Mammography screening reduces rates of advanced and fatal breast cancers: Results in 549,091 women

Breast Ultrasound Volume Sweep Imaging: A New Horizon in Expanding Imaging Access for Breast Cancer Detection

OBJECTIVE

The majority of people in the world lack basic access to breast diagnostic imaging resulting in delay to diagnosis of breast cancer. In this study, we tested a volume sweep imaging (VSI) ultrasound protocol for evaluation of palpable breast lumps that can be performed by operators after minimal training without prior ultrasound experience as a means to increase accessibility to breast ultrasound.

METHODS

Medical students without prior ultrasound experience were trained for less than 2 hours on the VSI breast ultrasound protocol. Patients presenting with palpable breast lumps for standard of care ultrasound examination were scanned by a trained medical student with the VSI protocol using a Butterfly iQ handheld ultrasound probe. Video clips of the VSI scan imaging were later interpreted by an attending breast imager. Results of VSI scan interpretation were compared to the same-day standard of care ultrasound examination.

RESULTS

Medical students scanned 170 palpable lumps with the VSI protocol. There was 97% sensitivity and 100% specificity for a breast mass on VSI corresponding to 97.6% agreement with standard of care (Cohen's κ = 0.95, P < .0001). There was a detection rate of 100% for all cancer presenting as a sonographic mass. High agreement for mass characteristics between VSI and standard of care was observed, including 87% agreement on Breast Imaging-Reporting and Data System assessments (Cohen's κ = 0.82, P < .0001).

CONCLUSIONS

Breast ultrasound VSI for palpable lumps offers a promising means to increase access to diagnostic imaging in underserved areas. This approach could decrease delay to diagnosis for breast cancer, potentially improving morbidity and mortality.

Fluoroscopic Intraoperative Breast Neoplasm and Node Detection

BACKGROUND

Preoperative localization is necessary for nonpalpable breast lesions. A novel procedure, fluoroscopic intraoperative neoplasm and node detection (FIND), obviates the preoperative painful and potentially expensive localization by using intraoperative visualization of the standard clip placed during diagnostic biopsy. We hypothesized FIND would improve negative margin rates.

STUDY DESIGN

This is an IRB-approved retrospective study (September 2016 to March 2021). Electronic chart review identified breast and axillary node procedures using wire localization (WL) or FIND. Primary outcome was margin status. Secondary outcomes included re-excision rate, specimen weight, surgery time, and axillary node localization rate.

RESULTS

We identified 459 patients, of whom 116 (25.3%) underwent FIND and 343 (74.7%) WL. Of these, 68.1% of FIND and 72.0% of WL procedures were for malignant lesions. Final margin positivity was 5.1% (4 of 79) for FIND and 16.6% (41 of 247) for WL (p = 0.008). This difference lost statistical significance on multivariable logistic regression (p = 0.652). Re-excision rates were 7.6% and 14.6% for FIND and WL (p = 0.125), with an equivalent mean specimen weight (p = 0.502), and mean surgery time of 177.5 ± 81.7 and 157.1 ± 66.8 minutes, respectively (mean ± SD; p = 0.022). FIND identified all (29 of 29) targeted axillary nodes, and WL identified only 80.1% (21 of 26) (p = 0.019).

CONCLUSIONS

FIND has lower positive margin rates and a trend towards lower re-excision rates compared with WL, proving its value in localizing nonpalpable breast lesions. It also offers accurate localization of axillary nodes, valuable in the era of targeted axillary dissection. It is a method of visual localization, using a skill and equipment surgeons already have, and saves patients and medical systems an additional schedule-disruptive, painful procedure, especially valuable when using novel localization devices is cost-prohibitive.

Toward the strengthening of radioprotection during mammography examinations through transparent glass screens: A benchmarking between experimental and Monte Carlo simulation studies

Introduction

A lead-acrylic protective screen is suggested to reduce radiation exposure to the unexposed breast during mammography. The presence of toxic lead in its structure may harm the tissues with which it comes in contact. This study aimed to design a CdO-rich quaternary tellurite glass screen (C40) and evaluate its efficiency compared to the Lead-Acrylic protective screen.

Methods

A three-layer advanced heterogeneous breast phantom designed in MCNPX (version 2.7.0) general-purpose Monte Carlo code. Lead acrylic and C40 shielding screens were modeled in the MCNPX and installed between the right and left breast. The reliability of the absorption differences between the lead acrylic and C40 glass were assessed.

Results and discussion

The results showed that C40 protective glass screen has much superior protection properties compared to the lead acrylic protective screen. The amount of total dose absorbed in the unexposed breast for C40 was found to be much less than that for lead-based acrylic. The protection provided by the C40 glass screen is 35–38% superior to that of the Lead-Acrylic screen. The C40 offer the opportunity to avoid the toxic Pb in the structure of Lead-Acrylic material and may be utilized for mammography to offer superior radioprotection to Lead-Acrylic and significantly lower the dose amount in the unexposed breast. It can be concluded that transparent glass screens may be utilized for radiation protection purposes in critical diagnostic radiology applications through mammography.

A Clinical Prediction Model for Breast Cancer in Women Having Their First Mammogram

Background: Digital mammography is the most efficient screening and diagnostic modality for breast cancer (BC). However, the technology is not widely available in rural areas. This study aimed to construct a prediction model for BC in women scheduled for their first mammography at a breast center to prioritize patients on waiting lists. Methods: This retrospective cohort study analyzed breast clinic data from January 2013 to December 2017. Clinical parameters that were significantly associated with a BC diagnosis were used to construct predictive models using stepwise multiple logistic regression. The models’ discriminative capabilities were compared using receiver operating characteristic curves (AUCs). Results: Data from 822 women were selected for analysis using an inverse probability weighting method. Significant risk factors were age, body mass index (BMI), family history of BC, and indicated symptoms (mass and/or nipple discharge). When these factors were used to construct a model, the model performance according to the Akaike criterion was 1387.9, and the AUC was 0.82 (95% confidence interval: 0.76–0.87). Conclusion: In a resource-limited setting, the priority for a first mammogram should be patients with mass and/or nipple discharge, asymptomatic patients who are older or have high BMI, and women with a family history of BC.

Test-set training improves the detection rates of invasive cancer in screening mammography

AIM

To investigate if mammographic test-set participation affects routine breast cancer screening performance.

MATERIALS AND METHODS

Clinical audit data between 2008 and 2018 were collected for 35 breast screen readers who participated in the BreastScreen Reader Assessment Strategy (BREAST) and 22 readers with no history of test-set participation. For BREAST readers, the annual audit data were divided according to the year they completed their first test set, and the same years were used randomly to align and divide the data of non-BREAST readers into pre- and post-test set periods. Multiple audit parameters were inspected retrospectively for the two cohorts to identify how their reading performance has evolved in screening mammography.

RESULTS

Investigating 2 calendar years before and after test-set participation, BREAST and non-BREAST readers recalled lower rates of women in the latter period (p=0.03 and p=0.02, respectively). They also improved their positive predictive value (PPV; p=0.01 and p=0.02, respectively). BREAST readers additionally improved their detection rates of invasive cancer (p=0.02) and all cancers (p=0.01). In an extended 3-year comparison, similar improvements occurred in the recall rate for BREAST (p=0.02) and non-BREAST readers (p=0.02) and in PPV (p=0.001, 0.01, respectively); however, improvements in detection rates also occurred exclusively in BREAST readers' performance for invasive cancer (p=0.04), DCIS (p=0.05), and all cancers (p=0.02); however, significant improvements in detection did not involve <15 mm invasive cancers in both periods. Meanwhile, non-BREAST readers demonstrated a decrease in sensitivity (p=0.02).

