Breast cancer risk-assessment models

Breast Cancer Screening in the Intermediate-Risk Population: Falling Through the Cracks?

Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.

A structured process for the validation of a decision-analytic model: application to a cost-effectiveness model for risk-stratified national breast screening

BACKGROUND

Decision-makers require knowledge of the strengths and weaknesses of decision-analytic models used to evaluate healthcare interventions to be able to confidently use the results of such models to inform policy. A number of aspects of model validity have previously been described, but no systematic approach to assessing the validity of a model has been proposed. This study aimed to consolidate the different aspects of model validity into a step-by-step approach to assessing the strengths and weaknesses of a decision-analytic model.

METHODS

A pre-defined set of steps were used to conduct the validation process of an exemplar early decision-analytic-model-based cost-effectiveness analysis of a risk-stratified national breast cancer screening programme [UK healthcare perspective; lifetime horizon; costs (£; 2021)]. Internal validation was assessed in terms of descriptive validity, technical validity and face validity. External validation was assessed in terms of operational validation, convergent validity (or corroboration) and predictive validity.

RESULTS

The results outline the findings of each step of internal and external validation of the early decision-analytic-model and present the validated model (called 'MANC-RISK-SCREEN'). The positive aspects in terms of meeting internal validation requirements are shown together with the remaining limitations of MANC-RISK-SCREEN.

CONCLUSION

Following a transparent and structured validation process, MANC-RISK-SCREEN has been shown to have satisfactory internal and external validity for use in informing resource allocation decision-making. We suggest that MANC-RISK-SCREEN can be used to assess the cost-effectiveness of exemplars of risk-stratified national breast cancer screening programmes (NBSP) from the UK perspective.

IMPLICATIONS

A step-by-step process for conducting the validation of a decision-analytic model was developed for future use by health economists. Using this approach may help researchers to fully demonstrate the strengths and limitations of their model to decision-makers.

Shared decision-making for supporting women's decisions about breast cancer screening

BACKGROUND

In breast cancer screening programmes, women may have discussions with a healthcare provider to help them decide whether or not they wish to join the breast cancer screening programme. This process is called shared decision-making (SDM) and involves discussions and decisions based on the evidence and the person's values and preferences. SDM is becoming a recommended approach in clinical guidelines, extending beyond decision aids. However, the overall effect of SDM in women deciding to participate in breast cancer screening remains uncertain.

OBJECTIVES

To assess the effect of SDM on women's satisfaction, confidence, and knowledge when deciding whether to participate in breast cancer screening.

SEARCH METHODS

We searched the Cochrane Breast Cancer Group's Specialised Register, CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform on 8 August 2023. We also screened abstracts from two relevant conferences from 2020 to 2023.

SELECTION CRITERIA

We included parallel randomised controlled trials (RCTs) and cluster-RCTs assessing interventions targeting various components of SDM. The focus was on supporting women aged 40 to 75 at average or above-average risk of breast cancer in their decision to participate in breast cancer screening.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed studies for inclusion and conducted data extraction, risk of bias assessment, and GRADE assessment of the certainty of the evidence. Review outcomes included satisfaction with the decision-making process, confidence in the decision made, knowledge of all options, adherence to the chosen option, women's involvement in SDM, woman-clinician communication, and mental health.

MAIN RESULTS

We identified 19 studies with 64,215 randomised women, mostly with an average to moderate risk of breast cancer. Two studies covered all aspects of SDM; six examined shortened forms of SDM involving communication on risks and personal values; and 11 focused on enhanced communication of risk without other SDM aspects. SDM involving all components compared to control The two eligible studies did not assess satisfaction with the SDM process or confidence in the decision. Based on a single study, SDM showed uncertain effects on participant knowledge regarding the age to start screening (risk ratio (RR) 1.18, 95% confidence interval (CI) 0.61 to 2.28; 133 women; very low certainty evidence) and frequency of testing (RR 0.84, 95% CI 0.68 to 1.04; 133 women; very low certainty evidence). Other review outcomes were not measured. Abbreviated forms of SDM with clarification of values and preferences compared to control Of the six included studies, none evaluated satisfaction with the SDM process. These interventions may reduce conflict in the decision made, based on two measures, Decisional Conflict Scale scores (mean difference (MD) -1.60, 95% CI -4.21 to 0.87; conflict scale from 0 to 100; 4 studies; 1714 women; very low certainty evidence) and the proportion of women with residual conflict compared to control at one to three months' follow-up (rate of women with a conflicted decision, RR 0.75, 95% CI 0.56 to 0.99; 1 study; 1001 women, very low certainty evidence). Knowledge of all options was assessed through knowledge scores and informed choice. The effect of SDM may enhance knowledge (MDs ranged from 0.47 to 1.44 higher scores on a scale from 0 to 10; 5 studies; 2114 women; low certainty evidence) and may lead to higher rates of informed choice (RR 1.24, 95% CI 0.95 to 1.63; 4 studies; 2449 women; low certainty evidence) compared to control at one to three months' follow-up. These interventions may result in little to no difference in anxiety (MD 0.54, 95% -0.96 to 2.14; scale from 20 to 80; 2 studies; 749 women; low certainty evidence) and the number of women with worries about cancer compared to control at four to six weeks' follow-up (RR 0.88, 95% CI 0.73 to 1.06; 1 study, 639 women; low certainty evidence). Other review outcomes were not measured. Enhanced communication about risks without other SDM aspects compared to control Of 11 studies, three did not report relevant outcomes for this review, and none assessed satisfaction with the SDM process. Confidence in the decision made was measured by decisional conflict and anticipated regret of participating in screening or not. These interventions, without addressing values and preferences, may result in lower confidence in the decision compared to regular communication strategies at two weeks' follow-up (MD 2.89, 95% CI -2.35 to 8.14; Decisional Conflict Scale from 0 to 100; 2 studies; 1191 women; low certainty evidence). They may result in higher anticipated regret if participating in screening (MD 0.28, 95% CI 0.15 to 0.41) and lower anticipated regret if not participating in screening (MD -0.28, 95% CI -0.42 to -0.14). These interventions increase knowledge (MD 1.14, 95% CI 0.61 to 1.62; scale from 0 to 10; 4 studies; 2510 women; high certainty evidence), while it is unclear if there is a higher rate of informed choice compared to regular communication strategies at two to four weeks' follow-up (RR 1.27, 95% CI 0.83 to 1.92; 2 studies; 1805 women; low certainty evidence). These interventions result in little to no difference in anxiety (MD 0.33, 95% CI -1.55 to 0.99; scale from 20 to 80) and depression (MD 0.02, 95% CI -0.41 to 0.45; scale from 0 to 21; 2 studies; 1193 women; high certainty evidence) and lower cancer worry compared to control (MD -0.17, 95% CI -0.26 to -0.08; scale from 1 to 4; 1 study; 838 women; high certainty evidence). Other review outcomes were not measured.

