imaging. Differences between TP and TN mean case score change (P < .001) and between TP and FP mean case score change (P = .02) were statistically significant. Conclusion Using the combination of DBT AI case score with change in case score over time may help radiologists make recall decisions in DBT screening. All studies with high case score and/or case score changes should be carefully scrutinized to maximize screening performance. Keywords: Mammography, Breast, Computer Aided Diagnosis (CAD) Supplemental material is available for this article. © RSNA, 2025.

The immune response is an intricate system that involves the complex connection of cellular and molecular components, each with distinct functional specialisations. It has a distinct capacity to adjust and mould the immune response in accordance with specific stimuli, influenced by both genetic and environmental factors. The presence of genetic diversity, particularly across different ethnic and racial groups, significantly contributes to the impact of incidence of diseases, disease susceptibility, autoimmune disorders, and cancer risks in specific regions and certain populations. Environmental factors, including geography and socioeconomic status, further modulate the variety of the immune system responses. These, in turn, affect the susceptibility to infectious diseases and development of autoimmune disorders. Despite the complexity of the relationship, there remains a gap in understanding the specificity of immune indices across races, immune reference ranges among populations, highlighting the need for deeper understanding of immune diversity for personalized approaches in diagnostics and therapeutics. This review systematically organizes these findings, with the goal of emphasizing the potential of targeted interventions to address health disparities and advance translational research, enabling a more comprehensive strategy. This approach promises significant advancements in identifying specific immunological conditions, focusing on personalized interventions, through both genetic and environmental factors.

This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach.

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.

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.

This cohort-based pipeline proves effective in identifying common errors for specific reader cohorts based on image-derived radiomic features.

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.

Ductal carcinoma in situ (DCIS) accounts for 25% of newly diagnosed breast cancer cases with only 14%-53% developing into invasive ductal carcinoma (IDC), but currently overtreated due to inadequate accuracy of mammography. Subtypes of calcification, discernible from histology, has been suggested to have prognostic value in DCIS, while the lipid composition of saturated and unsaturated fatty acids may be altered in de novo synthesis with potential sensitivity to the difference between DCIS and IDC. We therefore set out to examine calcification using ultra short echo time (UTE) MRI and lipid composition using chemical shift-encoded imaging (CSEI), as markers for histological calcification classification, in the initial ex vivo step towards in vivo application.

Twenty female patients, with mean age (range) of 57 (35-78) years, participated in the study. Intra- and peri-tumoural degree of calcification and peri-tumoural lipid composition were acquired on MRI using UTE and CSEI, respectively. Ex vivo imaging was conducted on the freshly excised breast tumour specimens immediately after surgery. Histopathological analysis was conducted to determine the calcification status, Nottingham Prognostic Index (NPI), and proliferative activity marker Ki-67.

Intra-tumoural degree of calcification in malignant classification (1.05 ± 0.13) was significantly higher (p = 0.012) against no calcification classification (0.84 ± 0.09). Peri-tumoural degree of calcification in malignant classification (1.64 ± 0.10) was significantly higher (p = 0.033) against no calcification classification (1.41 ± 0.18). Peri-tumoural MUFA in malignant classification (0.40 ± 0.01) was significantly higher (p = 0.039) against no calcification classification (0.38 ± 0.02). Ki-67 showed significant negative correlation against peri-tumoural MUFA (p = 0.043, ρ = -0.457), significant positive correlation against SFA (p = 0.008, ρ = 0.577), and significant negative correlation against PUFA (p = 0.002, ρ = -0.653).

The intra- and peri-tumoural degree of calcification and peri-tumoural MUFA are sensitive to histological calcification classes supporting future investigation into DCIS prognosis.

This scoping review summarizes two emerging electrical impedance technologies: electrical impedance myography (EIM) and electrical impedance tomography (EIT). These methods involve injecting a current into tissue and recording the response at different frequencies to understand tissue properties. The review discusses basic methods and trends, particularly the use of electrodes: EIM uses electrodes for either injection or recording, while EIT uses them for both. Ag/AgCl electrodes are prevalent, and current injection is preferred over voltage injection due to better resistance to electrode wear and impedance changes. Advances in digital processing and integrated circuits have shifted EIM and EIT toward digital acquisition, using voltage-controlled current sources (VCCSs) that support multiple frequencies. The review details powerful processing algorithms and reconstruction tools for EIT and EIM, examining their strengths and weaknesses. It also summarizes commercial devices and clinical applications: EIT is effective for detecting cancerous tissue and monitoring pulmonary issues, while EIM is used for neuromuscular disease detection and monitoring. The role of machine learning and deep learning in advancing diagnosis, treatment planning, and monitoring is highlighted. This review provides a roadmap for researchers on device evolution, algorithms, reconstruction tools, and datasets, offering clinicians and researchers information on commercial devices and clinical studies for effective use and innovative research.

In evidence-based medicine frameworks, the highest level of evidence is derived from quantitative synthesis of double-masked, high-quality, randomly assigned controlled trials. Meta-analyses of randomly assigned controlled trials have demonstrated that screening mammography reduces breast cancer deaths. In the United States, every major guideline-producing organization has recommended screening mammography in average-risk women; however, there are controversies about age and frequency. Carefully controlled observational research studies and statistical modeling studies can address evidence gaps and inform evidence-based, contemporary screening practices. As breast imaging radiologists develop and evaluate existing and new screening tests and technologies, they will need to understand the key methodological considerations and scientific criteria used by policy makers and health service researchers to support dissemination and implementation of evidence-based screening tests. The Wilson and Jungner principles and the U.S. Preventive Services Task Force general analytic framework provide structured evaluations of the effectiveness of screening tests. Key considerations in both frameworks include public health significance, natural history of disease, cost-effectiveness, and characteristics of screening tests and treatments. Rigorous evaluation of screening tests using analytic frameworks can maximize the benefits of screening tests while reducing potential harms. The purpose of this article is to review key methodological considerations and analytic frameworks used to evaluate screening studies and develop evidence-based recommendations.