CONCLUSION

Participation in test sets is linked to over-time improvements in most audit-measured cancer detection rates.

Does the Combination of Phone, Email and Text-Based Reminders Improve No-show Rates for Patients in Breast Imaging?

The issue of no-shows in radiology is complicated and challenging. Mammography and ultrasound have the highest rate of no-shows among radiologic exams. Screening mammography is one of the most cost-effective ways to reduce breast cancer related deaths. However, the benefit of screening is heavily dependent on patient compliance to routine exams. Enhancing patients' commitments to their scheduled appointments, thereby improving early detection and decreasing breast cancer related mortality. Retrospective analysis of no-show visits scheduled from August 2017 to December 2017 (before the implementation of combined phone, email and text-based reminders) and from August 2019 to December 2019 (after the implementation of reminder and follow-up phone calls after missed appointments by the coordinator) in an urban academic breast imaging center was conducted. There were 368 no-show patients in 2017 and 238 no-show patients in 2019. Percentage of no-shows, and delay time to the rescheduled missed appointment were calculated. Subgroup analysis of the type of studies that were missed and those who did not reschedule the missed appointment was conducted. Mann Whitney U test was used to analyze differences between group means. No-show visits decreased by 50% in 2019 when compared to 2017. The average wait time between the missed appointment and the rescheduled appointment decreased significantly from 30.7 weeks in 2017 to 12.1 weeks in 2019 (P = 0.047). The percentage of no-show visits was highest among the unemployed, patients scheduled for screening mammograms and patients with a high average of no-show visits. No-show visits adversely impact patient outcome and contribute to increased cost of healthcare. Through a deeper understanding of the factors contributing to no-shows, we can strive to make appropriate interventions to alleviate the consequences of no-shows.

A Catchment and Location-Allocation Analysis of Mammography Access in Delaware, US: Implications for disparities in geographic access to breast cancer screening

Background

Despite a 40% reduction in breast cancer mortality over the last 30 years, not all groups have benefited equally from these gains. A consistent link between later stage of diagnosis and disparities in breast cancer mortality has been observed by race, socioeconomic status, and rurality. Therefore, ensuring equitable geographic access to screening mammography represents an important priority for reducing breast cancer disparities. This study conducted a catchment and location-allocation analysis of mammography access in Delaware, a state that is representative of the US in terms of race and urban-rural characteristics and experiences an elevated burden from breast cancer.

Methods

A catchment analysis using the ArcGIS Pro Service Area analytic tool characterized the geographic distribution of mammography sites and Breast Imaging Centers of Excellence (BICOEs). Poisson regression analyses identified census tract-level correlates of access. Next, the ArcGIS Pro Location-Allocation analytic tool identified candidate locations for the placement of additional mammography sites in Delaware according to several sets of breast cancer screening guidelines.

Results

The catchment analysis showed that for each standard deviation increase in the number of Black women in a census tract, there were 64% (95% CI, 0.18-0.66) fewer mammography units and 85% (95% CI, 0.04-0.48) fewer BICOEs. The more rural counties in the state accounted for 41 % of the population but only 22% of the BICOEs. The results of the location-allocation analysis depended on which set of screening guidelines were adopted, which included increasing mammography sites in communities with a greater proportion of younger Black women and in rural areas.

Conclusions

The results of this study illustrate how catchment and location-allocation analytic tools can be leveraged to guide the equitable selection of new mammography facility locations as part of a larger strategy to close breast cancer disparities.

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

Tumour-educated platelets for breast cancer detection: biological and technical insights

BACKGROUND

Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier.

METHODS

Platelet mRNA was sequenced from 266 women with stage I-IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses.

RESULTS

Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals.

CONCLUSIONS

We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.

Contrast-Enhanced Spectral Mammography in the Evaluation of Breast Microcalcifications: Controversies and Diagnostic Management

The aim of this study was to evaluate the diagnostic performance of contrast-enhanced spectral mammography (CESM) in predicting breast lesion malignancy due to microcalcifications compared to lesions that present with other radiological findings. Three hundred and twenty-one patients with 377 breast lesions that underwent CESM and histological assessment were included. All the lesions were scored using a 4-point qualitative scale according to the degree of contrast enhancement at the CESM examination. The histological results were considered the gold standard. In the first analysis, enhancement degree scores of 2 and 3 were considered predictive of malignity. The sensitivity (SE) and positive predictive value (PPV) were significative lower for patients with lesions with microcalcifications without other radiological findings (SE = 53.3% vs. 82.2%, p-value < 0.001 and PPV = 84.2% vs. 95.2%, p-value = 0.049, respectively). On the contrary, the specificity (SP) and negative predictive value (NPV) were significative higher among lesions with microcalcifications without other radiological findings (SP = 95.8% vs. 84.2%, p-value = 0.026 and NPV = 82.9% vs. 55.2%, p-value < 0.001, respectively). In a second analysis, degree scores of 1, 2, and 3 were considered predictive of malignity. The SE (80.0% vs. 96.8%, p-value < 0.001) and PPV (70.6% vs. 88.3%, p-value: 0.005) were significantly lower among lesions with microcalcifications without other radiological findings, while the SP (85.9% vs. 50.9%, p-value < 0.001) was higher. The enhancement of microcalcifications has low sensitivity in predicting malignancy. However, in certain controversial cases, the absence of CESM enhancement due to its high negative predictive value can help to reduce the number of biopsies for benign lesions.

Clinical prototype implementation enabling an improved day-to-day mammography compression

PURPOSE

In mammography, breast compression is achieved by lowering a compression paddle on the breast. Despite the directive that compression is needed, there is no concrete guideline on its execution. To estimate the degree of compression, current mammography units only provide compression force and breast thickness as parameters. Therefore, radiographers could be induced to mainly determine the level of compression based on compression force and apply the same value to all breast sizes. In this case, smaller breast sizes are exposed to higher pressure. This results in a highly varying perception of discomfort or even pain during the procedure, depending on the breast size.

METHODS

To overcome this imbalance, current research results suggest that pressure might be a more qualified parameter for a more uniform compression among all breast sizes. To utilize pressure, the contact area between breast and compression paddle must be determined. In this paper, we present an easy-to-implement prototype enabling a real-time pressure-based measure without the need of direct patient contact. Using an optical camera, the contact area between the breast and the compression paddle is automatically segmented by a deep learning model.

RESULTS

The model provides a mean pixel accuracy of 96.7% (SD: 2.3%), mean frequency-weighted intersection over union of 88.5% (SD: 6.3%), and a Dice score of 93.6% (SD: 2.2%). The subsequent pressure display is updated more than five times per second which enables the use in clinical routines to set the compression level.

CONCLUSION

This prototype could help guiding to an improved breast compression routine in mammography procedures.