AUTHORS' CONCLUSIONS

Studies using abbreviated forms of SDM and other forms of enhanced communications indicated improvements in knowledge and reduced decisional conflict. However, uncertainty remains about the effect of SDM on supporting women's decisions. Most studies did not evaluate outcomes considered important for this review topic, and those that did measured different concepts. High-quality randomised trials are needed to evaluate SDM in diverse cultural settings with a focus on outcomes such as women's satisfaction with choices aligned to their values.

Personalized survivorship care: Routine breast cancer risk assessment in the gynecologic oncology clinic

INTRODUCTION

Gynecologic and breast cancers share several risk factors. Breast cancer risk assessment tools can identify those at elevated risk and allow for enhanced breast surveillance and chemoprevention, however such tools are underutilized. We aim to evaluate the use of routine breast cancer risk assessment in a gynecologic oncology clinic.

METHODS

A patient-facing web-based tool was used to collect personal and family history and run four validated breast cancer risk assessment models (Tyrer-Cuzick (TC), Gail, BRCAPRO, and Claus) in a gynecologic oncology clinic. We evaluated completion of the tools and identification of patients at elevated risk for breast cancer using the four validated models.

RESULTS

A total of 99 patients were included in this analysis. The BRCAPRO model had the highest completion rate (84.8%), followed by the TC model (74.7%), Gail model (74.7%), and the Claus model (52.1%). The TC model identified 21.6% of patients completing the model as having ≥20% lifetime risk of breast cancer, compared to 6.8% by the Gail model, and 0% for both the BRCAPRO and Claus models. The Gail model identified 52.5% of patients as having ≥1.67% 5-year risk of breast cancer. Among patients identified as high-risk for breast cancer and eligible for screening, 9/9 (100%) were referred to a high-risk breast clinic.

CONCLUSION

Among patients that completed the TC breast cancer risk assessment in a gynecologic oncology clinic, approximately 1 in 5 were identified to be at significantly elevated lifetime risk for breast cancer. The gynecologic oncologist's office might offer a convenient and feasible setting to incorporate this risk assessment into routine patient care, as gynecologic oncologists often have long-term patient relationships and participate in survivorship care.

A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States

Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools.

Highlights

There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.

Talking numbers: how women and providers use risk scores during and after risk counseling - a qualitative investigation from the NRG Oncology/NSABP DMP-1 study

OBJECTIVES

Little research exists on how risk scores are used in counselling. We examined (a) how Breast Cancer Risk Assessment Tool (BCRAT) scores are presented during counselling; (b) how women react and (c) discuss them afterwards.

DESIGN

Consultations were video-recorded and participants were interviewed after the consultation as part of the NRG Oncology/National Surgical Adjuvant Breast and Bowel Project Decision-Making Project 1 (NSABP DMP-1).

SETTING

Two NSABP DMP-1 breast cancer care centres in the USA: one large comprehensive cancer centre serving a high-risk population and an academic safety-net medical centre in an urban setting.

PARTICIPANTS

Thirty women evaluated for breast cancer risk and their counselling providers were included.

METHODS

Participants who were identified as at increased risk of breast cancer were recruited to participate in qualitative study with a video-recorded consultation and subsequent semi-structured interview that included giving feedback and input after viewing their own consultation. Consultation videos were summarised jointly and inductively as a team.tThe interview material was searched deductively for text segments that contained the inductively derived themes related to risk assessment. Subgroup analysis according to demographic variables such as age and Gail score were conducted, investigating reactions to risk scores and contrasting and comparing them with the pertinent video analysis data. From this, four descriptive categories of reactions to risk scores emerged. The descriptive categories were clearly defined after 19 interviews; all 30 interviews fit principally into one of the four descriptive categories.

RESULTS

Risk scores were individualised and given meaning by providers through: (a) presenting thresholds, (b) making comparisons and (c) emphasising or minimising the calculated risk. The risk score information elicited little reaction from participants during consultations, though some added to, agreed with or qualified the provider's information. During interviews, participants reacted to the numbers in four primary ways: (a) engaging easily with numbers; (b) expressing greater anxiety after discussing the risk score; (c) accepting the risk score and (d) not talking about the risk score.

CONCLUSIONS

Our study highlights the necessity that patients' experiences must be understood and put into relation to risk assessment information to become a meaningful treatment decision-making tool, for instance by categorising patients' information engagement into types.

TRIAL REGISTRATION NUMBER

NCT01399359.