Breast cancer remains a leading cause of cancer mortality worldwide, with early detection crucial for improving outcomes. This systematic review evaluates recent advances in portable non-invasive technologies for early breast cancer detection, assessing their methods, performance, and potential for clinical implementation. A comprehensive literature search was conducted across major databases for relevant studies published between 2015 and 2024. Data on technology types, detection methods, and diagnostic performance were extracted and synthesized from 41 included studies. The review examined microwave imaging, electrical impedance tomography (EIT), thermography, bioimpedance spectroscopy (BIS), and pressure sensing technologies. Microwave imaging and EIT showed the most promise, with some studies reporting sensitivities and specificities over 90 %. However, most technologies are still in early stages of development with limited large-scale clinical validation. These innovations could complement existing gold standards, potentially improving screening rates and outcomes, especially in underserved populations, whiles decreasing screening waiting times in developed countries. Further research is therefore needed to validate their clinical efficacy, address implementation challenges, and assess their impact on patient outcomes before widespread adoption can be recommended.

False-positive results on screening mammography may affect women's willingness to return for future screening.

To evaluate the association between screening mammography results and the probability of subsequent screening.

Cohort study.

177 facilities participating in the Breast Cancer Surveillance Consortium (BCSC).

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.

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.

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.

Women could receive care at non-BCSC facilities.

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.

National Cancer Institute.

(1) Purpose: The purpose of this study was to describe the outcomes of diagnostic breast imaging and the incidence of delayed breast cancer diagnosis in the study population. (2) Methods: We collected the outcome data from diagnostic mammograms and/or breast ultrasounds (USs) performed on women between the ages of 30 and 50 with symptomatic breast clinical presentations between 2018 and 2019. (3) Results: Out of 171 eligible patients, 10 patients (5.8%) had BIRADS 0, 90 patients (52.6%) had benign findings (BIRADS 1 and 2), 41 (24.0%) patients had probable benign findings requiring short-term follow-up (BIRADS 3), while 30 (17.5%) patients had findings suspicious of malignancy (BIRADS 4 and 5). In the BIRADS 3 group, 92.7% had recommended follow-up, while in BIRADS 4 and 5, only 83.3% underwent recommended biopsy at a mean time of 1.7 weeks (range 0-22 wks) from their follow-up scan. Ten (6%) patients were diagnosed with breast cancer, all of whom had BIRADS 4 or 5, with a mean time of breast cancer diagnosis from initial diagnostic imaging of 2.2 weeks (range 1-22 wks). No patients had delayed breast cancer diagnosis in our cohort. (4) Conclusions: We conclude that diagnostic mammograms and breast US are appropriate investigations for clinical breast concerns in women aged 30-50 years.

Breast cancer is a leading cause of cancer mortality for US women. Trials have established that screening mammography can reduce mortality risk, but optimal screening ages, intervals, and modalities for population screening guidelines remain unclear.

To review studies comparing different breast cancer screening strategies for the US Preventive Services Task Force.

MEDLINE, Cochrane Library through August 22, 2022; literature surveillance through March 2024.

English-language publications; randomized clinical trials and nonrandomized studies comparing screening strategies; expanded criteria for screening harms.

Two reviewers independently assessed study eligibility and quality; data extracted from fair- and good-quality studies.

Mortality, morbidity, progression to advanced cancer, interval cancers, screening harms.

Seven randomized clinical trials and 13 nonrandomized studies were included; 2 nonrandomized studies reported mortality outcomes. A nonrandomized trial emulation study estimated no mortality difference for screening beyond age 74 years (adjusted hazard ratio, 1.00 [95% CI, 0.83 to 1.19]). Advanced cancer detection did not differ following annual or biennial screening intervals in a nonrandomized study. Three trials compared digital breast tomosynthesis (DBT) mammography screening with digital mammography alone. With DBT, more invasive cancers were detected at the first screening round than with digital mammography, but there were no statistically significant differences in interval cancers (pooled relative risk, 0.87 [95% CI, 0.64-1.17]; 3 studies [n = 130 196]; I2 = 0%). Risk of advanced cancer (stage II or higher) at the subsequent screening round was not statistically significant for DBT vs digital mammography in the individual trials. Limited evidence from trials and nonrandomized studies suggested lower recall rates with DBT. An RCT randomizing individuals with dense breasts to invitations for supplemental screening with magnetic resonance imaging reported reduced interval cancer risk (relative risk, 0.47 [95% CI, 0.29-0.77]) and additional false-positive recalls and biopsy results with the intervention; no longer-term advanced breast cancer incidence or morbidity and mortality outcomes were available. One RCT and 1 nonrandomized study of supplemental ultrasound screening reported additional false-positives and no differences in interval cancers.

Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.

Mitigating false negative imaging studies remains an important issue given its association with worse morbidity and mortality in patients with breast cancer. We aimed to identify risk factors that predispose to false negative breast imaging exams.

In an IRB-approved, HIPAA compliant retrospective study, we identified all patients who were diagnosed with breast cancer within 365 days of a negative imaging study assessed as BI-RADS 1-3 between January 1, 2014 and January 31, 2020. A matched cohort based on mammographic breast density was created from randomly selected studies with BI-RADS 4-5 designation that yielded breast cancer at pathology within the same time frame. Patient and cancer characteristics, prior personal history of breast cancer and gene mutation status were collected from patient charts. Pearson chi-squared and Student's t-test on two independent groups with significance at < 0.05 was used for statistical analysis.