[Trend of inequalities in the performance of mammography in Brazilian capitals in the last ten years]

The objective was to verify the trend of inequality in the realization of mammography exam according to the possession of health insurance plan and schooling from data from the period 2011 to 2020 of VIGITEL. Population-based study with data from the Surveillance System of Risk and Protection Factors for Chronic Diseases by Telephone Survey (VIGITEL) between 2011 and 2020. Outcome: mammography exam in the last 2 years in women aged 50 to 69 years. The magnitude of inequalities of outcome in relation to exposures (health insurance plan and education) was estimated using two indices: inequality slope index (SII) and concentration index (CIX). The prevalence of mammography exam (2011-2020) increased from 74,4% to 78,0%, with a stable trend. The prevalence of those with health insurance plan were 85,7% and 86,4%, and without 63.4% and 71.2%, with an increasing trend. According to education, women with 0-8 years of schooling the prevalence increased from 68,2% to 72,6%, 9-11 years from 80,4% to 80,0% (decreasing trend), 12 years or more 88,0% to 86,6% (decreasing trend). As for the absolute (SII) and relative (CIX) inequality indices of schooling and health insurance plan show that there is a decrease in inequality over the last 10 years.

Changes in the survival of patients with breast cancer: Poland, 2000-2019

PURPOSE

The main aim of this study was to estimate breast cancer survival in Poland over the period from 2000 to 2019 in both sexes.

METHODS

Data were obtained from the Polish National Cancer Registry. The presented metrics included age-standardized 5- and 10-year net survival (NS), median survival times, years of life lost (YLLs), and standardized mortality ratios (SMRs).

RESULTS

Between 2000 and 2019, 315,278 patients (2353 men and 312,925 women; male-to-female ratio 1/100) were diagnosed with breast cancer in Poland. In this period, 721,987 YLLs were linked to breast cancer. Women presented a higher 5- and 10-year age-standardized NS than men (5-year NS: 77.33% for women and 65.47% for men, P < 0.001, common language effect size (CL) 1.00; 10-year NS: 68.75% for women and 49.50% for men, P < 0.001, CL 1.00). Between the earliest and latest studied period, namely 2000-2004 and 2015-2019, there was a statistically significant increase only in female survival (+ 7.32 pp, P < 0.001, CL 1.00). SMRs were significantly higher for women than for men (3.35 vs. 2.89, respectively).

CONCLUSION

Over the last two decades, breast cancer survival in Poland has improved significantly. Nonetheless, special attention should be given to the disparities between sexes and the gap in overall improvement of survival rates compared with other European countries.

Impact of Access to Breast Care For West Texas Program on Early Detection and Regional Breast Cancer Mortality

INTRODUCTION

This study aimed to assess whether the Access to Breast Care for West Texas (ABC4WT) program impacted regional breast cancer detection and mortality in the Texas Council of Governments (COG)1 region.

METHODS

Interrupted time series analyses were utilized to evaluate the impact of the intervention. Spearman's rank correlation and cross-orrelation analyses were performed to assess the relationship between the total number of screenings and (i) the total number of breast cancer detected and (ii) the proportion of early-stage cancer detected and the (pre-whitened) residuals. A three-way interaction model compared pre-and post-intervention mortality in COG 1 with the COG 9 region (control).

RESULTS

Increased screening rate was associated with increased breast and early-stage cancer incidences (P = .001 and P = .002, respectively). There were significant positive cross-correlations between the total number of screenings and the total number of breast cancer detected (r = .996) and the proportion of early-stage cancer detected (r = .709) without a lag even after pre-whitening. Univariate analysis showed that regional mortality decreased with time (P < .001) and after intervention (P = .001). Multivariate analysis did not show any significant difference in time (P = .594), intervention (P = .453), and time and intervention interaction (P = .273). The three-way interaction model showed no difference in the baseline mortality and pre-intervention trend difference in COG 1 and COG 9 regions. However, there was a significant pre-post intervention trend difference in mortality COG 1 compared to the COG 9 region (P = .041).

CONCLUSION

Implementing the ABC4WT program was associated with the early detection of breast cancer and reducing regional mortality in the COG 1 region.

Screening Programs for Breast Cancer: Toward Individualized, Risk-Adapted Strategies of Early Detection

Early detection of breast cancer (BC) comprises two approaches: screening of asymptomatic women in a specified target population at risk (usually a target age range for women at average risk), and early diagnosis for women with BC signs and symptoms. Screening for BC is a key health intervention for early detection. While population-based screening programs have been implemented for age-selected women, the pivotal clinical trials have not addressed the global utility nor the improvement of screening performance by utilizing more refined parameters for patient eligibility, such as individualized risk stratification. In addition, with the exception of the subset of women known to carry germline pathogenetic mutations in (high- or moderately-penetrant) cancer predisposition genes, such as BRCA1 and BRCA2, there has been less success in outreach and service provision for the unaffected relatives of women found to carry a high-risk mutation (i.e., "cascade testing") as it is in these individuals for whom such actionable information can result in cancers (and/or cancer deaths) being averted. Moreover, even in the absence of clinical cancer genetics services, as is the case for the immediate and at least near-term in most countries globally, the capacity to stratify the risk of an individual to develop BC has existed for many years, is available for free online at various sites/platforms, and is increasingly being validated for non-Caucasian populations. Ultimately, a precision approach to BC screening is largely missing. In the present chapter, we aim to address the concept of risk-adapted screening of BC, in multiple facets, and understand if there is a value in the implementation of adapted screening strategies in selected women, outside the established screening prescriptions, in the terms of age-range, screening modality and schedules of imaging.

Breast cancer: a randomized controlled trial assessing the effect of a decision aid on mammography screening uptake: study protocol

Introduction

Breast cancer (BC) is the primary cancer among women. The World Health Organization recommends a bilateral screening mammogram every 2 years for women aged 50 to 74 years. However, it has been shown that there is an absence of information about the benefits and risks of screening. Shared medical decision-making is important to ensure patients are involved in the decision process. Decision aids can facilitative this decision-making process. This article presents a protocol to evaluate the effect of a decision aid on participation rates in the French organized BC screening program.

Methods and analysis

Design and setting. The design is a 2 arm randomized controlled study, performed in the Pays de la Loire region (French West Coast). Randomization will be based on general medicine practices (Primary Care).

Participants

Women aged between 50 and 74 years, eligible for BC screening. In this region, there are 75000 women, and 2800 general practitioners eligible for recruitment.

Intervention

In the « Decision aid for organized cancer screening » arm, the intervention will distribute invitation letters to eligible women combined with the provision of decision aid to these women and their general practitioners and an incentive to implement shared medical decision-making. In the « Standard organized cancer screening » arm, only the screening invitation will be sent to eligible women.

Primary endpoint

BC screening participation rates will be assessed after an 18-month follow-up period.

Statistical analysis

In this non-inferiority trial, the percentage of women who are up-to-date with their screening at 18 months after the intervention will be compared across arms using a generalized mixed linear model.

Discussion

The research team expect to demonstrate that providing a better explanation of the benefits and risks of BC screening is not at odds with screening participation. The study results should help policy makers thinking about implementing shared medical decision-making within the framework of organized BC screening programs in the future.

Ethics and dissemination

On 6 December 2021, the protocol received a favorable opinion from the French Committee for the Protection of Persons (2021-A01583-38). This study is registered with ClinicalTrials.gov, number NCT05607849. (Version 1, November 7, 2022; https://www.clinicaltrials.gov/ct2/show/NCT05607849). The study findings will be used for publication in peer-reviewed scientific journals and presentations in scientific meetings.

