Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level

Dissemination and implementation science frameworks and strategies to increase breast cancer screening for at-risk women in the United States: A scoping review

Dissemination and implementation science (D&I) can help bridge the gap between research and practice by addressing how to facilitate and maintain pre-existing evidence-based interventions (EBIs) in various contexts within different fields, including that of breast cancer screening and treatment. Yet, despite the availability of D&I frameworks and strategies, there is a lack of studies exploring knowledge transfer dissemination and implementation models, strategies, and frameworks in the setting of breast cancer care. There is a need for studies that create guidelines and roadmaps built on theoretical foundations of D&I research to scale up successful D&I of strategies, frameworks, and protocols proven to cater to the needs of all breast cancer patients when seeking screening and treatment services. The Arksey and O'Malley (2005) York methodology was used as guidance for this review: (1) identifying research questions; (2) searching for relevant studies; (3) selecting studies relevant to the research questions; (4) charting the data; (5) collating, summarizing, and reporting results. Most cited barriers (n = 46) sorted into the category of "Recruitment, Measurement, and Delivery Challenges." The predominant ERIC strategy, featured in a noteworthy 84% of studies, was "Tailor strategies" (#16), which belongs to the "Adapt and tailor to context: culture, language, data analysis, collection" domain. This study can guide researchers, physicians, and community workers in improving accessibility, affordability, and quality of breast cancer screening and adequate follow-up opportunities through D&I strategies and models improving the reach and sustainability of evidence-based programs in at-risk female populations.

Screening Beyond the Evidence: Patterns of Age and Comorbidity for Breast, Cervical, and Colorectal Cancer Screening

BACKGROUND

Little evidence exists to guide continuation of screening beyond the recommended ages of national guidelines for breast, cervical, and colorectal cancers, although increasing age and comorbidity burden is likely to reduce the screening benefit of lower mortality.

OBJECTIVE

Characterize screening after recommended stopping ages, by age and comorbidities in a large, diverse sample.

DESIGN

Serial cross-sectional.

PARTICIPANTS

All individuals in the PROSPR-I consortium cohorts from 75 to 89 years of age for breast cancer screening, 66-89 years of age for cervical cancer screening, and 76-89 years of age for colorectal cancer screening from 2011 to 2013. The lower age thresholds were based on the guidelines for each respective cancer type.

MAIN MEASURES

Proportion of annual screening by cancer type in relation to age and Charlson comorbidity score and median years of screening past guideline age. We estimated the likelihood of screening past the guideline-based age as a function of age and comorbidity using logistic regression.

KEY RESULTS

The study cohorts included individuals screening for breast (n = 33,475); cervical (n = 459,318); and colorectal (n = 556,356) cancers. In the year following aging out, approximately 30% of the population was screened for breast cancer, 2% of the population was screened for cervical, and almost 5% for colorectal cancer. The median number of years screened past the guideline-based recommendation was 5, 3, and 4 for breast, cervical, and colorectal cancer, respectively. Of those screening  > 10 years past the guideline-based age,15%, 46%, and 25% had  ≥ 3 comorbidities respectively. Colorectal cancer screening had the smallest decline in the likelihood of screening beyond the age-based recommendation.

CONCLUSIONS

The odds of screening past guideline-based age decreased with comorbidity burden for breast and cervical cancer screening but not for colorectal. These findings suggest the need to evaluate shared decision tools to help patients understand whether screening is appropriate and to generate more evidence in older populations.

Primary Care Providers' Experiences Recommending and Performing Cervical Cancer Screening for Women with Intellectual Disabilities: A Qualitative Study

Women with intellectual and developmental disabilities (I/DD) are less likely to receive cervical cancer screening (CCS) relative to women without disabilities. Primary care providers (PCPs) play key roles in recommending CCS. The purpose of this study was to identify factors PCPs consider when recommending and performing CCS for women with I/DD. Using a qualitative approach, in-depth semi-structured interviews (N = 13) were conducted with majority family medicine-trained PCPs. Through inductive data analysis, it was found that most PCPs reported recommending CCS; however, follow-through for performing CCS varied. PCPs attempted to align their CCS recommendations with national guidelines and provided counseling and education to families and patients about CCS while taking an individualized risk-benefit approach. Despite most PCPs reporting a lack of knowledge or training related to providing I/DD-specific care, PCPs attempted to draw upon experiences with similar populations to recommend and perform CCS. There is an opportunity to improve knowledge of PCPs related to performing CCS for women with I/DD.