We identified 155 false negative studies of 129 missed cancers and 128 breast density matched true positive cancers. False negative studies were screening mammograms in 57.42% (89/155), diagnostic mammograms in 29.68% (46/155), ultrasounds in 6.45% (10/155) and MRIs in 6.45% (10/155). Rates of personal (41.09% vs. 18.75%, p < 0.001) and family history of breast cancer (68.22% vs. 49.21%, p = 0.002) were higher in the false negative cohort and remained significant when asymptomatic MRI-detected cancers were removed.

Our findings suggest that supplemental screening may be useful in breast cancer survivors.

Literature meta-analysis results show that digital breast tomosynthesis (DBT) combined with synthesized two-dimensional (s2D) mammograms can reduce recalls and improve breast cancer detection. Uncertainty regarding the screening of patients with breast cancer presents a health economic challenge, both in terms of healthcare resource use and quality of life impact on patients.

This study aims to estimate the cost effectiveness of DBT + s2D versus digital mammography (DM) used in a biennial breast cancer screening setting of women aged 40-69 years with scattered areas of fibroglandular breast density and heterogeneous dense breasts in the Brazilian supplementary health system.

A cost-effectiveness analysis was performed on the basis of clinical data obtained from a systematic review with meta-analysis performed to evaluate the analytical validity and clinical utility of DBT + s2D compared with DM. The search was conducted in the PubMed, Cochrane Library and Embase databases, with the main descriptors of the technology, a comparator, and the clinical condition in question, on 9 June 2022. The hybrid economic model (decision tree plus Markov model) simulated costs and outcomes over a lifetime for women aged 40-69 years with scattered areas of fibroglandular breast density and heterogeneous dense breasts. We analyzed incremental cost-effectiveness ratio (ICER) to measure the incremental cost difference per quality-adjusted life year (QALY) of adding DBT + s2D to breast cancer screening.

DBT + s2D incurred a cost saving of € 954.02 per patient, in the time horizon of 30 years, compared with DM, and gained 5.1989 QALYs, which would be considered a dominant intervention. These results were confirmed in sensitivity analyses.

Switching from DM to biennial DBT + s2D was cost effective. Furthermore, reductions in false-positive recall rates should also be considered in decision making.

Breast cancer (BC) recurrence remains a concerning issue, requiring accurate identification and differentiation from primary lesions for optimal patient management. This comprehensive review aims to summarize and evaluate the current evidence on methods to distinguish primary breast tumors from recurrent lesions in patients with a history of BC. Also, we provide a comprehensive understanding of the different imaging techniques, including mammography, ultrasound, magnetic resonance imaging, and positron emission tomography, highlighting their diagnostic accuracy, limitations, and potential integration. In addition, the role of various biopsy modalities and molecular markers was explored. Furthermore, the potential role of liquid biopsy, circulating tumor cells, and circulating tumor DNA in differentiating between primary and recurrent BC was emphasized. Finally, it addresses emerging diagnostic modalities, such as radiomic analysis and artificial intelligence, which show promising potential in enhancing diagnostic accuracy. Through comprehensive analysis and review of the available literature, the current study provides an up-to-date understanding of the current state of knowledge, challenges, and future directions in accurately distinguishing between primary and recurrent breast lesions in patients with a history of BC.

An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women's mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed. Instead, test accuracy improved over time, through a reduction in false alarms and an increase in speed, with no significant change in sensitivity. The multiple-decision model explains why experts miss targets in low prevalence settings through a change in decision threshold and search quit threshold and propose it should be adapted to explain these observed patterns of accuracy with time on task. What is typically thought of as standard and robust research findings in controlled laboratory settings may not directly apply to real-world environments and instead large, controlled studies in relevant environments are needed.

To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models.

We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics.

When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC.

Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.

BACKGROUND. Screening mammography has decreased performance in patients with dense breasts. Supplementary screening ultrasound is a recommended option in such patients, although it has yielded mixed results in prior investigations. OBJECTIVE. The purpose of this article is to compare the performance characteristics of screening mammography alone, standalone artificial intelligence (AI), ultrasound alone, and mammography in combination with AI and/or ultrasound in patients with dense breasts. METHODS. This retrospective study included 1325 women (mean age, 53 years) with dense breasts who underwent both screening mammography and supplementary breast ultrasound within a 1-month interval from January 2017 to December 2017; prior mammography and prior ultrasound examinations were available for comparison in 91.2% and 91.8%, respectively. Mammography and ultrasound examinations were interpreted by one of 15 radiologists (five staff; 10 fellows); clinical reports were used for the present analysis. A commercial AI tool was used to retrospectively evaluate mammographic examinations for presence of cancer. Screening performances were compared among mammography, AI, ultrasound, and test combinations, using generalized estimating equations. Benign diagnoses required 24 months or longer of imaging stability. RESULTS. Twelve cancers (six invasive ductal carcinoma; six ductal carcinoma in situ) were diagnosed. Mammography, standalone AI, and ultrasound showed cancer detection rates (per 1000 patients) of 6.0, 6.8, and 6.0 (all p > .05); recall rates of 4.4%, 11.9%, and 9.2% (all p < .05); sensitivity of 66.7%, 75.0%, and 66.7% (all p > .05); specificity of 96.2%, 88.7%, and 91.3% (all p < .05); and accuracy of 95.9%, 88.5%, and 91.1% (all p < .05). Mammography with AI, mammography with ultrasound, and mammography with both ultrasound and AI showed cancer detection rates of 7.5, 9.1, and 9.1 (all p > .05); recall rates of 14.9, 11.7, and 21.4 (all p < .05); sensitivity of 83.3%, 100.0%, and 100.0% (all p > .05); specificity of 85.8%, 89.1%, and 79.4% (all p < .05); and accuracy of 85.7%, 89.2%, and 79.5% (all p < .05). CONCLUSION. Mammography with supplementary ultrasound showed higher accuracy, higher specificity, and lower recall rate in comparison with mammography with AI and in comparison with mammography with both ultrasound and AI. CLINICAL IMPACT. The findings fail to show benefit of AI with respect to screening mammography performed with supplementary breast ultrasound in patients with dense breasts.