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Disparities Associated With Patient Adherence to BI-RADS 3 Assessment Follow-up Recommendations for Mammography and Ultrasound

OBJECTIVE

To assess the relationship between sociodemographic factors and adherence rates in patients with a BI-RADS 3 assessment.

METHODS

This retrospective cohort study reviewed data from all patients with a BI-RADS 3 assessment on mammography and ultrasound examinations at a single, multisite academic institution, which serves a diverse urban-suburban population, from January 1, 2015, to December 13, 2017. Appropriate follow-up was defined as returning for the first follow-up examination 3 to 9 months after the index examination. Associations between BI-RADS 3 adherence rates and patient sociodemographic characteristics were evaluated using logistic regression.

RESULTS

There were 4,038 patients in our study period; 2,437 patients (60%) had appropriate follow-up, 765 (19%) patients had delayed follow-up, and 836 patients (21%) were lost to follow-up. The overall malignancy rate was 1.4% (46 of 3,202). Older age, retired employment status, and Medicare insurance status were associated with increased adherence to BI-RADS 3 follow-up recommendations. Black race, single relationship status, Medicaid and self-pay insurance status, and living in a top 15% disadvantaged zip code were associated with decreased adherence. On multivariate analysis, older age remained associated with increased adherence and Medicaid insurance status with decreased adherence. Time between index examination and cancer diagnosis was shorter in patients who had timely follow-up (202 days [interquartile range 183-358] versus 392 days [interquartile range 365-563], P ≤ .001), although there was not a significant difference in stage at diagnosis (P = .46).

DISCUSSION

Multiple sociodemographic factors are associated with low adherence to BI-RADS 3 follow-up recommendations suggesting that more frequent and targeted interventions are needed to close disparity gaps.

Feasibility of a Reinforcement Learning-Enabled Digital Health Intervention to Promote Mammograms: Retrospective, Single-Arm, Observational Study

BACKGROUND

Preventive screenings such as mammograms promote health and detect disease. However, mammogram attendance lags clinical guidelines, with roughly one-quarter of women not completing their recommended mammograms. A scalable digital health intervention leveraging behavioral science and reinforcement learning and delivered via email was implemented in a US health system to promote uptake of recommended mammograms among patients who were 1 or more years overdue for the screening (ie, 2 or more years from last mammogram).

OBJECTIVE

The aim of this study was to establish the feasibility of a reinforcement learning-enabled mammography digital health intervention delivered via email. The research aims included understanding the intervention's reach and ability to elicit behavioral outcomes of scheduling and attending mammograms, as well as understanding reach and behavioral outcomes for women of different ages, races, educational attainment levels, and household incomes.

METHODS

The digital health intervention was implemented in a large Catholic health system in the Midwestern United States and targeted the system's existing patients who had not received a recommended mammogram in 2 or more years. From August 2020 to July 2022, 139,164 eligible women received behavioral science-based email messages assembled and delivered by a reinforcement learning model to encourage clinically recommended mammograms. Target outcome behaviors included scheduling and ultimately attending the mammogram appointment.

RESULTS

In total, 139,164 women received at least one intervention email during the study period, and 81.52% engaged with at least one email. Deliverability of emails exceeded 98%. Among message recipients, 24.99% scheduled mammograms and 22.02% attended mammograms (88.08% attendance rate among women who scheduled appointments). Results indicate no practical differences in the frequency at which people engage with the intervention or take action following a message based on their age, race, educational attainment, or household income, suggesting the intervention may equitably drive mammography across diverse populations.

CONCLUSIONS

The reinforcement learning-enabled email intervention is feasible to implement in a health system to engage patients who are overdue for their mammograms to schedule and attend a recommended screening. In this feasibility study, the intervention was associated with scheduling and attending mammograms for patients who were significantly overdue for recommended screening. Moreover, the intervention showed proportionate reach across demographic subpopulations. This suggests that the intervention may be effective at engaging patients of many different backgrounds who are overdue for screening. Future research will establish the effectiveness of this type of intervention compared to typical health system outreach to patients who have not had recommended screenings as well as identify ways to enhance its reach and impact.

"It Will Lead You to Make Better Decisions about Your Health"-A Focus Group and Survey Study on Women's Attitudes towards Risk-Based Breast Cancer Screening and Personalised Risk Assessments

Singapore launched a population-based organised mammography screening (MAM) programme in 2002. However, uptake is low. A better understanding of breast cancer (BC) risk factors has generated interest in shifting from a one-size-fits-all to a risk-based screening approach. However, public acceptability of the change is lacking. Focus group discussions (FGD) were conducted with 54 women (median age 37.5 years) with no BC history. Eight online sessions were transcribed, coded, and thematically analysed. Additionally, we surveyed 993 participants in a risk-based MAM study on how they felt in anticipation of receiving their risk profiles. Attitudes towards MAM (e.g., fear, low perceived risk) have remained unchanged for ~25 years. However, FGD participants reported that they would be more likely to attend routine mammography after having their BC risks assessed, despite uncertainty and concerns about risk-based screening. This insight was reinforced by the survey participants reporting more positive than negative feelings before receiving their risk reports. There is enthusiasm in knowing personal disease risk but concerns about the level of support for individuals learning they are at higher risk for breast cancer. Our results support the empowering of Singaporean women with personal health information to improve MAM uptake.

No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans

Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound scans to evaluate the possibility of inexpensive, fully automated breast ultrasound acquisition and preliminary interpretation without an experienced sonographer or radiologist. This study was conducted using examinations from a curated data set from a previously published clinical study of breast VSI. Examinations in this data set were obtained by medical students without prior ultrasound experience who performed VSI using a portable Butterfly iQ ultrasound probe. Standard of care ultrasound exams were performed concurrently by an experienced sonographer using a high-end ultrasound machine. Expert-selected VSI images and standard of care images were input into S-Detect which output mass features and classification as "possibly benign" and "possibly malignant." Subsequent comparison of the S-Detect VSI report was made between 1) the standard of care ultrasound report by an expert radiologist, 2) the standard of care ultrasound S-Detect report, 3) the VSI report by an expert radiologist, and 4) the pathological diagnosis. There were 115 masses analyzed by S-Detect from the curated data set. There was substantial agreement of the S-Detect interpretation of VSI among cancers, cysts, fibroadenomas, and lipomas to the expert standard of care ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), the standard of care ultrasound S-Detect interpretation (Cohen's κ = 0.79 (0.65-0.94 95% CI), p<0.0001), the expert VSI ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), and the pathological diagnosis (Cohen's κ = 0.80 (0.64-0.95 95% CI), p<0.0001). All pathologically proven cancers (n = 20) were designated as "possibly malignant" by S-Detect with a sensitivity of 100% and specificity of 86%. Integration of artificial intelligence and VSI could allow both acquisition and interpretation of ultrasound images without a sonographer and radiologist. This approach holds potential for increasing access to ultrasound imaging and therefore improving outcomes related to breast cancer in low- and middle- income countries.