Unlocking Early Cancer Detection: Exploring Biomarkers, Circulating DNA, and Innovative Technological Approaches

Research and development improvements in early cancer diagnosis have had a significant positive impact on health. In the treatment and prevention of cancer, early detection is essential. In this context, biomarkers are essential because they offer important information on the state of cells at any particular time. Cells go through unique changes when they shift from a healthy condition to a malignant state, changes that appropriate biomarkers may pick up. Recent advancements have been made to identify and characterize circulating cancer-specific mutations in cell-free circulating DNA derived from tumors and tumor cells. A patient's delay between the time they first detect symptoms and the time they contact a doctor has been noted for many cancer forms. The tumor's location and features significantly impact the presentation of symptoms judged appropriate for early diagnosis. Lack of knowledge of the severity of the symptoms may be one cause for this delay. Our review is largely focused on the ongoing developments of early diagnosis in the study of biomarkers, circulating DNA for diagnosis, the biology of early challenges, early symptoms, liquid biopsies, detectable by imaging, established tumor markers, plasma DNA technologies, gender differences, and artificial intelligence (AI) in diagnosis. This review aims to determine and evaluate Indicators for detecting early cancer, assessing medical conditions, and evaluating potential risks. For Individuals with a heightened likelihood of developing cancer or who have already been diagnosed, early identification is crucial for enhancing prognosis and raising the likelihood of effective treatment.

Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images

Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. We aimed to establish a method to predict breast cancer risk using radiomics features of mammography images and to enable further examinations and prophylactic treatment to reduce breast cancer mortality. We used mammography images of 4000 women with breast cancer and 1000 healthy women from the 'starting point set' of the OPTIMAM dataset, a public dataset. We trained a Light Gradient Boosting Machine using radiomics features extracted from mammography images of women with breast cancer (only the healthy side) and healthy women. This model was a binary classifier that could discriminate whether a given mammography image was of the contralateral side of women with breast cancer or not, and its performance was evaluated using five-fold cross-validation. The average area under the curve for five folds was 0.60122. Some radiomics features, such as 'wavelet-H_glcm_Correlation' and 'wavelet-H_firstorder_Maximum', showed distribution differences between the malignant and normal groups. Therefore, a single radiomics feature might reflect the breast cancer risk. The odds ratio of breast cancer incidence was 7.38 in women whose estimated malignancy probability was ≥0.95. Radiomics features from mammography images can help predict breast cancer risk.

Personalized Breast Cancer Screening

PURPOSE

Abundant literature and clinical trials indicate that routine cancer screenings decrease patient mortality for several common cancers. However, current national cancer screening guidelines heavily rely on patient age as the predominant factor in deciding cancer screening timing, neglecting other important medical characteristics of individual patients. This approach either delays screening or prescribes excessive screenings. Another disadvantage of the current approach is its inability to combine information across hospital systems because of the lack of a coherent records system.

METHODS

We propose to use claims data and medical insurance transactions that use consistent and pre-established sets of codes for diagnosis, procedures, and medications to develop a clinical support tool to supply supplemental insights and precautions for physicians to make more informed decisions. Furthermore, we propose a novel machine learning framework to recommend personalized, data-driven, and dynamic screening decisions.

RESULTS

We apply this new method to the study of breast cancer mammograms using claims data from 378,840 female patients to demonstrate that across different risk populations, personalized screening reduces the average delay in a cancer diagnosis by 2-3 months with statistical significance, with even stronger benefits for individual patients up to 10 months.

CONCLUSION

Incorporating personal medical characteristics using claims data and novel machine learning methodologies into breast cancer screening improves screening delay by more dynamically considering changing patient risks. Future incorporation of the proposed methodology in health care settings could be provided as a potential support tool for clinicians.