Synthetic Mammography: Benefits, Drawbacks, and Pitfalls

Digital breast tomosynthesis (DBT) allows three-dimensional assessment of breast tissue; however, DBT requires a two-dimensional (2D) image for comparison with prior mammograms and accurate interpretation of calcifications. Traditionally, full-field digital mammography (FFDM) has been performed after the DBT image acquisition. Synthetic mammography (SM), the 2D reconstruction of the tomosynthesis slice dataset, has been designed to replace FFDM. Advantages of SM include decreased image acquisition time and decreased radiation exposure, with maintained or improved screening performance metrics. Because SM algorithms give extra weight to lesion-like characteristics (eg, calcifications and architectural distortions), they may enable increased visibility of these characteristics relative to that at FFDM. Although SM algorithms were designed to improve lesion identification, they have led to varied outcomes in studies reported in the literature. Compared with FFDM, SM has been reported to be associated with a higher false-positive rate for calcifications, decreased conspicuity of asymmetries, lower breast density assessments, and imaging artifacts (eg, metallic artifact, bright-band artifact, blurring of the axilla, and truncation artifact). The authors review the literature on SM, including its implementation, benefits, and artifacts. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer

Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.

Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population?

Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580-0.628). Calibration was better for longer-term prediction horizons (E/O_{long-term ranges}: 0.86-1.71; E/O_{short-term ranges}:1.24-3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval.

MRI phenotypes associated with breast cancer predisposing genetic variants, a multisite review

PURPOSE

Examine MRI phenotypes of breast cancers arising in patients with various pathogenic variants, to assess for imaging trends and associations.

METHOD

Multisite retrospective review evaluated 410 patients from 2001 to 2020 with breast cancer and a predisposing pathogenic variant who underwent breast MRI at time of cancer diagnosis. Dominant malignant lesion features were reported, including lesion type (mass versus non-mass enhancement), size, shape, margin, internal enhancement pattern, plus other features. Kruskal-Wallis test, Fisher's exact test, and pairwise comparisons performed comparing imaging manifestations for the most frequent genetic results.

RESULTS

BRCA1 (29.5 %) and BRCA2 (25.9 %) variants were most common, followed by CHEK2 (16.6 %), ATM (8.0 %), and PALB2 (6.3 %), with significant associated differences in race/ethnicity (p = 0.040), age at cancer diagnosis (p = 0.005), tumor shapes (p = 0.001), margins (p < 0.001), grade (p < 0.001), internal enhancement pattern (rim enhancement) (p < 0.001), kinetics (washout) (p < 0.001), and presence of necrosis (p < 0.001). CHEK2 and ATM tumors were often lower grade with spiculated margins (CHEK2: 47.1 %, ATM: 45.5 %), rarely exhibiting washout or tumor necrosis (p < 0.001), and were mostly comprised of luminal molecular subtypes (CHEK2: 88.2 %, ATM: 90.9 %). BRCA1 tumors had the highest proportions with round shape (31.4 %), circumscribed margins (24.0 %), rim enhancement (24.0 %), washout (58.7 %), and necrosis (19.8 %), with 47.9 % comprised of triple negative subtype. Bilateral mastectomy was performed in higher proportions of patients with BRCA1 (84.3 %) and BRCA2 (75.5 %) variants compared to others.

CONCLUSIONS

Genetic and molecular profiles of breast cancers demonstrate reproducible MRI phenotypes.

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.

Integration of survival data from multiple studies

We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. The estimation of the study-specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta-analytic methods.

Implementing Risk-Stratified Breast Screening in England: An Agenda Setting Meeting

It is now possible to accurately assess breast cancer risk at routine NHS Breast Screening Programme (NHSBSP) appointments, provide risk feedback and offer risk management strategies to women at higher risk. These strategies include National Institute for Health and Care Excellence (NICE) approved additional breast screening and risk-reducing medication. However, the NHSBSP invites nearly all women three-yearly, regardless of risk. In March 2022, a one-day agenda setting meeting took place in Manchester to discuss the feasibility and desirability of implementation of risk-stratified screening in the NHSBSP. Fifty-eight individuals participated (38 face-to-face, 20 virtual) with relevant expertise from academic, clinical and/or policy-making perspectives. Key findings were presented from the PROCAS2 NIHR programme grant regarding feasibility of risk-stratified screening in the NHSBSP. Participants discussed key uncertainties in seven groups, followed by a plenary session. Discussions were audio-recorded and thematically analysed to produce descriptive themes. Five themes were developed: (i) risk and health economic modelling; (ii) health inequalities and communication with women; (iii); extending screening intervals for low-risk women; (iv) integration with existing NHSBSP; and (v) potential new service models. Most attendees expected some form of risk-stratified breast screening to be implemented in England and collectively identified key issues to be resolved to facilitate this.

Prediction time of breast cancer tumor recurrence using Machine Learning

An in-depth study using the database from GLOBOCAN, CDC, and WHO health repository highlights the lethality of breast cancer, taking thousands of lives each year. However, a timely prediction of cancer can help patients to consult the doctor on time. In the past, various studies have successfully predicted the nature of the tumor to be benign or malignant and if the breast cancer tumor will reoccur or not but, no time-based models have been studied. With the help of Machine Learning, this study shows various prediction models that can be used to predict tumor reoccurrence time as accurately as 1 year. Among the 198 patients analyzed, 40% of the total patients were predicted to have breast cancer tumors reoccurring within 1^st year of the diagnosis. The proposed machine learning techniques use various classification models such as Spectral clustering, DBSCAN, and k-means along with prediction models like Support Vector Machines (SVM), Decision trees, and Random Forest. The results demonstrate the ability of the model to predict the time taken by the tumor to reoccur or the time taken by the patient for full recovery with the best accuracy of 78.7% using SVM. This population-based study performed on multivariate real attributed characteristics data can therefore provide the patients a reasonable estimate about their recovery time or the time before which they should consult the doctor.

Addressing the routine failure to clinically identify monogenic cases of common disease

Changes in medical practice are needed to improve the diagnosis of monogenic forms of selected common diseases. This article seeks to focus attention on the need for universal genetic testing in common diseases for which the recommended clinical management of patients with specific monogenic forms of disease diverges from standard management and has evidence for improved outcomes.We review evidence from genomic screening of large patient cohorts, which has confirmed that important monogenic case identification failures are commonplace in routine clinical care. These case identification failures constitute diagnostic misattributions, where the care of individuals with monogenic disease defaults to the treatment plan offered to those with polygenic or non-genetic forms of the disease.The number of identifiable and actionable monogenic forms of common diseases is increasing with time. Here, we provide six examples of common diseases for which universal genetic test implementation would drive improved care. We examine the evidence to support genetic testing for common diseases, and discuss barriers to widespread implementation. Finally, we propose recommendations for changes to genetic testing and care delivery aimed at reducing diagnostic misattributions, to serve as a starting point for further evaluation and development of evidence-based guidelines for implementation.