Effectiveness of Mammography Screening on Breast Cancer Mortality – A Study Protocol for Emulation of Target Trials Using German Health Claims Data

Background

The efficacy of mammography screening in reducing breast cancer mortality has been demonstrated in randomized trials. However, treatment options - and hence prognosis – for advanced tumor stages as well as mammography techniques have considerably improved since completion of these trials. Consequently, the effectiveness of mammography screening under current conditions is unclear and controversial. The German mammography screening program (MSP), an organized population-based screening program, was gradually introduced between 2005 and 2008 and achieved nation-wide coverage in 2009.

Objective

We describe in detail a study protocol for investigating the effectiveness of the German MSP in reducing breast cancer mortality in women aged 50 to 69 years based on health claims data. Specifically, the proposed study aims at estimating per-protocol effects of several screening strategies on cumulative breast cancer mortality. The first analysis will be conducted once 10-year follow-up data are available.

Methods and Analysis

We will use claims data from five statutory health insurance providers in Germany, covering approximately 37.6 million individuals. To estimate the effectiveness of the MSP, hypothetical target trials will be emulated across time, an approach that has been demonstrated to minimize design-related biases. Specifically, the primary contrast will be in terms of the cumulative breast cancer mortality comparing the screening strategies of “never screen” versus “regular screening as intended by the MSP”.

Ethics and Dissemination

In Germany, the utilization of data from health insurances for scientific research is regulated by the Code of Social Law. All involved health insurance providers as well as the responsible authorities approved the use of the health claims data for this study. The Ethics Committee of the University of Bremen determined that studies based on claims data are exempt from institutional review. The findings of the proposed study will be published in peer-reviewed journals.

Evaluation of participation and performance indicators in a breast cancer screening program in Saudi Arabia

OBJECTIVES

To evaluate early performance indicators for breast cancer screening at the King Abdulaziz University Hospital in Saudi Arabia.

METHODS

This study retrospectively evaluated data from women who underwent their first breast cancer screening program in Jeddah, Saudi Arabia between 2012 and 2019. Data on screening results were used to estimate performance indicators and generate descriptive statistics.

RESULTS

Of the 16000 women invited from 2012 to 2019, a total of 1911 (11.9%) participated. The majority of women (68.8%) were between 40 and 55 years old. Based on the screening process results, 26.6%, 40.1%, 9.7%, 1.3%, 0.7%, and 5.2% of women had BI-RADS scores of R1, R2, R3, R4, R5, and R0 respectively. The remaining 16.3% did not have mammogram records. The recall rate, or the percentage of women who underwent further evaluation, was 19.9%; 18.9% underwent a biopsy procedure. In addition, 1.6% of women had cancer screen-detected, although only 0.7% were diagnosed with breast cancer.

CONCLUSION

In light of the low participation and high recall rates, it is essential that the screening program utilizes performance indicators to optimize resource utilization and ensure the quality of the service provided. Additionally, a national framework and standardized performance indicators could mitigate this problem for other cancer screening programs.

90Y-Labeled Gold Nanoparticle Depot (NPD) Combined with Anti-PD-L1 Antibodies Strongly Inhibits the Growth of 4T1 Tumors in Immunocompetent Mice and Induces an Abscopal Effect on a Distant Non-Irradiated Tumor

The effectiveness and normal tissue toxicity of a novel nanoparticle depot (NPD) brachytherapy seed incorporating gold nanoparticles (AuNPs) labeled with β-particle emitting, ⁹⁰Y (termed a "radiation nanomedicine"), were studied for the treatment of 4T1 triple-negative murine mammary carcinoma tumors in Balb/c mice and for inducing an abscopal effect on a distant non-irradiated tumor alone or combined with anti-PD-L1 immune checkpoint antibodies. Balb/c mice with two subcutaneous 4T1 tumors─a primary tumor and a distant secondary tumor were implanted intratumorally (i.t.) in the primary tumor with NPD incorporating 3.5 MBq of ⁹⁰Y-AuNPs (1 × 10¹⁴ AuNPs) or unlabeled AuNPs, alone or combined with systemically administered anti-PD-L1 antibodies (200 μg i.p. three times/week for 2 weeks) or received anti-PD-L1 antibodies alone or no treatment. The primary tumor was strongly growth-inhibited over 14 d by NPD incorporating ⁹⁰Y-AuNPs but only very modestly inhibited by NPD incorporating unlabeled AuNPs. Anti-PD-L1 antibodies alone were ineffective, and combining anti-PD-L1 antibodies with NPD incorporating ⁹⁰Y-AuNPs did not further inhibit the growth of the primary tumor. Secondary tumor growth was inhibited by treatment of the primary tumor with NPD incorporating ⁹⁰Y-AuNPs, and growth inhibition was enhanced by anti-PD-L1 antibodies. Treatment of the primary tumor with NPD incorporating unlabeled AuNPs or anti-PD-L1 antibodies alone had no effect on secondary tumor growth. Biodistribution studies showed high uptake of ⁹⁰Y in the primary tumor [516-810% implanted dose/g (%ID/g)] but very low uptake in the secondary tumor (0.033-0.16% ID/g) and in normal tissues (<0.5% ID/g) except for kidneys (5-8% ID/g). Very high radiation absorbed doses were estimated for the primary tumor (472 Gy) but very low doses in the secondary tumor (0.13 Gy). There was highdose-heterogeneity in the primary tumor with doses as high as 9964 Gy in close proximity to the NPD, decreasing rapidly with distance from the NPD. Normal organ doses were low (<1 Gy) except for kidneys (4 Gy). No normal tissue toxicity was observed, but white blood cell counts (WBC) decreased in tumor-bearing mice treated with NPD incorporating ⁹⁰Y-AuNPs. Decreased WBC counts were interpreted as tumor response and not toxicity since these were higher than that in healthy non-tumor-bearing mice, and there was a direct association between WBC counts and 4T1 tumor burden. We conclude that implantation of NPD incorporating ⁹⁰Y-AuNPs into a primary 4T1 tumor in Balb/c mice strongly inhibited tumor growth and combined with anti-PD-L1 antibodies induced an abscopal effect on a distant secondary tumor. This radiation nanomedicine is promising for the local treatment of triple-negative breast cancer tumors in patients, and these therapeutic effects may extend to non-irradiated lesions, especially when combined with checkpoint immunotherapy.

Accuracy of cone-beam computed tomography, digital mammography and digital breast tomosynthesis for microcalcifications and margins to microcalcifications in breast specimens

Accurate determination of resection margins in breast specimens is important as complete removal of malignancy is a prerequisite for patients' outcome. Mammography (DM) as 2D-technique provides only limited value in margin assessment. Therefore, we investigated whether cone-beam computed tomography (CBCT) or digital breast tomosynthesis (DBT) has incremental value in assessing margins to microcalcifications. Three independent readers investigated breast specimens for presence of microcalcifications and the smallest distance to margins. Histopathology served as gold standard. Microcalcifications were detected in 15 out of 21 included specimens (71%). Pooled sensitivity for DM, DBT and CBCT for microcalcifications compared to preoperative DM was 0.98 (CI 0.94-0.99), 0.83 (CI 0.73-0.94) and 0.94 (CI 0.87-0.99), pooled specificity was 0.99 (CI 0.99-0.99), 0.73 (CI 0.51-0.96) and 0.60 (CI 0.35-0.85). Mean measurement error for margin determination for DM, DBT and CBCT was 10 mm, 14 mm and 6 mm (p = 0.002) with significant difference between CBCT and the other devices (p < 0.03). Mean reading time required by the readers to analyze DM, DBT and CBCT, was 36, 43 and 54 s (p < 0.001). Although DM allows reliable detection of microcalcifications, measurement of resection margin was significantly more accurate with CBCT. Thus, a combination of methods or improved CBCT might provide a more accurate determination of disease-free margins in breast specimens.