Testing Theory-Based Messages to Encourage Women at Average Risk for Breast Cancer to Consider Biennial Mammography Screening

BACKGROUND

The US Preventive Services Task Force does not recommend routine annual mammography screening for women aged 40-49 at average risk. Little research has been done to develop theory-based communication interventions to facilitate informed decision-making about reducing potentially low-value mammography screening.

PURPOSE

Evaluate the effects of theory-based persuasive messages on women's willingness to consider delaying screening mammography until age 50 or have mammograms biennially.

METHODS

We conducted a randomized controlled communication experiment online with a population-based sample of U.S. women aged 40-49 (N = 383) who screened to be at average risk for breast cancer. Women were randomly assigned to the following messaging summaries: annual mammography risks in 40s (Arm 1, n = 124), mammography risks plus family history-based genetic risk (Arm 2, n = 120), and mammography risks, genetic risk, and behavioral alternatives (Arm 3, n = 139). Willingness to delay screening or reduce screening frequency was assessed post-experiment by a set of 5-point Likert scale items.

RESULTS

Women in Arm 3 reported significantly greater willingness to delay screening mammography until age 50 (mean = 0.23, SD = 1.26) compared with those in Arm 1 (mean = -0.17, SD = 1.20; p = .04). There were no significant arm differences in willingness to reduce screening frequency. Exposure to the communication messages significantly shifted women's breast cancer-related risk perceptions without increasing unwarranted cancer worry across all three arms.

CONCLUSIONS

Providing women with screening information and options may help initiate challenging discussions with providers about potentially low-value screening.

Impact of a risk based breast screening decision aid on understanding, acceptance and decision making

Internationally, population breast cancer screening is moving towards a risk-stratified approach and requires engagement and acceptance from current and future screening clients. A decision aid ( www.defineau.org ) was developed based on women's views, values, and knowledge regarding risk-stratified breast cancer screening. This study aims to evaluate the impact of the decision aid on women's knowledge, risk perception, acceptance of risk assessment and change of screening frequency, and decision-making. Here we report the results of a pre and post-survey in which women who are clients of BreastScreen Victoria were invited to complete an online questionnaire before and after viewing the decision aid. 3200 potential participants were invited, 242 responded with 127 participants completing both surveys. After reviewing the decision aid there was a significant change in knowledge, acceptance of risk-stratified breast cancer screening and of decreased frequency screening for lower risk. High levels of acceptance of risk stratification, genetic testing and broad support for tailored screening persisted pre and post review. The DEFINE decision aid has a positive impact on acceptance of lower frequency screening, a major barrier to the success of a risk-stratified program and may contribute to facilitating change to the population breast screening program in Australia.

A model for predicting both breast cancer risk and non-breast cancer death among women > 55 years old

BACKGROUND

Guidelines recommend shared decision making (SDM) for mammography screening for women ≥ 75 and not screening women with < 10-year life expectancy. High-quality SDM requires consideration of women's breast cancer (BC) risk, life expectancy, and values but is hard to implement because no models simultaneously estimate older women's individualized BC risk and life expectancy.

METHODS

Using competing risk regression and data from 83,330 women > 55 years who completed the 2004 Nurses' Health Study (NHS) questionnaire, we developed (in 2/3 of the cohort, n = 55,533) a model to predict 10-year non-breast cancer (BC) death. We considered 60 mortality risk factors and used best-subsets regression, the Akaike information criterion, and c-index, to identify the best-fitting model. We examined model performance in the remaining 1/3 of the NHS cohort (n = 27,777) and among 17,380 Black Women's Health Study (BWHS) participants, ≥ 55 years, who completed the 2009 questionnaire. We then included the identified mortality predictors in a previously developed competing risk BC prediction model and examined model performance for predicting BC risk.

RESULTS

Mean age of NHS development cohort participants was 70.1 years (± 7.0); over 10 years, 3.1% developed BC, 0.3% died of BC, and 20.1% died of other causes; NHS validation cohort participants were similar. BWHS participants were younger (mean age 63.7 years [± 6.7]); over 10-years 3.1% developed BC, 0.4% died of BC, and 11.1% died of other causes. The final non-BC death prediction model included 21 variables (age; body mass index [BMI]; physical function [3 measures]; comorbidities [12]; alcohol; smoking; age at menopause; and mammography use). The final BC prediction model included age, BMI, alcohol and hormone use, family history, age at menopause, age at first birth/parity, and breast biopsy history. When risk factor regression coefficients were applied in the validation cohorts, the c-index for predicting 10-year non-BC death was 0.790 (0.784-0.796) in NHS and 0.768 (0.757-0.780) in BWHS; for predicting 5-year BC risk, the c-index was 0.612 (0.538-0.641) in NHS and 0.573 (0.536-0.611) in BWHS.