A Review of Breast Cancer Risk Factors in Adolescents and Young Adults

Simple Summary

Cancer diagnosed in patients between the ages of 15 and 39 deserves special consideration. Diagnoses within this cohort of adolescents and young adults include childhood cancers which present at an older age than expected, or an early presentation of cancers that are typically observed in older adults, such as breast cancer. Cancers within this age group are associated with worse disease-free and overall survival rates, and the incidence of these cases are rising. Knowing an individual’s susceptibility to disease can change their clinical management and allow for the risk-testing of relatives. This review discusses the risk factors that contribute to breast cancer in this unique cohort of patients, including inherited genetic risk factors, as well as environmental and lifestyle factors. We also describe risk models that allow clinicians to quantify a patient’s lifetime risk of developing disease.

Abstract

Cancer in adolescents and young adults (AYAs) deserves special consideration for several reasons. AYA cancers encompass paediatric malignancies that present at an older age than expected, or early-onset of cancers that are typically observed in adults. However, disease diagnosed in the AYA population is distinct to those same cancers which are diagnosed in a paediatric or older adult setting. Worse disease-free and overall survival outcomes are observed in the AYA setting, and the incidence of AYA cancers is increasing. Knowledge of an individual’s underlying cancer predisposition can influence their clinical care and may facilitate early tumour surveillance strategies and cascade testing of at-risk relatives. This information can further influence reproductive decision making. In this review we discuss the risk factors contributing to AYA breast cancer, such as heritable predisposition, environmental, and lifestyle factors. We also describe a number of risk models which incorporate genetic factors that aid clinicians in quantifying an individual’s lifetime risk of disease.

A framework for personalized mammogram screening

Breast cancer screening guidelines serve as crucial evidence-based recommendations in deciding when to begin regular screenings. However, due to developments in breast cancer research and differences in research interpretation, screening guidelines can vary between organizations and within organizations over time. This leads to significant lapses in adopting updated guidelines, variable decision making between physicians, and unnecessary screening for low to moderate risk patients (Jacobson and Kadiyala, 2017; Corbelli et al., 2014). For analysis, risk factors were assessed for patient screening behaviors and results. The outcome variable for the first analysis was whether the patient had undergone screening. The risk factors considered were age, marital status, education level, rural versus urban residence, and family history of breast cancer. The outcome variable for the second analysis was whether patients who had undergone breast cancer screening presented abnormal results. The risk factors considered were age, Body Mass Index, family history, smoking and alcohol status, hormonal contraceptive use, Hormone Replacement Therapy use, age of first pregnancy, number of pregnancies (parity), age of first menses, rural versus urban residence, and whether or not patients had at least one child. Logistic regression analysis displayed strong associations for both outcome variables. Risk of screening nonattendance was negatively associated with age as a continuous variable, age as a dichotomous variable, being married, any college education, and family history. Risk of one or more abnormal mammogram findings was positively associated with family history, and hormonal contraceptive use. This procedure will be further developed to incorporate additional risk factors and refine the analysis of currently implemented risk factors.

Surgical Management of Hereditary Breast Cancer

The identification that breast cancer is hereditary was first described in the nineteenth century. With the identification of the BRCA1 and BRCA 2 breast/ovarian cancer susceptibility genes in the mid-1990s and the introduction of genetic testing, significant advancements have been made in tailoring surveillance, guiding decisions on medical or surgical risk reduction and cancer treatments for genetic variant carriers. This review discusses various medical and surgical management options for hereditary breast cancers.

Diagnostic Accuracy of Nipple Aspirate Fluid Cytology in Asymptomatic Patients: A Meta-analysis and Systematic Review of the Literature

PURPOSE

To calculate the diagnostic accuracy of nipple aspirate fluid (NAF) cytology.

BACKGROUND

Evaluation of NAF cytology in asymptomatic patients conceptually offers a non-invasive method for either screening for breast cancer or else predicting or stratifying future cancer risk.

METHODS

Studies were identified by performing electronic searches up to August 2019. A meta-analysis was conducted to attain an overall pooled sensitivity and specificity of NAF for breast cancer detection.

RESULTS

A search through 938 studies yielded a total of 19 studies. Overall, 9308 patients were examined, with cytology results from 10,147 breasts [age (years), mean ± SD = 49.73 ± 4.09 years]. Diagnostic accuracy meta-analysis of NAF revealed a pooled specificity of 0.97 (95% CI 0.97-0.98), and sensitivity of 0.64 (95% CI 0.62-0.66).

CONCLUSIONS

The diagnostic accuracy of nipple smear cytology is limited by poor sensitivity. If nipple fluid assessment is to be used for diagnosis, then emerging technologies for fluid biomarker analysis must supersede the current diagnostic accuracy of NAF cytology.

Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.

Lessons Learned from Setting Up a Prospective, Longitudinal, Multicenter Study with Women at High Risk for Breast Cancer

Women identified with an increased risk of breast cancer due to mutations in cancer susceptibility genes or a familial history of breast cancer undergo tailored screening with the goal of detecting tumors earlier, when potential curative interventions are still possible. Ideally, screening would identify signs of carcinogenesis even before a tumor is detectable by imaging. This could be achieved by timely signaling of altered biomarker levels for precancerous processes in liquid biopsies. Currently, the Nipple Aspirate Fluid (NAF) and the Trial Early Serum Test BREAST cancer (TESTBREAST), both ongoing, prospective, multicenter studies, are investigating biomarkers in liquid biopsies to improve breast cancer screening in high-risk women. The NAF study focuses on changes over time in miRNA expression levels both in blood and NAF samples, whereas the TESTBREAST study analyzes changes in protein levels in blood samples at sequential interval timepoints. These within-subject changes are studied in relation to later occurrence of breast cancer using a nested case-control design. These longitudinal studies face their own challenges in execution, such as hindrances in logistics and in sample processing that were difficult to anticipate. This article offers insight into those challenges and concurrently aims to provide useful strategies for the set-up of similar studies.See related commentary by Sauter, p. 429.