10-year opportunistic mammographic screening scenario in Brazil and its impact on breast cancer early detection: a nationwide population-based study

Background

Methods

Results

Conclusions

Effectiveness of Organized Mammography Screening for Different Breast Cancer Molecular Subtypes

Simple Summary

We evaluated the short-term effectiveness of a mammography screening program in all women who participated in the screening program and were diagnosed with screen-detected or interval breast cancer (BC) in Flanders (2008–2018). The evaluation was performed for the major molecular subtypes of invasive BC separately and considering the regularity of participation. We found that screen-detected BC was more likely to be diagnosed at early stages than interval BC of luminal, luminal-HER2-positive, and triple-negative BC (TNBC) type, but not for the human epidermal growth factor receptor 2-positive (HER2 positive) subtype. In addition, regular participation was related to a higher likelihood of screening detection than irregular participation for luminal, luminal-HER2-positive, and TNBC, but not for the HER2 positive subtype, either. Our results indicate that regular screening as compared to irregular screening is effective for all breast cancers except for the HER2 subtype.

Abstract

Background: Screening program effectiveness is generally evaluated for breast cancer (BC) as one disease and without considering the regularity of participation, while this might have an impact on detection rate. Objectives: To evaluate the short-term effectiveness of a mammography screening program for the major molecular subtypes of invasive BC. Methods: All women who participated in the screening program and were diagnosed with screen-detected or interval BC in Flanders were included in the study (2008–2018). Molecular subtypes considered were luminal and luminal-HER2-positive, human epidermal growth factor receptor 2-positive, and triple-negative BC (TNBC). The relationship between the BC stage at diagnosis (early (I–II) versus advanced (III–IV)) and the method of detection (screen-detected or interval) and the relationship between the method of detection and participation regularity (regular versus irregular) were evaluated by multi-variable logistic regression models. All models were performed for each molecular subtype and adjusted for age. Results: Among the 12,318 included women, BC of luminal and luminal-HER2-positive subtypes accounted for 70.9% and 11.3%, respectively. Screen-detected BC was more likely to be diagnosed at early stages than interval BC with varied effect sizes for luminal, luminal-HER2-positive, and TNBC with OR:2.82 (95% CI: 2.45–3.25), OR:2.39 (95% CI: 1.77–3.24), and OR:2.29 (95% CI: 1.34–4.05), respectively. Regular participation was related to a higher likelihood of screening detection than irregular participation for luminal, luminal-HER2-positive, and TNBC with OR:1.21 (95% CI: 1.09–1.34), OR: 1.79 (95% CI: 1.38–2.33), and OR: 1.62 (95% CI: 1.10–2.41), respectively. Conclusions: Regular screening as compared to irregular screening is effective for all breast cancers except for the HER2 subtype.

Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis

OBJECTIVES

Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of breast cancer. However, it is questioned whether this increased risk estimate is applicable to current breast density classification methods. Therefore, the aim of this study was to further investigate and clarify the association between mammographic density and breast cancer risk based on current literature.

METHODS

Medline, Embase and Web of Science were systematically searched for articles published since 2013, that used BI-RADS lexicon 5th edition and incorporated data on digital mammography. Crude and maximally confounder-adjusted data were pooled in odds ratios (ORs) using random-effects models. Heterogeneity regarding breast cancer risks were investigated using I² statistic, stratified and sensitivity analyses.

RESULTS

Nine observational studies were included. Having extremely dense breast tissue (BI-RADS density D) resulted in a 2.11-fold (95% CI 1.84-2.42) increased breast cancer risk compared to having scattered dense breast tissue (BI-RADS density B). Sensitivity analysis showed that when only using data that had adjusted for age and BMI, the breast cancer risk was 1.83-fold (95% CI 1.52-2.21) increased. Both results were statistically significant and homogenous.

CONCLUSIONS

Mammographic breast density BI-RADS D is associated with an approximately two-fold increased risk of breast cancer compared to having BI-RADS density B in general population women. This is a novel and lower risk estimate compared to previously reported and might be explained due to the use of digital mammography and BI-RADS lexicon 5th edition.

A Score to Predict the Malignancy of a Breast Lesion Based on Different Contrast Enhancement Patterns in Contrast-Enhanced Spectral Mammography

Background: To create a predictive score of malignancy of a breast lesion based on the main contrast enhancement features ascertained by contrast-enhanced spectral mammography (CESM). Methods: In this single-centre prospective study, patients with suspicious breast lesions (BIRADS > 3) were enrolled between January 2013 and February 2022. All participants underwent CESM prior to breast biopsy, and eventually surgery. A radiologist with 20 years’ experience in breast imaging evaluated the presence or absence of enhancement and the following enhancement descriptors: intensity, pattern, margin, and ground glass. A score of 0 or 1 was given for each descriptor, depending on whether the enhancement characteristic was predictive of benignity or malignancy (both in situ and invasive). Then, an overall enhancement score ranging from 0 to 4 was obtained. The histological results were considered the gold standard in the evaluation of the relationship between enhancement patterns and malignancy. Results: A total of 321 women (median age: 51 years; range: 22−83) with 377 suspicious breast lesions were evaluated. Two hundred forty-nine lesions (66%) have malignant histological results (217 invasive and 32 in situ). Considering an overall enhancement score ≥ 2 as predictive of malignancy, we obtain an overall sensitivity of 92.4%; specificity of 89.8%; positive predictive value of 94.7%; and negative predictive value of 85.8%. Conclusions: Our proposed predictive score on the enhancement descriptors of CESM to predict the malignancy of a breast lesion shows excellent results and can help in early breast cancer diagnosis and in avoiding unnecessary biopsies.

Addressing Misinformation About the Canadian Breast Screening Guidelines

Screening mammography has been shown to reduce breast cancer mortality by 41% in screened women ages 40-69 years. There is misinformation about breast screening and the Canadian breast screening guidelines. This can decrease confidence in screening mammography and can lead to suboptimal recommendations. We review some of this misinformation to help radiologists and referring physicians navigate the varied international and provincial guidelines. We address the ages to start and stop breast screening. We explore how these recommendations may vary for specific populations such as patients who are at increased risk, transgender patients and minorities. We identify who would benefit from supplemental screening and review the available supplemental screening modalities including ultrasound, MRI, contrast-enhanced mammography and others. We describe emerging technologies including the potential use of artificial intelligence for breast screening. We provide background on why screening policies vary across the country between provinces and territories. This review is intended to help radiologists and referring physicians understand and navigate the varied international and provincial recommendations and guidelines and make the best recommendations for their patients.