CONCLUSIONS

We developed and validated a novel competing-risk model that predicts 10-year non-BC death and 5-year BC risk. Model risk estimates may help inform SDM around mammography screening.

Breast cancer incidence among women with a family history of breast cancer by relative's age at diagnosis

BACKGROUND

Women with a first-degree family history of breast cancer are often advised to begin screening when they are 10 years younger than the age at which their relative was diagnosed. Evidence is lacking to determine how much earlier they should begin.

METHODS

Using Breast Cancer Surveillance Consortium data on screening mammograms from 1996 to 2016, the authors constructed a cohort of 306,147 women 30-59 years of age with information on first-degree family history of breast cancer and relative's age at diagnosis. The authors compared cumulative 5-year breast cancer incidence among women with and without a first-degree family history of breast by relative's age at diagnosis and by screening age.

RESULTS

Among 306,147 women included in the study, approximately 11% reported a first-degree family history of breast cancer with 3885 breast cancer cases identified. Women reporting a relative diagnosed between 40 and 49 years and undergoing screening between ages 30 and 39 or 40 and 49 had similar 5-year cumulative incidences of breast cancer (respectively, 18.6/1000; 95% confidence interval [CI], 12.1, 25.7; 18.4/1000; 95% CI, 13.7, 23.5) as women without a family history undergoing screening between 50-59 years of age (18.0/1000; 95% CI, 17.0, 19.1). For relative's diagnosis age from 35 to 45 years of age, initiating screening 5-8 years before diagnosis age resulted in a 5-year cumulative incidence of breast cancer of 15.2/1000, that of an average 50-year-old woman.

CONCLUSION

Women with a relative diagnosed at or before age 45 may wish to consider, in consultation with their provider, initiating screening 5-8 years earlier than their relative's diagnosis age.

Promote Community Engagement in Participatory Research for Improving Breast Cancer Prevention: The P.I.N.K. Study Framework

Breast cancer (BC) has overtaken lung cancer as the most common cancer in the world and the projected incidence rates show a further increase. Early detection through population screening remains the cornerstone of BC control, but a progressive change from early diagnosis only-based to a personalized preventive and risk-reducing approach is widely debated. Risk-stratification models, which also include personal lifestyle risk factors, are under evaluation, although the documentation burden to gather population-based data is relevant and traditional data collection methods show some limitations. This paper provides the preliminary results from the analysis of clinical data provided by radiologists and lifestyle data collected using self-administered questionnaires from 5601 post-menopausal women. The weight of the combinations of women's personal features and lifestyle habits on the BC risk were estimated by combining a model-driven and a data-driven approach to analysis. The weight of each factor on cancer occurrence was assessed using a logistic model. Additionally, communities of women sharing common features were identified and combined in risk profiles using social network analysis techniques. Our results suggest that preventive programs focused on increasing physical activity should be widely promoted, in particular among the oldest women. Additionally, current findings suggest that pregnancy, breast-feeding, salt limitation, and oral contraception use could have different effects on cancer risk, based on the overall woman's risk profile. To overcome the limitations of our data, this work also introduces a mobile health tool, the Dress-PINK, designed to collect real patients' data in an innovative way for improving women's response rate, data accuracy, and completeness as well as the timeliness of data availability. Finally, the tool provides tailored prevention messages to promote critical consciousness, critical thinking, and increased health literacy among the general population.