False-positive mammography results are common. However, long-term outcomes after a false-positive result remain unclear.

To examine long-term outcomes after a false-positive mammography result and to investigate whether the association of a false-positive mammography result with cancer differs by baseline characteristics, tumor characteristics, and time since the false-positive result.

This population-based, matched cohort study was conducted in Sweden from January 1, 1991, to March 31, 2020. It included 45 213 women who received a first false-positive mammography result between 1991 and 2017 and 452 130 controls matched on age, calendar year of mammography, and screening history (no previous false-positive result). The study also included 1113 women with a false-positive result and 11 130 matched controls with information on mammographic breast density from the Karolinska Mammography Project for Risk Prediction of Breast Cancer study. Statistical analysis was performed from April 2022 to February 2023.

A false-positive mammography result.

Breast cancer incidence and mortality.

The study cohort included 497 343 women (median age, 52 years [IQR, 42-59 years]). The 20-year cumulative incidence of breast cancer was 11.3% (95% CI, 10.7%-11.9%) among women with a false-positive result vs 7.3% (95% CI, 7.2%-7.5%) among those without, with an adjusted hazard ratio (HR) of 1.61 (95% CI, 1.54-1.68). The corresponding HRs were higher among women aged 60 to 75 years at the examination (HR, 2.02; 95% CI, 1.80-2.26) and those with lower mammographic breast density (HR, 4.65; 95% CI, 2.61-8.29). In addition, breast cancer risk was higher for women who underwent a biopsy at the recall (HR, 1.77; 95% CI, 1.63-1.92) than for those without a biopsy (HR, 1.51; 95% CI, 1.43-1.60). Cancers after a false-positive result were more likely to be detected on the ipsilateral side of the false-positive result (HR, 1.92; 95% CI, 1.81-2.04) and were more common during the first 4 years of follow-up (HR, 2.57; 95% CI, 2.33-2.85 during the first 2 years; HR, 1.93; 95% CI, 1.76-2.12 at >2 to 4 years). No statistical difference was found for different tumor characteristics (except for larger tumor size). Furthermore, associated with the increased risk of breast cancer, women with a false-positive result had an 84% higher rate of breast cancer death than those without (HR, 1.84; 95% CI, 1.57-2.15).

This study suggests that the risk of developing breast cancer after a false-positive mammography result differs by individual characteristics and follow-up. These findings can be used to develop individualized risk-based breast cancer screening after a false-positive result.

We aimed to evaluate the false interpretations between artificial intelligence (AI) and radiologists in screening mammography to get a better understanding of how the distribution of diagnostic mistakes might change when moving from entirely radiologist-driven to AI-integrated breast cancer screening.

This retrospective case-control study was based on a mammography screening cohort from 2008 to 2015. The final study population included screening examinations for 714 women diagnosed with breast cancer and 8029 randomly selected healthy controls. Oversampling of controls was applied to attain a similar cancer proportion as in the source screening cohort. We examined how false-positive (FP) and false-negative (FN) assessments by AI, the first reader (RAD 1) and the second reader (RAD 2), were associated with age, density, tumor histology and cancer invasiveness in a single- and double-reader setting.

For each reader, the FN assessments were distributed between low- and high-density females with 53 (42%) and 72 (58%) for AI; 59 (36%) and 104 (64%) for RAD 1 and 47 (36%) and 84 (64%) for RAD 2. The corresponding numbers for FP assessments were 1820 (47%) and 2016 (53%) for AI; 1568 (46%) and 1834 (54%) for RAD 1 and 1190 (43%) and 1610 (58%) for RAD 2. For ductal cancer, the FN assessments were 79 (77%) for AI CAD; with 120 (83%) for RAD 1 and with 96 (16%) for RAD 2. For the double-reading simulation, the FP assessments were distributed between younger and older females with 2828 (2.5%) and 1554 (1.4%) for RAD 1 + RAD 2; 3850 (3.4%) and 2940 (2.6%) for AI+RAD 1 and 3430 (3%) and 2772 (2.5%) for AI+RAD 2.

The most pronounced decrease in FN assessments was noted for females over the age of 55 and for high density-women. In conclusion, AI could have an important complementary role when combined with radiologists to increase sensitivity for high-density and older females.

Our results highlight the potential impact of integrating AI in breast cancer screening, particularly to improve interpretation accuracy. The use of AI could enhance screening outcomes for high-density and older females.

Mammographic screening detects most breast cancers but there are still women diagnosed with breast cancer between annual mammograms. We aim to identify features that differentiate screen detected breast cancers from interval breast cancer.

All screening mammograms (n = 211,517) performed 7/1/2013-6/30/2020 at our institution were reviewed. Patients with breast cancer diagnosed within one year of screening were included and divided into two distinct groups: screen detected cancer group and interval cancer group. Characteristics in these groups were compared using the chi square test, fisher test, and student's T test.

A total of 1,232 patients were included (mean age 64 +/- 11). Sensitivity of screening mammography was 92% (1,136 screen detected cancers, 96 interval cancers). Patient age, race, and personal history of breast cancer were similar between the groups (p > 0.05). Patients with interval cancers more often had dense breast tissue (75/96 = 78% versus 694/1136 = 61%, p < 0.001). Compared to screen detected cancers, interval cancers were more often primary tumor stage two or higher (41/96 = 43% versus 139/1136 = 12%, p < 0.001) and regional lymph node stage one or higher (21/96 = 22% versus 132/1136 = 12%, p = 0.003). Interval cancers were more often triple negative (16/77 = 21% versus [48/813 = 6%], p < 0.001) with high Ki67 proliferation indices (28/45 = 62% versus 188/492 = 38%, p = 0.002).