Misinformation and Facts about Breast Cancer Screening

Quality medical practice is based on science and evidence. For over a half-century, the efficacy of breast cancer screening has been challenged, particularly for women aged 40–49. As each false claim has been raised, it has been addressed and refuted based on science and evidence. Nevertheless, misinformation continues to be promoted, resulting in confusion for women and their physicians. Early detection has been proven to save lives for women aged 40–74 in randomized controlled trials of mammography screening. Observational studies, failure analyses, and incidence of death studies have provided evidence that there is a major benefit when screening is introduced to the general population. In large part due to screening, there has been an over 40% decline in deaths from breast cancer since 1990. Nevertheless, misinformation about screening continues to be promoted, adding to the confusion. Despite claims to the contrary, a careful reading of the guidelines issued by major groups such as the U.S. Preventive Services Task Force and the American College of Physicians shows that they all agree that most lives are saved by screening starting at the age of 40. There is no scientific support for using the age of 50 as a threshold for screening. All women should be provided with the facts and not false information about breast cancer screening so that they can make “informed decisions” for themselves about whether to participate.

The Impact of Organised Screening Programs on Breast Cancer Stage at Diagnosis for Canadian Women Aged 40-49 and 50-59

The relationship between Canadian mammography screening practices for women 40−49 and breast cancer (BC) stage at diagnosis in women 40−49 and 50−59 years was assessed using data from the Canadian Cancer Registry, provincial/territorial screening practices, and screening information from the Canadian Community Health Survey. For the 2010 to 2017 period, women aged 40−49 were diagnosed with lesser relative proportions of stage I BC (35.7 vs. 45.3%; p < 0.001), but greater proportions of stage II (42.6 vs. 36.7%, p < 0.001) and III (17.3 vs. 13.1%, p < 0.001) compared to women 50−59. Stage IV was lower among women 40−49 than 50−59 (4.4% vs. 4.8%, p = 0.005). Jurisdictions with organised screening programs for women 40−49 with annual recall (screeners) were compared with those without (comparators). Women aged 40−49 in comparator jurisdictions had higher proportions of stages II (43.7% vs. 40.7%, p < 0.001), III (18.3% vs. 15.6%, p < 0.001) and IV (4.6% vs. 3.9%, p = 0.001) compared to their peers in screener jurisdictions. Based on screening practices for women aged 40−49, women aged 50−59 had higher proportions of stages II (37.2% vs. 36.0%, p = 0.003) and III (13.6% vs. 12.3%, p < 0.001) in the comparator versus screener groups. The results of this study can be used to reassess the optimum lower age for BC screening in Canada.

Mammography screening is associated with more favourable breast cancer tumour characteristics and better overall survival: case-only analysis of 3739 Asian breast cancer patients

BACKGROUND

Early detection of breast cancer (BC) through mammography screening (MAM) is known to reduce mortality. We examined the differential effect that mammography has on BC characteristics and overall survival and the sociodemographic determinants of MAM utilization in a multi-ethnic Asian population.

METHODS

This study included 3739 BC patients from the Singapore Breast Cancer Cohort (2010-2018). Self-reported sociodemographic characteristics were collected using a structured questionnaire. Clinical data were obtained through medical records. Patients were classified as screeners (last screening mammogram ≤ 2 years before diagnosis), non-screeners (aware but did not attend or last screen > 2years), and those unaware of MAM. Associations between MAM behaviour (MB) and sociodemographic factors and MB and tumour characteristics were examined using multinomial regression. Ten-year overall survival was modelled using Cox regression.

RESULTS

Patients unaware of screening were more likely diagnosed with late stage (OR_{stage III vs stage I (Ref)} [95% CI]: 4.94 [3.45-7.07], p < 0.001), high grade (OR_{poorly vs well-differentiated (reference)}: 1.53 [1.06-2.20], p = 0.022), nodal-positive, large size (OR_{>5cm vs ≤2cm (reference)}: 5.06 [3.10-8.25], p < 0.001), and HER2-positive tumours (OR_{HER2-negative vs HER2-positive (reference)}: 0.72 [0.53-0.97], p = 0.028). Similar trends were observed between screeners and non-screeners with smaller effect sizes. Overall survival was significantly shorter than screeners in the both groups (HR_{non-screeners}: 1.89 [1.22-2.94], p = 0.005; HR_unaware: 2.90 [1.69-4.98], p < 0.001). Non-screeners and those unaware were less health conscious, older, of Malay ethnicity, less highly educated, of lower socioeconomic status, more frequently ever smokers, and less physically active. Among screeners, there were more reported personal histories of benign breast surgeries or gynaecological conditions and positive family history of breast cancer.

CONCLUSIONS

Mammography attendance is associated with more favourable BC characteristics and overall survival. Disparities in the utility of MAM services suggest that different strategies may be needed to improve MAM uptake.

The Benefits of Early Detection: Evidence From Modern International Mammography Service Screening Programs

Research from randomized controlled trials initiated up to 60 years ago consistently confirms that regular screening with mammography significantly reduces breast cancer mortality. Despite this success, there is ongoing debate regarding the efficacy of screening, which is confounded by technologic advances and concerns about cost, overdiagnosis, overtreatment, and equitable care of diverse patient populations. More recent screening research, designed to quell the debates, derives data from variable study designs, each with unique strengths and weaknesses. This article reviews observational population-based screening research that has followed the early initial long-term randomized controlled trials that are no longer practical or ethical to perform. The advantages and disadvantages of observational data and study design are outlined, including the three subtypes of population-based observational studies: cohort/case-control, trend, and incidence-based mortality/staging. The most recent research, typically performed in countries that administer screening mammography to women through centralized health service programs and directly track patient-specific outcomes and detection data, is summarized. These data are essential to understand and inform construction of effective new databases that facilitate continuous assessment of optimal screening techniques in the current era of rapidly developing medical technology, combined with a focus on health care that is both personal and equitable.

An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks

A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially into one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for (1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; (2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and (3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies.

Irregular screening participation increases advanced stage breast cancer at diagnosis: A population-based study

OBJECTIVE

To evaluate the effect of irregular screening behaviour on the risk of advanced stage breast cancer at diagnosis in Flanders.

METHODS

All women aged 50-69 who were invited to the organized breast cancer screening and diagnosed with breast cancer before age 72 from 2001 to 2018 were included. All prevalent screen and interval cancers within 2 years of a prevalent screen were excluded. Screening behaviour was categorized based on the number of invitations and performed screenings. Four groups were defined: regular, irregular, only-once, and never attenders. Advanced stage cancer was defined as a stage III + breast cancer. The association between screening regularity and breast cancer stage at diagnosis was evaluated in multivariable logistic regression models, taking age of diagnosis and socio-economic status into account.

RESULTS

In total 13.5% of the 38,005 breast cancer cases were diagnosed at the advanced stage. Compared to the regular attenders, the risk of advanced stage breast cancer for the irregular attenders, women who participated only-once, and never attenders was significantly higher with OR_adjusted:1.17 (95%CI:1.06-1.29) and OR_adjusted:2.18 (95%CI:1.94-2.45), and OR_adjusted:5.95 (95%CI:5.33-6.65), respectively.

CONCLUSIONS

In our study, never attenders were nearly six times more likely to be diagnosed with advanced stage breast cancer than regular attenders, which was much higher than the estimates published thus far. An explanation for this is that the ever screened women is a heterogeneous group regarding the participation profiles which also includes irregular and only-once attenders. The benefit of regular screening should be informed to all women invited for screening.

Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution

PURPOSE

To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography.