Patient Navigation Can Improve Breast Cancer Outcomes among African American Women in Chicago: Insights from a Modeling Study

African American (AA) women experience much greater mortality due to breast cancer (BC) than non-Latino Whites (NLW). Clinical patient navigation is an evidence-based strategy used by healthcare institutions to improve AA women's breast cancer outcomes. While empirical research has demonstrated the potential effect of navigation interventions for individuals, the population-level impact of navigation on screening, diagnostic completion, and stage at diagnosis has not been assessed. An agent-based model (ABM), representing 50-74-year-old AA women and parameterized with locally sourced data from Chicago, is developed to simulate screening mammography, diagnostic resolution, and stage at diagnosis of cancer. The ABM simulated three counterfactual scenarios: (1) a control setting without any navigation that represents the "standard of care"; (2) a clinical navigation scenario, where agents receive navigation from hospital-affiliated staff; and (3) a setting with network navigation, where agents receive clinical navigation and/or social network navigation (i.e., receiving support from clinically navigated agents for breast cancer care). In the control setting, the mean population-level screening mammography rate was 46.3% (95% CI: 46.2%, 46.4%), the diagnostic completion rate was 80.2% (95% CI: 79.9%, 80.5%), and the mean early cancer diagnosis rate was 65.9% (95% CI: 65.1%, 66.7%). Simulation results suggest that network navigation may lead up to a 13% increase in screening completion rate, 7.8% increase in diagnostic resolution rate, and a 4.9% increase in early-stage diagnoses at the population-level. Results suggest that systems science methods can be useful in the adoption of clinical and network navigation policies to reduce breast cancer disparities.

Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach

BACKGROUND

Family health history has been recognized as an essential factor for cancer risk assessment and is an integral part of many cancer screening guidelines, including genetic testing for personalized clinical management strategies. However, manually identifying eligible candidates for genetic testing is labor intensive.

OBJECTIVE

The aim of this study was to develop a natural language processing (NLP) pipeline and assess its contribution to identifying patients who meet genetic testing criteria for hereditary cancers based on family health history data in the electronic health record (EHR). We compared an algorithm that uses structured data alone with structured data augmented using NLP.

METHODS

Algorithms were developed based on the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing for hereditary breast, ovarian, pancreatic, and colorectal cancers. The NLP-augmented algorithm uses both structured family health history data and the associated unstructured free-text comments. The algorithms were compared with a reference standard of 100 patients with a family health history in the EHR.

RESULTS

Regarding identifying the reference standard patients meeting the NCCN criteria, the NLP-augmented algorithm compared with the structured data algorithm yielded a significantly higher recall of 0.95 (95% CI 0.9-0.99) versus 0.29 (95% CI 0.19-0.40) and a precision of 0.99 (95% CI 0.96-1.00) versus 0.81 (95% CI 0.65-0.95). On the whole data set, the NLP-augmented algorithm extracted 33.6% more entities, resulting in 53.8% more patients meeting the NCCN criteria.

CONCLUSIONS

Compared with the structured data algorithm, the NLP-augmented algorithm based on both structured and unstructured family health history data in the EHR increased the number of patients identified as meeting the NCCN criteria for genetic testing for hereditary breast or ovarian and colorectal cancers.

Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda

BACKGROUND

Cancer screening is a complex process involving multiple steps and levels of influence (e.g., patient, provider, facility, health care system, community, or neighborhood). We describe the design, methods, and research agenda of the Population-based Research to Optimize the Screening Process (PROSPR II) consortium. PROSPR II Research Centers (PRC), and the Coordinating Center aim to identify opportunities to improve screening processes and reduce disparities through investigation of factors affecting cervical, colorectal, and lung cancer screening in U.S. community health care settings.

METHODS

We collected multilevel, longitudinal cervical, colorectal, and lung cancer screening process data from clinical and administrative sources on &gt;9 million racially and ethnically diverse individuals across 10 heterogeneous health care systems with cohorts beginning January 1, 2010. To facilitate comparisons across organ types and highlight data breadth, we calculated frequencies of multilevel characteristics and volumes of screening and diagnostic tests/procedures and abnormalities.

RESULTS

Variations in patient, provider, and facility characteristics reflected the PROSPR II health care systems and differing target populations. PRCs identified incident diagnoses of invasive cancers, in situ cancers, and precancers (invasive: 372 cervical, 24,131 colorectal, 11,205 lung; in situ: 911 colorectal, 32 lung; precancers: 13,838 cervical, 554,499 colorectal).