Targeting lung cancer screening to individuals at greatest risk: the role of genetic factors

Lung cancer (LC) is the most common global cancer. An individual’s risk of developing LC is mediated by an array of factors, including family history of the disease. Considerable research into genetic risk factors for LC has taken place in recent years, with both low-penetrance and high-penetrance variants implicated in increasing or decreasing a person’s risk of the disease. LC is the leading cause of cancer death worldwide; poor survival is driven by late onset of non-specific symptoms, resulting in late-stage diagnoses. Evidence for the efficacy of screening in detecting cancer earlier, thereby reducing lung-cancer specific mortality, is now well established. To ensure the cost-effectiveness of a screening programme and to limit the potential harms to participants, a risk threshold for screening eligibility is required. Risk prediction models (RPMs), which provide an individual’s personal risk of LC over a particular period based on a large number of risk factors, may improve the selection of high-risk individuals for LC screening when compared with generalised eligibility criteria that only consider smoking history and age. No currently used RPM integrates genetic risk factors into its calculation of risk. This review provides an overview of the evidence for LC screening, screening related harms and the use of RPMs in screening cohort selection. It gives a synopsis of the known genetic risk factors for lung cancer and discusses the evidence for including them in RPMs, focusing in particular on the use of polygenic risk scores to increase the accuracy of targeted lung cancer screening.

Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study

Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions. Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either "high risk" or "low risk" in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model. Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound nomogram included 3 independent predictors (margins, calcification and strain ratio), and the AUC value in this nomogram was 0.782 and 0.747 in the development and validation groups, respectively. In the development group of 499 patients, approximately 50.90% (254/499) of patients were twice classified "low risk", with a malignancy rate of 1.18%. In the validation group of 181 patients, approximately 47.51% (86/181) of patients had been twice classified as "low risk", with a malignancy rate of 1.16%. Conclusions: A dual-predicted nomogram incorporating clinical factors and imaging characteristics is an applicable model for downgrading the low-risk lesions in BI-RADS category 4a and shows good stability and accuracy, which is useful for decreasing the rate of invasive examinations and surgery.

Breast cancer-induced immune suppression in the sentinel lymph node is effectively countered by CpG-B in conjunction with inhibition of the JAK2/STAT3 pathway

BACKGROUND

We previously showed selectively hampered activation of lymph node-resident (LNR) dendritic cell (DC) subsets in the breast cancer (BrC) sentinel lymph node (SLN) to precede a state of profound T cell anergy. Reactivating these DC subsets by intratumoral delivery of the Toll-like receptor-9 (TLR9) agonist CpG-B could potentially offer a promising immune therapeutic strategy to combat this immune suppression and prevent disease spread. Unfortunately, CpG-B can limit its own immune stimulatory activity through direct TLR9-mediated activation of signal transducer and activator of transcription 3 (STAT3), pinpointed as a key regulator of immune suppression in the tumor microenvironment. Here, we have investigated whether in vitro exposure to CpG-B, with or without simultaneous inhibition of STAT3 signaling, could overcome immune suppression in BrC SLN.

METHODS

Immune modulatory effects of CpG-B (CPG7909) with or without the JAK2/STAT3 inhibitor (STAT3i) AG490 were assessed in ex vivo cultured BrC SLN-derived single-cell suspensions (N=29). Multiparameter flow cytometric analyses were conducted for DC and T cell subset characterization and assessment of (intracellular) cytokine profiles. T cell reactivity against the BrC-associated antigen Mammaglobin-A was determined by means of interferon-γ ELISPOT assay.

RESULTS

Although CpG-B alone induced activation of all DC subsets, combined inhibition of the JAK2/STAT3 pathway resulted in superior DC maturation (ie, increased CD83 expression), with most profound activation and maturation of LNR DC subsets. Furthermore, combined CpG-B and JAK2/STAT3 inhibition promoted Th1 skewing by counterbalancing the CpG-induced Th2/regulatory T cell response and significantly enhanced Mammaglobin-A specific T cell reactivity.

CONCLUSION

Ex vivo immune modulation of the SLN by CpG-B and simultaneous JAK2/STAT3 inhibition can effectively overcome BrC-induced immune suppression by preferential activation of LNR DC, ultimately restoring type 1-mediated antitumor immunity, thereby securing a BrC-specific T cell response. These findings provide a clear rationale for clinical exploration of SLN-immune potentiation through local CpG/STAT3i administration in patients with BrC.

Towards an Integral Therapeutic Protocol for Breast Cancer Based upon the New H+-Centered Anticancer Paradigm of the Late Post-Warburg Era

A brand new approach to the understanding of breast cancer (BC) is urgently needed. In this contribution, the etiology, pathogenesis, and treatment of this disease is approached from the new pH-centric anticancer paradigm. Only this unitarian perspective, based upon the hydrogen ion (H+) dynamics of cancer, allows for the understanding and integration of the many dualisms, confusions, and paradoxes of the disease. The new H+-related, wide-ranging model can embrace, from a unique perspective, the many aspects of the disease and, at the same time, therapeutically interfere with most, if not all, of the hallmarks of cancer known to date. The pH-related armamentarium available for the treatment of BC reviewed here may be beneficial for all types and stages of the disease. In this vein, we have attempted a megasynthesis of traditional and new knowledge in the different areas of breast cancer research and treatment based upon the wide-ranging approach afforded by the hydrogen ion dynamics of cancer. The concerted utilization of the pH-related drugs that are available nowadays for the treatment of breast cancer is advanced.