METHOD

Mammograms were consecutively collected from 5080 patients who underwent breast cancer screening from January 2015 to March 2017. Of these, 93 patients (136 breasts, mean age, 50 ± 7 years) had microcalcifications in their breasts on mammograms. We applied an artificial intelligence model known as a fast SR convolutional neural network to the mammograms. SR and original mammograms were visually evaluated by four breast radiologists using a 5-point scale (1: original mammograms are strongly preferred, 5: SR mammograms are strongly preferred) for the detection, diagnostic quality, contrast, sharpness, and noise of microcalcifications. Mammograms were quantitatively evaluated using a perception-based image-quality evaluator (PIQE).

RESULTS

All radiologists rated the SR mammograms better than the original ones in terms of detection, diagnostic quality, contrast, and sharpness of microcalcifications. These ratings were significantly different according to the Wilcoxon signed-rank test (p <.001), while the noise score of the three radiologists was significantly lower (p <.001). According to PIQE, SR mammograms were rated better than the original mammograms, showing a significant difference by paired t-test (p <.001).

CONCLUSION

An SR model based on deep learning can improve the visibility of microcalcifications in mammography and help detect and diagnose them in mammograms.

Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis

In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for related studies in Web of Science, Scopus, PubMed, Google Scholar and Embase. The studies were screened in two stages to exclude the unrelated studies and duplicates. Finally, 36 studies containing 68 machine learning models were included in this meta-analysis. The area under the curve (AUC), hierarchical summary receiver operating characteristics (HSROC) curve, pooled sensitivity and pooled specificity were estimated using a bivariate Reitsma model. Overall AUC, pooled sensitivity and pooled specificity were 0.90 (95% CI: 0.85–0.90), 0.83 (95% CI: 0.78–0.87) and 0.84 (95% CI: 0.81–0.87), respectively. Additionally, the three significant covariates identified in this study were country (p = 0.003), source (p = 0.002) and classifier (p = 0.016). The type of data covariate was not statistically significant (p = 0.121). Additionally, Deeks’ linear regression test indicated that there exists a publication bias in the included studies (p = 0.002). Thus, the results should be interpreted with caution.

3D Harmonic and Subharmonic Imaging for Characterizing Breast Lesions: A Multi-Center Clinical Trial

OBJECTIVE

Breast cancer is the most frequent type of cancer among women. This multi-center study assessed the ability of 3D contrast-enhanced ultrasound to characterize suspicious breast lesions using clinical assessments and quantitative parameters.

METHODS

Women with suspicious breast lesions scheduled for biopsy were enrolled in this prospective, study. Following 2D grayscale ultrasound and power Doppler imaging (PDI), a contrast agent (Definity; Lantheus) was administrated. Contrast-enhanced 3D harmonic imaging (HI; transmitting/receiving at 5.0/10.0 MHz), as well as 3D subharmonic imaging (SHI; transmitting/receiving at 5.8/2.9 MHz), were performed using a modified Logiq 9 scanner (GE Healthcare). Five radiologists independently scored the imaging modes (including standard-of-care imaging) using a 7-point BIRADS scale as well as lesion vascularity and diagnostic confidence. Parametric volumes were constructed from time-intensity curves for vascular heterogeneity, perfusion, and area under the curve. Diagnostic accuracy was determined relative to pathology using receiver operating characteristic (ROC) and reverse, step-wise logistical regression analyses. The κ-statistic was calculated for inter-reader agreement.

RESULTS

Data were successfully acquired in 219 cases and biopsies indicated 164 (75%) benign and 55 (25%) malignant lesions. SHI depicted more anastomoses and vascularity than HI (P < .021), but there were no differences by pathology (P > .27). Ultrasound achieved accuracies of 82 to 85%, which was significantly better than standard-of-care imaging (72%; P < .03). SHI increased diagnostic confidence by 3 to 6% (P < .05), but inter-reader agreements were medium to low (κ < 0.52). The best regression model achieved 97% accuracy by combining clinical reads and parametric SHI.

CONCLUSIONS

Combining quantitative 3D SHI parameters and clinical assessments improves the characterization of suspicious breast lesions.

Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques

BACKGROUND AND OBJECTIVE

Computer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Four categories of cases were included: Mass, Calcification, Architectural Distortions, and Normal from a private digital mammographic database including 413 cases. For all cases, Prior mammograms (typically scanned 1 year before) were all reported as Normal, while Current mammograms were diagnosed as cancerous (confirmed by biopsies) or healthy.

METHODS

We propose to apply the YOLO-based fusion model to the Current mammograms for breast lesions detection and classification. Then apply the same model retrospectively to synthetic mammograms for an early cancer prediction, where the synthetic mammograms were generated from the Prior mammograms by using the image-to-image translation models, CycleGAN and Pix2Pix.

RESULTS

Evaluation results showed that our methodology could significantly detect and classify breast lesions on Current mammograms with a highest rate of 93% ± 0.118 for Mass lesions, 88% ± 0.09 for Calcification lesions, and 95% ± 0.06 for Architectural Distortion lesions. In addition, we reported evaluation results on Prior mammograms with a highest rate of 36% ± 0.01 for Mass lesions, 14% ± 0.01 for Calcification lesions, and 50% ± 0.02 for Architectural Distortion lesions. Normal mammograms were accordingly classified with an accuracy rate of 92% ± 0.09 and 90% ± 0.06 respectively on Current and Prior exams.

CONCLUSIONS

Our proposed framework was first developed to help detecting and identifying suspicious breast lesions in X-ray mammograms on their Current screening. The work was also suggested to reduce the temporal changes between pairs of Prior and follow-up screenings for early predicting the location and type of abnormalities in Prior mammogram screening. The paper presented a CAD method to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning and image-to-image translation for a biomedical application.

Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population

BACKGROUND

Estimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval.

METHODS

We included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%).

RESULTS

Obesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval.

CONCLUSION

Most women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.

Screening MRI in Women at Intermediate Breast Cancer Risk: An Update of the Recent Literature

Guidelines issued by the American Cancer Society (ACS) in 2007 recommend neither for nor against screening MRI in women at intermediate breast cancer risk (15%–20%), including those with dense breast tissue, a history of lobular neoplasia or atypical ductal hyperplasia (ADH), or a prior breast cancer, because of scarce supporting evidence about the utility of MRI in these specific patient populations. However, since the issuance of the ACS guidelines in 2007, multiple investigations have found that women at intermediate risk may be suitable candidates for screening MRI, given the high detection rates of early-stage cancers and acceptable false-positive rates. For women with dense breast tissue, the Dense Tissue and Early Breast Neoplasm Screening trial reported that the incremental cancer detection rate (CDR) by MRI exceeded 16 cancers per 1000 examinations but decreased in the second round of screening; this decrease in CDR, however, occurred alongside a marked decrease in the false-positive rate. For women with lobular neoplasia or ADH, single-institution retrospective analyses have shown CDRs mostly ranging from 11 to 16 cancers per 1000 MRI examinations, with women with lobular carcinoma in situ benefitting more than women with atypical lobular hyperplasia or ADH. For patients with a prior breast cancer, the cancer yield by MRI varies widely but mostly ranges from 8 to 20 cancers per 1000 examinations, with certain subpopulations more likely to benefit, such as those with dense breasts. This article reviews and summarizes more recent studies on MRI screening of intermediate-risk women.