Mammographic screening had high sensitivity for breast cancer detection (92%). Interval cancers were associated with dense breast tissue and had higher stage with less favorable molecular features compared to screen detected cancers.

Tools to predict a screening mammogram recall at the time of scheduling could improve patient care. We extracted patient demographic and breast care history information within the electronic medical record (EMR) for women undergoing digital breast tomosynthesis (DBT) to identify which factors were associated with a screening recall recommendation.

In 2018, 21,543 women aged 40 years or greater who underwent screening DBT at our institution were identified. Demographic information and breast care factors were extracted automatically from the EMR. The primary outcome was a screening recall recommendation of BI-RADS 0. A multivariable logistic regression model was built and included age, race, ethnicity groups, family breast cancer history, personal breast cancer history, surgical breast cancer history, recall history, and days since last available screening mammogram.

Multiple factors were associated with a recall on the multivariable model: history of breast cancer surgery (OR: 2.298, 95% CI: 1.854, 2.836); prior recall within the last five years (vs no prior, OR: 0.768, 95% CI: 0.687, 0.858); prior screening mammogram within 0-18 months (vs no prior, OR: 0.601, 95% CI: 0.520, 0.691), prior screening mammogram within 18-30 months (vs no prior, OR: 0.676, 95% CI: 0.520, 0.691); and age (normalized OR: 0.723, 95% CI: 0.690, 0.758).

It is feasible to predict a DBT screening recall recommendation using patient demographics and breast care factors that can be extracted automatically from the EMR.

Carcinoma breast is the commonest cancer among women. Various authors have studied breast cancer with Contrast-Enhanced Ultrasound (CEUS) with promising results. Despite promising results, the additional cost of post-processing software limits its availability. In this study, we evaluated the utility of CEUS in differentiating malignant from benign breast lesions on regular ultrasound equipment without the use of dedicated software.

We performed CEUS in 121 women with 121 breast lesions. CEUS was done by creating a custom preset on existing ultrasound equipment with the help of an application specialist authorized by the vendor. Lesions were evaluated qualitatively without the use of any commercial software. The pattern of enhancement i.e. homogenous, heterogeneous, peripheral, or no enhancement, and the number of penetrating vessels i.e., few or multiple were recorded. Results were compared with histopathological diagnosis.

There were a total of 121 breast lesions. The study showed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 86.67 %, 54.10 %, 65 %, and 80.49% respectively for differentiating benign vs malignant lesions on the basis of the pattern of contrast enhancement. Using penetrating vessels for differentiating malignant lesions from benign lesions, the sensitivity, specificity, PPV, and NPV were found to be 64%, 67.86%, 78.05%, and 51.35% respectively.

CEUS is useful in differentiating malignant from benign breast lesions. It can be easily performed by creating a custom preset on standard ultrasound equipment without the use of expensive software.

In order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density.

This retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands' density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity.

In total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001).

Deep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.

Background Prior cross-sectional studies have observed that breast cancer screening with digital breast tomosynthesis (DBT) has a lower recall rate and higher cancer detection rate compared with digital mammography (DM). Purpose To evaluate breast cancer screening outcomes with DBT versus DM on successive screening rounds. Materials and Methods In this retrospective cohort study, data from 58 breast imaging facilities in the Breast Cancer Surveillance Consortium were collected. Analysis included women aged 40-79 years undergoing DBT or DM screening from 2011 to 2020. Absolute differences in screening outcomes by modality and screening round were estimated during the study period by using generalized estimating equations with marginal standardization to adjust for differences in women's risk characteristics across modality and round. Results A total of 523 485 DBT examinations (mean age of women, 58.7 years ± 9.7 [SD]) and 1 008 123 DM examinations (mean age, 58.4 years ± 9.8) among 504 863 women were evaluated. DBT and DM recall rates decreased with successive screening round, but absolute recall rates in each round were significantly lower with DBT versus DM (round 1 difference, -3.3% [95% CI: -4.6, -2.1] [P < .001]; round 2 difference, -1.8% [95% CI: -2.9, -0.7] [P = .003]; round 3 or above difference, -1.2% [95% CI: -2.4, -0.1] [P = .03]). DBT had significantly higher cancer detection (difference, 0.6 per 1000 examinations [95% CI: 0.2, 1.1]; P = .009) compared with DM only for round 3 and above. There were no significant differences in interval cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.24, 0.30] [P = .96]; round 2 or above difference, 0.04 [95% CI: -0.19, 0.31] [P = .76]) or total advanced cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.15, 0.19] [P = .94]; round 2 or above difference, -0.06 [95% CI: -0.18, 0.11] [P = .43]). Conclusion DBT had lower recall rates and could help detect more cancers than DM across three screening rounds, with no difference in interval or advanced cancer rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Skaane in this issue.