Robust Estimation of Breast Cancer Incidence Risk in Presence of Incomplete or Inaccurate Information

Purpose:

To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information.

Data and methods:

Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.gov/). We conducted two ablation-type experiments to compare the robustness of different classifiers where we randomly switched known information to missing with a missing probability of p_m in one experiment, and randomly corrupted the existing information with a probability of p_c in another experiment. We considered three prominent machine-learning classifiers such as Logistic regression (LR), Random Forests (RF) and a custom Neural Network (NN) architecture and compared their degradation of discrimination performance as a function of increasing probability of missing or inaccurate data.

Results:

LR, RF and custom NN resulted in an Area Under Curve (AUC) of 0.645, 0.643 and 0.649, respectively, on a test set with 500,000 total observations. When we manipulated the data by varying probabilities p_m and p_c from 0 to 1, NN resulted in better performance in terms of AUC compared to RF and LR as long as less than half the data was missing/inaccurate (that is, for values of p_m < 0.5 and p_c < 0.5). However, for missing (p_m) or corruption (p_c) probabilities above 0.5, LR gave similar performance as the custom NN. RF resulted in overall poorer performance when the data had additional missing or incorrect entries.

Conclusion:

In cases where the input information is missing or inaccurate, our experiments show that the proposed custom NN provides reliable risk estimates in medical datasets like BCSC. These results are particularly important in health care applications where not every attribute of the individual participant might be available.

Women's views about breast cancer prevention at mammography screening units and well women's clinics

Background

Women attending mammography screening units (msus) and well women's clinics (wwcs) represent a motivated cohort likely to engage in interventions aimed at primary breast cancer (bca) prevention.

Methods

We used a feasibility questionnaire distributed to women (40-49 or 50-74 years of age) attending msus and wwcs in Halifax, Nova Scotia, to examine■ women's views about bca primary prevention and sources of health care information,■ prevalence of lifestyle-related bca risk factors, and■ predictors of prior mammography encounters within provincial screening guidelines.Variables examined included personal profiling, comorbidities, prior mammography uptake, lifestyle behaviours, socioeconomic status, health information sources, and willingness to discuss or implement lifestyle modifications, or endocrine therapy, or both. A logistic regression analysis examined associations with prior mammography encounters.

Results

Of the 244 responses obtained during 1.5 months from women aged 40-49 years (n = 75) and 50-74 years (n = 169), 56% and 75% respectively sought or would prefer to receive health information from within, as opposed to outside, health care. Lifestyle-related bca risk factors were prevalent, and most women were willing to discuss or implement lifestyle modifications (93%) or endocrine therapy (67%). Of the two age groups, 49% and 93% respectively had previously undergone mammography within guidelines. Increasing age and marital status (single, separated, or divorced vs. married or partnered) were independent predictors of prior mammography encounters within guidelines for women 40-49 years of age; no independent predictors were observed in the older age group.

Conclusions

Women attending msus and wwcs seem to largely adhere to mammography guidelines and appear motivated to engage in bca primary prevention strategies, including lifestyle modifications and endocrine therapy. Women's views as observed in this study provide a rationale for the potential incorporation of bca risk assessment within the "mammogram point of care" to engage motivated women in bca primary prevention strategies.

Genetic Testing and Screening Recommendations for Patients with Hereditary Breast Cancer

Professionals who specialize in breast imaging may be the first to initiate the conversation about genetic counseling with patients who have a diagnosis of premenopausal breast cancer or a strong family history of breast and ovarian cancer. Commercial genetic testing panels have gained popularity and have become more affordable in recent years. Therefore, it is imperative for radiologists to be able to provide counseling and to identify those patients who should be referred for genetic testing. The authors review the process of genetic counseling and the associated screening recommendations for patients at high and moderate risk. Ultimately, genetic test results enable appropriate patient-specific screening, which allows improvement of overall survival by early detection and timely treatment. The authors discuss pretest counseling, which involves the use of various breast cancer risk assessment tools such as the Gail and Tyrer-Cuzick models. The most common high- and moderate-risk gene mutations associated with breast cancer are also reviewed. In addition to BRCA1 and BRCA2, several high-risk genes, including TP53, PTEN, CDH1, and STK11, are discussed. Moderate-risk genes include ATM, CHEK2, and PALB2. The imaging appearances of breast cancer typically associated with each gene mutation, as well as the other associated cancers, are described. ^©RSNA, 2020 See discussion on this article by Butler (pp 937-940).

A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Panels of single nucleotide polymorphisms (SNPs) stratify risk for breast cancer in women from the general population, but studies are needed assess their use in a fully comprehensive model including classical risk factors, mammographic density and more than 100 SNPs associated with breast cancer. A case-control study was designed (1,668 controls, 405 cases) in women aged 47-73 years attending routine screening in Manchester UK, and enrolled in a wider study to assess methods for risk assessment. Risk from classical questionnaire risk factors was assessed using the Tyrer-Cuzick model; mean percentage visual mammographic density was scored by two independent readers. DNA extracted from saliva was genotyped at selected SNPs using the OncoArray. A predefined polygenic risk score based on 143 SNPs was calculated (SNP143). The odds ratio (OR, and 95% confidence interval, CI) per interquartile range (IQ-OR) of SNP143 was estimated unadjusted and adjusted for Tyrer-Cuzick and breast density. Secondary analysis assessed risk by oestrogen receptor (ER) status. The primary polygenic risk score was well calibrated (O/E OR 1.10, 95% CI 0.86-1.34) and accuracy was retained after adjustment for Tyrer-Cuzick risk and mammographic density (IQ-OR unadjusted 2.12, 95% CI% 1.75-2.42; adjusted 2.06, 95% CI 1.75-2.42). SNP143 was a risk factor for ER+ and ER- breast cancer (adjusted IQ-OR, ER+ 2.11, 95% CI 1.78-2.51; ER- 1.81, 95% CI 1.16-2.84). In conclusion, polygenic risk scores based on a large number of SNPs improve risk stratification in combination with classical risk factors and mammographic density, and SNP143 was similarly predictive for ER-positive and ER-negative disease.