There are four solid tumors with common screening options in the average-risk population aged 21 to 75 years (breast, cervical, colorectal, and, based on personalized risk assessment, prostate), but many cancers lack recommended population screening and are often detected at advanced stages when mortality is high. Blood-based multi-cancer early detection tests have the potential to improve cancer mortality through additional population screening. Reported here is a post-hoc analysis from the third Circulating Cell-free Genome Atlas substudy to examine multi-cancer early detection test performance in solid tumors with and without population screening recommendations and in hematologic malignancies. Participants with cancer in the third Circulating Cell-free Genome Atlas substudy analysis were split into three subgroups: solid screened tumors (breast, cervical, colorectal, prostate), solid unscreened tumors, and hematologic malignancies. In this post hoc analysis, sensitivity is reported for each subgroup across all ages and those aged ⩾50 years overall, by cancer, and by clinical cancer stage. Aggregate sensitivity in the solid screened, solid unscreened, and hematologic malignancy subgroups was 34%, 66%, and 55% across all cancer stages, respectively; restricting to participants aged ⩾50 years showed similar aggregate sensitivity. Aggregate sensitivity was 27%, 53%, and 60% across stages I to III, respectively. Within the solid unscreened subgroup, aggregate sensitivity was >75% in 8/18 cancers (44%) and >50% in 13/18 (72%). This multi-cancer early detection test detected cancer signals at high (>75%) sensitivity for multiple cancers without existing population screening recommendations, suggesting its potential to complement recommended screening programs.Clinical trial identifier: NCT02889978.

In Dutch breast cancer screening, solitary, new or growing well-circumscribed masses should be recalled for further assessment. This results in cancers detected but also in false positive recalls, especially at initial screening. The aim of this study was to determine characteristics of well-circumscribed masses at mammography and identify potential methods to improve the recall strategy.

A systematic literature search was performed using PubMed. In addition, follow-up data were retrieved on all 8860 recalled women in a Dutch screening region from 2014 to 2019.

Based on 15 articles identified in the literature search, we found that probably benign well-circumscribed masses that were kept under surveillance had a positive predictive value (PPV) of 0-2%. New or enlarging solitary well-circumscribed masses had a PPV of 10-12%. In general the detected carcinomas had a favorable prognosis. In our exploration of screening practice, 25% of recalls (2133/8860) were triggered by a well-circumscribed mass. Those recalls had a PPV of 2.0% for initial and 10.6% for subsequent screening. Most detected carcinomas had a favorable prognosis as well.

To recognize malignancies presenting as well-circumscribed masses, identifying solitary, new or growing lesions is key. This information is missing at initial screening since prior examinations are not available, leading to a low PPV. Access to prior clinical examinations may therefore improve this PPV. In addition, given the generally favorable prognosis of screen-detected malignant well-circumscribed masses, one may opt to recall these lesions at subsequent screening, if grown, rather than at initial screening.

BACKGROUND. Overlap in ultrasound features of benign and malignant breast masses yields high rates of false-positive interpretations and benign biopsy results. Optoacoustic imaging is an ultrasound-based functional imaging technique that can increase specificity. OBJECTIVE. The purpose of this study was to compare specificity at fixed sensitivity of ultrasound images alone and of fused ultrasound and optoacoustic images evaluated with machine learning-based decision support tool (DST) assistance. METHODS. This retrospective Reader-02 study included 480 patients (mean age, 49.9 years) with 480 breast masses (180 malignant, 300 benign) that had been classified as BI-RADS category 3-5 on the basis of conventional gray-scale ultrasound findings. The patients were selected by stratified random sampling from the earlier prospective 16-site Pioneer-01 study. For that study, masses were further evaluated by ultrasound alone followed by fused ultrasound and optoacoustic imaging between December 2012 and September 2015. For the current study, 15 readers independently reviewed the previously acquired images after training in optoacoustic imaging interpretation. Readers first assigned probability of malignancy (POM) on the basis of clinical history, mammographic findings, and conventional ultrasound findings. Readers then evaluated fused ultrasound and optoacoustic images, assigned scores for ultrasound and optoacoustic imaging features, and viewed a POM prediction score derived by a machine learning-based DST before issuing final POM. Individual and mean specificities at fixed sensitivity of 98% and partial AUC (pAUC) (95-100% sensitivity) were calculated. RESULTS. Averaged across all readers, specificity at fixed sensitivity of 98% was significantly higher for fused ultrasound and optoacoustic imaging with DST assistance than for ultrasound alone (47.2% vs 38.2%; p = .03). Across all readers, pAUC was higher (p < .001) for fused ultrasound and optoacoustic imaging with DST assistance (0.024 [95% CI, 0.023-0.026]) than for ultrasound alone (0.021 [95% CI, 0.019-0.022]). Better performance using fused ultrasound and optoacoustic imaging with DST assistance than using ultrasound alone was observed for 14 of 15 readers for specificity at fixed sensitivity and for 15 of 15 readers for pAUC. CONCLUSION. Fused ultrasound and optoacoustic imaging with DST assistance had significantly higher specificity at fixed sensitivity than did conventional ultrasound alone. CLINICAL IMPACT. Optoacoustic imaging, integrated with reader training and DST assistance, may help reduce the frequency of biopsy of benign breast masses.

Nonsteroidal anti-inflammatory agents (NSAID) are associated with modest inconsistent reductions in breast cancer risk in population-based cohorts, whereas two focused studies of patients with benign breast disease (BBD) have found lower risk with NSAID use. Given that BBD includes fibroinflammatory lesions linked to elevated breast cancer risk, we assessed whether NSAID use was associated with lower breast cancer risk among patients with BBD.Participants were postmenopausal women in the Cancer Prevention Study-II (CPS-II), a prospective study of cancer incidence and mortality, who completed follow-up surveys in 1997 with follow-up through June 30, 2015. History of BBD, NSAID use, and covariate data were updated biennially. This analysis included 23,615 patients with BBD and 36,751 patients with non-BBD, including 3,896 incident breast cancers over an average of 12.72 years of follow-up among participants. NSAID use, overall and by formulation, recency, duration, and pills per month was analyzed versus breast cancer risk overall and by BBD status using multivariable-adjusted Cox models; BBD status and NSAID use were modeled as time-dependent exposures.Patients with BBD who reported using NSAIDs experienced lower breast cancer risk (HR, 0.87; 95% CI, 0.78-0.97), with similar effects for estrogen receptor (ER)-positive breast cancers [HR, 0.85; 95% confidence interval (CI), 0.74-0.97] and ER-negative breast cancers (HR, 0.87; 95% CI, 0.59-1.29); among women without BBD, NSAID use was unrelated to risk (HR, 1.02; 95% CI, 0.92-1.13; Pinteraction = 0.04). Associations stratified by age, obesity, menopausal hormone use, and cardiovascular disease were similar.Among patients with BBD, NSAID use appears linked to lower breast cancer risk. Further studies to assess the value of NSAID use among patients with BBD are warranted.

We examined whether NSAID use, a modifiable exposure, is associated with breast cancer risk in postmenopausal women from the Cancer Prevention Study-II with self-reported benign breast disease, an often inflammatory condition associated with higher rates of breast cancer.

The aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation.

A population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7 Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms.

The study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women <50 years of age with dense breasts accounted for 20.3% of mammograms and 33.9% of recalls (efficiency ratio = 1.7). Results including facility-level characteristics were similar.

Prioritizing women with baseline mammograms or ≥5 years since prior mammogram for immediate interpretation and possible diagnostic evaluation could considerably reduce the number of women needing to return for diagnostic imaging at another visit.

BRCA1/2-associated invasive breast cancer has been extensively studied. However, there are few reports of ductal carcinoma in situ (DCIS).

This study aimed to investigate the clinicopathological and imaging findings of DCIS in patients with BRCA1/2 mutations.

This was a single-institution, retrospective study. We identified patients diagnosed with DCIS with BRCA mutations between September 2003 and December 2020. Clinicopathological data and mammography (MG), magnetic resonance imaging (MRI), and ultrasound (US) findings were reviewed.

We identified 30 cancers in 28 patients; 7 (25.0%) patients had BRCA1 mutations, and 21 (75.0%) had BRCA2 mutations. The median patient age was 42 years. Screening was the most common reason for the detection of DCIS (50.0%), followed by occult cancer diagnosed by pathological examination after risk-reducing mastectomy (26.7%). The nuclear grade was most often 1 (46.7%), and 93.3% were estrogen and/or progesterone receptor positive. The detection rates of MG, MRI, and US were 64.3%, 72.0%, and 64.0%, respectively. The most common imaging findings were calcification (100%) on MG, non-mass enhancement (88.9%) on MRI, and hypoechoic area (75.0%) on US.

BRCA-associated DCIS was more strongly associated with BRCA2, and imaging features were similar to those of sporadic DCIS. Our results are helpful in informing surveillance strategies based on genotypes in women with BRCA mutations.

The aim of this study was to compare screening mammography performance metrics for immediate (live) interpretation versus offline interpretation at a cancer center.

An institutional review board-approved, retrospective comparison of screening mammography metrics at a cancer center for January 1, 2018, to December 31, 2019 (live period), and September 1, 2020, to March 31, 2022 (offline period), was performed. Before July 2020, screening examinations were interpreted while patients waited (live period), and diagnostic workup was performed concurrently. After the coronavirus disease 2019 shutdown from March to mid-June 2020, offline same-day interpretation was instituted. Patients with abnormal screening results returned for separate diagnostic evaluation. Screening metrics of positive predictive value 1 (PPV1), cancer detection rate (CDR), and abnormal interpretation rate (AIR) were compared for 17 radiologists who interpreted during both periods. Statistical significance was assessed using χ² analysis.

In the live period, there were 7,105 screenings, 635 recalls, and 51 screen-detected cancers. In the offline period, there were 7,512 screenings, 586 recalls, and 47 screen-detected cancers. Comparison of live screening metrics versus offline metrics produced the following results: AIR, 8.9% (635 of 7,105) versus 7.8% (586 of 7,512) (P = .01); PPV1, 8.0% (51 of 635) versus 8.0% (47 of 586); and CDR, 7.2/1,000 versus 6.3/1,000 (P = .50). When grouped by >10% AIR or <10% AIR for the live period, the >10% AIR group showed a significant decrease in AIR for offline interpretation (from 12.7% to 9.7%, P < .001), whereas the <10% AIR group showed no significant change (from 7.4% to 6.7%, P = .17).

Conversion to offline screening interpretation from immediate interpretation at a cancer center was associated with lower AIR and similar CDR and PPV1. This effect was seen largely in radiologists with AIR > 10% in the live setting.

We aimed to identify factors associated with false-positive recalls in mammography screening compared with women who were not recalled and those who received true-positive recalls.

We included 29,129 women, aged 40 to 74 years, who participated in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) between 2011 and 2013 with follow-up until the end of 2017. Nonmammographic factors were collected from questionnaires, mammographic factors were generated from mammograms, and genotypes were determined using the OncoArray or an Illumina custom array. By the use of conditional and regular logistic regression models, we investigated the association between breast cancer risk factors and risk models and false-positive recalls.

Women with a history of benign breast disease, high breast density, masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have mammography recalls, including both false-positive and true-positive recalls. Further analyses restricted to women who were recalled found that women with a history of benign breast disease and dense breasts had a similar risk of having false-positive and true-positive recalls, whereas women with masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have true-positive recalls than false-positive recalls.

We found that risk factors associated with false-positive recalls were also likely, or even more likely, to be associated with true-positive recalls in mammography screening.

Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.

In 2009, the U.S. Preventive Services Task Force updated recommended mammography screening frequency from annual to biennial for average-risk women aged 50-74 years. The association between estimated breast cancer risk and mammography screening frequency was evaluated.