External validation of a mammographic texture marker for breast cancer risk in a case-control study

Purpose: The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting.

Approach: A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography).

Results: MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (IQ-OR=1.16, 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density (IQ-OR=0.92, 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: IQ-OR=1.46, 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: IQ-OR=1.11, 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21).

Conclusions: MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation.

A New and Integral Approach to the Etiopathogenesis and Treatment of Breast Cancer Based upon Its Hydrogen Ion Dynamics

Despite all efforts, the treatment of breast cancer (BC) cannot be considered to be a success story. The advances in surgery, chemotherapy and radiotherapy have not been sufficient at all. Indeed, the accumulated experience clearly indicates that new perspectives and non-main stream approaches are needed to better characterize the etiopathogenesis and treatment of this disease. This contribution deals with how the new pH-centric anticancer paradigm plays a fundamental role in reaching a more integral understanding of the etiology, pathogenesis, and treatment of this multifactorial disease. For the first time, the armamentarium available for the treatment of the different types and phases of BC is approached here from a Unitarian perspective-based upon the hydrogen ion dynamics of cancer. The wide-ranged pH-related molecular, biochemical and metabolic model is able to embrace most of the fields and subfields of breast cancer etiopathogenesis and treatment. This single and integrated approach allows advancing towards a unidirectional, concerted and synergistic program of treatment. Further efforts in this line are likely to first improve the therapeutics of each subtype of this tumor and every individual patient in every phase of the disease.

Predicting breast cancer risk using personal health data and machine learning models

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests (p < 0.05) to compare the testing data set performance of each machine learning model to that of the Breast Cancer Risk Prediction Tool (BCRAT), an implementation of the Gail model. None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.

Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer

Objectives:

We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk.

Materials and Methods:

This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models.

Results:

The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively.

Conclusion:

Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

Potential effect of probiotics in the treatment of breast cancer

Breast cancer is one of the most important causes of cancerrelated morbidity and mortality in the world. Probiotics, as functional food, have the potential to act against breast cancer, as evidenced by cell-based and animal model experiments. Probiotic may be useful in prevention or treatment of breast cancer by modulating the gastrointestinal bacteria and the systemic immune system. However, large-scale clinical trials and intensive research are mandatory to confirm the in vitro and in vivo results and exploring the probiotics-related metabolic, immune, and molecular mechanisms in breast cancer. This current review summarizes the available data related to probiotics and their potential role in the treatment of breast cancer.

Financial Conflicts of Interest Among Authors of Clinical Practice Guidelines for Routine Screening Mammography

PURPOSE

Financial conflicts of interest (FCOIs) may influence or undermine the credibility of clinical practice guidelines or society recommendations. Given the wide regard of such publications, understanding the prevalence and extent of FCOIs among their authors is essential.

METHODS

The most current guidelines containing recommendations for breast cancer screening from the US Preventive Services Task Force, American Cancer Society, American College of Obstetricians and Gynecologists, International Agency for Research on Cancer, ACR, and American College of Physicians were retrieved from their respective organizational websites. Industry payments received by authors were then extracted using CMS Open Payments database (OPD), and the values and types of these payments were evaluated. Finally, financial disclosures were compared with open payments.

RESULTS

Among a total of 43 authors and 7 guideline documents, 14 authors (33%) received at least one industry payment according to OPD payment records, whereas a majority of 29 authors (67%) had none. The median total payment from all sources across all breast imaging guidelines was $0 (interquartile range, $0-$84). Four authors (9%) declared at least one significant FCOI, five (12%) received more than $5,000 from a single company in a single year, and one author had a significant FCOI (2%) identified from OPD records but not disclosed within the guideline document.

CONCLUSIONS

These findings suggest that FCOIs likely have little to no influence on the adoption of consensus recommendations regarding routine screening mammography for all cohorts of women.

Utilization of a breast cancer risk assessment tool by internal medicine residents in a primary care clinic: impact of an educational program

Background

Despite strong evidence of benefit, breast cancer risk assessment and chemoprevention are underutilized by primary care physicians. This study evaluates the impact of an educational program on knowledge and utilization of the NCI Breast Cancer Risk Assessment Tool (BCRAT) by internal medicine residents.

Methods

Internal medicine residents at the primary care clinic at William Beaumont Hospital participated in an educational program on breast cancer risk assessment and chemoprevention. A questionnaire was used to assess knowledge and practice before and after participation. Electronic health records of women between the ages of 35 and 65 who were seen by participating residents for annual health exams between Dec 15, 2015 and Dec 14, 2016 were reviewed. Utilization of BCRAT by the residents was compared pre- and post-educational program.

Results

A total of 43 residents participated in the study. 31 (72.1%) residents reported no prior knowledge about BCRAT. The remaining 12 (27.9%) reported limited knowledge of BCRAT, but the majority of these (n = 10, 83.3%) had not used it in the last six months. For each question on the pre-educational knowledge assessment, fewer than 10% of the residents responded correctly. After implementation of the educational program, there was a significant increase in the proportion of residents who answered correctly (Range: 67 to 100%, p < 0.001).

Electronic health records of 301 clinic patients were reviewed, 118 (39.2%) in the pre-educational program group and 183 (60.8%) in the post-educational program group. There was a higher use of BCRAT in the post-educational program group compared to the pre-intervention group (3.8% vs. 0%, p < 0.05). However, a majority (n = 294, 98.7%) of eligible patients from both groups did not undergo breast cancer risk assessment.

Conclusions

Our study demonstrates that an educational intervention improved residents’ knowledge of BCRAT. Despite this improvement, a significant proportion of patients did not undergo breast cancer risk assessment. Expanding the scope and duration of this intervention and combining it with innovative use of technology to improve utilization should be the subject of future investigation.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5418-6) contains supplementary material, which is available to authorized users.