A single-center retrospective cohort study was conducted among racially/ethnically diverse women, aged 50-74 years, who underwent screening mammography from 2014 to 2018. Data on age, race/ethnicity, first-degree family history of breast cancer, previous benign breast biopsies, and mammographic density were extracted from the electronic health record to calculate Breast Cancer Surveillance Consortium 5-year risk of invasive breast cancer, with a 5-year risk ≥1.67% defined as high risk. Multivariable analyses were conducted to determine the association between breast cancer risk factors and mammography screening frequency (annual versus biennial). Data were analyzed from 2020 to 2022.

Among 12,929 women with a mean age of 61±6.9 years, 82.7% underwent annual screening mammography, and 30.7% met high-risk criteria for breast cancer. Hispanic women were more likely to screen annually than non-Hispanic Whites (85.0% vs 79.8%, respectively), despite fewer meeting high-risk criteria. In multivariable analyses adjusting for breast cancer risk factors, high- versus low/average-risk women (OR=1.17; 95% CI=1.04, 1.32) and Hispanic versus non-Hispanic White women (OR=1.46; 95% CI=1.29, 1.65) were more likely to undergo annual mammography.

A majority of women continue to undergo annual screening mammography despite only a minority meeting high-risk criteria, and Hispanic women were more likely to screen annually despite lower overall breast cancer risk. Future studies should focus on the implementation of risk-stratified breast cancer screening strategies.

Globally, breast cancer is the most frequently occurring cancer in women and is the reason for more disability-adjusted life years lost than any other type of cancer. Hence, early screening plays a vital role in reducing breast cancer mortality. Although mammography is the standard procedure used for screening and diagnosis of breast cancer, it still has some limitations. Other methods used for screening include ultrasound and clinical breast examination. Despite its limitations, mammography is the gold standard for screening breast malignancy. Another emerging method for screening is thermography. With recent technological advances, breast cancer screening through thermography has demonstrated several advantages over existing modalities. For this review, a literature search was performed using databases such as PubMed, Google Scholar, and ScienceDirect. The keywords searched included breast cancer, early detection, breast cancer screening, mammography, and thermography. This review discusses the benefits of thermography showing that it can be a significant modality for breast cancer screening. The recent developments in thermal sensors, imaging protocols, and computer-aided software diagnostics hold great promise for making this technique a mainstream screening method for cancer. Moreover, the use of artificial intelligence and thermal imaging to detect early-stage breast cancer can provide impressive results. Therefore, thermography will be a promising technology for the early detection of breast cancer.

Determine whether there are patterns of lesion recall among breast imaging subspecialists interpreting screening mammography, and if so, whether recall patterns correlate to morphologies of screen-detected cancers.

This Institutional Review Board-approved, retrospective review included all screening examinations January 3, 2012-October 1, 2018 interpreted by fifteen breast imaging subspecialists at a large academic medical center and two outpatient imaging centers. Natural language processing identified radiologist recalls by lesion type (mass, calcifications, asymmetry, architectural distortion); proportions of callbacks by lesion types were calculated per radiologist. Hierarchical cluster analysis grouped radiologists based on recall patterns. Groups were compared to overall practice and each other by proportions of lesion types recalled, and overall and lesion-specific positive predictive value-1 (PPV1).

Among 161,859 screening mammograms with 13,086 (8.1%) recalls, Hierarchical cluster analysis grouped 15 radiologists into five groups. There was substantial variation in proportions of lesions recalled: calcifications 13%-18% (Chi-square 45.69, p < 0.00001); mass 16%-44% (Chi-square 498.42, p < 0.00001); asymmetry 13%-47% (Chi-square 660.93, p < 0.00001) architectural distortion 6%-20% (Chi-square 283.81, p < 0.00001). Radiologist groups differed significantly in overall PPV1 (range 5.6%-8.8%; Chi-square 17.065, p = 0.0019). PPV1 by lesion type varied among groups: calcifications 9.2%-15.4% (Chi-square 2.56, p = 0.6339); mass 5.6%-8.5% (Chi-square 1.31, p = 0.8597); asymmetry 3.4%-5.9% (Chi-square 2.225, p = 0.6945); architectural distortion 5.6%-10.8% (Chi-square 5.810, p = 0.2138). Proportions of recalled lesions did not consistently correlate to proportions of screen-detected cancer.

Breast imaging subspecialists have patterns for screening mammography recalls, suggesting differential weighting of imaging findings for perceived malignant potential. Radiologist recall patterns are not always predictive of screen-detected cancers nor lesion-specific PPV1s.

Interval breast cancer refers to cancer diagnosed after a negative screening mammogram and before the next scheduled screening mammogram. Interval breast cancer has worse prognosis than screening-detected cancer. Body mass index (BMI) influences the accuracy of mammography and overall postmenopausal breast cancer risk, yet how is obesity associated with postmenopausal interval breast cancer incidence is unclear. The current study included cancer-free postmenopausal women aged 50-79 years at enrollment in the Women's Health Initiative who were diagnosed with breast cancer during follow-up. Analyses include 324 interval breast cancer cases diagnosed within one year after the participant's last negative screening mammogram and 1969 screening-detected breast cancer patients. Obesity (BMI ≥ 30 kg/m2) was measured at baseline. Associations between obesity and incidence of interval cancer were determined by sequential logistic regression analyses. In multivariable-adjusted models, obesity was inversely associated with interval breast cancer risk [OR (95% CI) = 0.65 (0.46, 0.92)]. The inverse association persisted after excluding women diagnosed within 2 years [OR (95% CI) = 0.60 (0.42, 0.87)] or 4 years [OR (95% CI) = 0.56 (0.37, 0.86)] of enrollment, suggesting consistency of the association regardless of screening practices prior to trial entry. These findings warrant confirmation in studies with body composition measures.