Role of elastography for downgrading BI-RADS category 4a breast lesions according to risk factors

BACKGROUND

Elastography has been introduced as an additional diagnostic tool to ultrasonography (US) which helps clinicians decide whether or not to perform biopsy on US-detected lesions.

PURPOSE

To evaluate the role of strain elastography in downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions according to personal risk factors for breast cancer in asymptomatic women.

MATERIAL AND METHODS

Strain elastography features of a total of 255 asymptomatic category 4a lesions were classified as soft and not soft (intermediate and hard). Malignancy was confirmed by surgery or biopsy, and benignity was confirmed by surgery or biopsy with no change on US for at least six months. Malignancy rates of lesions with soft and not soft elastography were calculated according to the presence of risk factors.

RESULTS

Of 255 lesions, 25 (9.8%) were malignant and 230 (90.2%) were benign. Of 195 lesions in average-risk women, the malignancy rate of lesions with soft elastography was 1.5% (1/68), which was significantly lower than the 14.2% (18/127) of lesions with not soft elastography ( P = 0.004). Of 60 lesions in increased-risk women, the malignancy rate of lesions with soft elastography was 15.0% (3/20), which was not significantly different from the 7.5% (3/40) of lesions with not soft elastography ( P = 0.390).

CONCLUSION

In average-risk women, category 4a lesions with soft elastography could be followed up with US because of a low malignancy rate of 1.5%.

Genetic Testing to Guide Risk-Stratified Screens for Breast Cancer

Breast cancer screening modalities and guidelines continue to evolve and are increasingly based on risk factors, including genetic risk and a personal or family history of cancer. Here, we review genetic testing of high-penetrance hereditary breast and ovarian cancer genes, including BRCA1 and BRCA2, for the purpose of identifying high-risk individuals who would benefit from earlier screening and more sensitive methods such as magnetic resonance imaging. We also consider risk-based screening in the general population, including whether every woman should be genetically tested for high-risk genes and the potential use of polygenic risk scores. In addition to enabling early detection, the results of genetic screens of breast cancer susceptibility genes can be utilized to guide decision-making about when to elect prophylactic surgeries that reduce cancer risk and the choice of therapeutic options. Variants of uncertain significance, especially missense variants, are being identified during panel testing for hereditary breast and ovarian cancer. A finding of a variant of uncertain significance does not provide a basis for increased cancer surveillance or prophylactic procedures. Given that variant classification is often challenging, we also consider the role of multifactorial statistical analyses by large consortia and functional tests for this purpose.

Clinicians' use of breast cancer risk assessment tools according to their perceived importance of breast cancer risk factors: an international survey

The BOADICEA breast cancer (BC) risk assessment model and its associated Web Application v3 (BWA) tool are being extended to incorporate additional genetic and non-genetic BC risk factors. From an online survey through the BOADICEA website and UK, Dutch, French and Swedish national genetic societies, we explored the relationships between the usage frequencies of the BWA and six other common BC risk assessment tools and respondents' perceived importance of BC risk factors. Respondents (N = 443) varied in age, country and clinical seniority but comprised mainly genetics health professionals (82%) and BWA users (93%). Oncology professionals perceived reproductive, hormonal (exogenous) and lifestyle BC risk factors as more important in BC risk assessment compared to genetics professionals (p values < 0.05 to 0.0001). BWA was used more frequently by respondents who gave high weight to breast tumour pathology and low weight to personal BC history as BC risk factors. BWA use was positively related to the weight given to hormonal BC risk factors. The importance attributed to lifestyle and BMI BC risk factors was not associated with the use of BWA or any of the other tools. Next version of the BWA encompassing additional BC risk factors will facilitate more comprehensive BC risk assessment in genetics and oncology practice.

Celecoxib in breast cancer prevention and therapy

Breast cancer has a high incidence worldwide. The results of substantial studis reveal that inflammation plays an important role in the initiation, development, and aggressiveness of many malignancies. The use of celecoxib, a novel NSAID, is repetitively associated with the reduced risk of the occurrence and progression of a number of types of cancer, particularly breast cancer. This observation is also substantiated by various meta-analyses. Clinical trials have been implemented on integration treatment of celecoxib and shown encouraging results. Celecoxib could be treated as a potential candidate for antitumor agent. There are, nonetheless, some unaddressed questions concerning the precise mechanism underlying the anticancer effect of celecoxib as well as its activity against different types of cancer. In this review, we discuss different mechanisms of anticancer effect of celecoxib as well as preclinical/clinical results signifying this beneficial effect.

Analysis of factors predicting surgical intervention and associated costs in pediatric breast masses: a single center study

PURPOSE

Finding a breast mass in a child provokes apprehension in parents, especially in those with a family history of breast cancer. Clinicians must decide between serial imaging or biopsy of the mass. Herein, we identify management differences in those with and without a positive family history, as well as identify cost differences.

METHODS

An institutional retrospective review was performed of patients (2-18 years of age) with a diagnosis of breast mass. Patient demographics, presentation, medical and surgical history, physical exam, imaging, and pathologic diagnosis were collected. Cost data were acquired from the pediatric health information system (PHIS). Costs were compared between patients managed by biopsy versus serial ultrasounds. Bivariate analyses including Pearson's Chi-square, student's t tests, and logistic regression were performed.

RESULTS

The probability of biopsy increases with age (p = 0.0001) and female gender (p = 0.006). Biopsy rate is higher for larger masses (p < 0.0001), growing size (p < 0.0001), and in patients with a positive family history of breast cancer (p < 0.0001). The average cost of care for management with initial excisional biopsy was $4491 versus those with serial ultrasounds ($986) (p < 0.0001).

CONCLUSIONS

In patients with small lesions, even with a family history of breast cancer, non-operative monitoring is a safe and cost-effective alternative to invasive biopsy.