CONCLUSIONS

PROSPR II's research agenda aims to advance: (i) conceptualization and measurement of the cancer screening process, its multilevel factors, and quality; (ii) knowledge of cancer disparities; and (iii) evaluation of the COVID-19 pandemic's initial impacts on cancer screening. We invite researchers to collaborate with PROSPR II investigators.

IMPACT

PROSPR II is a valuable data resource for cancer screening researchers.

Factors Associated with Mammography Screening Choices by Women Aged 40-49 at Average Risk

Background: The U.S. Preventive Services Task Force (USPSTF) does not recommend routine mammogram screening for women aged 40-49 years at average risk for breast cancer. We aimed to assess the extent to which women were following guideline recommendations and to examine whether guideline awareness and other individual-level factors were associated with adherence. Materials and Methods: We surveyed a nationally representative panel of 383 U.S. women aged 40-49 years at low risk for hereditary breast cancer in October 2019. Results: Only 29% of women reported not having initiated screening mammography. Most women (80%) were unaware of the USPSTF screening guideline related to age of initiation and frequency of mammography. Being aware of the recommendation to initiate screening at age 50 increased the odds of not initiating screening (odds ratio [OR] = 6.70, p < 0.001), whereas being older than 45 years (OR = 0.22, p < 0.001) and having a primary care doctor decreased the odds of not initiating screening (OR = 0.25, p < 0.001). Conclusions: Mammogram screening in excess of USPSTF recommendations is prevalent among U.S. women aged 40-49 years. Efforts are needed to increase women's awareness of the rationale for guidelines and the opportunities to discuss with providers whether delaying mammograms is appropriate.

Identifying and Managing Patients with Elevated Breast Cancer Risk Presenting for Screening Mammography

BACKGROUND

Identifying the prevalence and management of patients at high-risk for breast cancer can improve resource utilization and provide individualized screening strategies.

OBJECTIVE

The purpose of this study was to identify the prevalence of high-risk patients in our institution who presented for screening mammography and to understand how they utilized downstream resources offered to them.

MATERIALS AND METHODS

This single institution retrospective study utilized the Tyrer-Cuzick risk assessment model to provide lifetime risk of breast cancer of patients presenting for screening mammography over a one-year period. Their subsequent management and resource utilization were collated.

RESULTS

High-risk patients comprised 7.7% (701/9061) of our screening population. Of those high-risk women offered a Breast Center (BC) consultation, 75.2% (276/367) participated in the consultation, with 51.1% (141/276) of those patients completing MRI for supplemental screening. Risk reducing medication was adopted by 7.6% (6/79) of those offered. Of patients offered a genetics consultation, 66.3% (53/80) participated in the consultation, and 50.0% (40/80) completed genetic testing.

CONCLUSIONS

Identifying and understanding high-risk patient cohorts, whether locally or in a population-based context, is important for individualized patient care and practice efficiency.

The current status of risk-stratified breast screening

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Views of health professionals on risk-based breast cancer screening and its implementation in the Spanish National Health System: A qualitative discussion group study

BACKGROUND

With the aim of increasing benefits and decreasing harms, risk-based breast cancer screening has been proposed as an alternative to age-based screening. This study explores barriers and facilitators to implementing a risk-based breast cancer screening program from the perspective of health professionals, in the context of a National Health Service.

METHODS

Socio-constructivist qualitative research carried out in Catalonia (Spain), in the year 2019. Four discussion groups were conducted, with a total of 29 health professionals from primary care, breast cancer screening programs, hospital breast units, epidemiology units, and clinical specialties. A descriptive-interpretive thematic analysis was performed.

RESULTS

Identified barriers included resistance to reducing the number of screening exams for low-risk women; resistance to change for health professionals; difficulties in risk communication; lack of conclusive evidence of the benefits of risk-based screening; limited economic resources; and organizational transformation. Facilitators include benefits of risk-based strategies for high and low-risk women; women's active role in their health care; proximity of women and primary care professionals; experience of health professionals in other screening programs; and greater efficiency of a risk-based screening program. Organizational and administrative changes in the health system, commitment by policy makers, training of health professionals, and educational interventions addressed to the general population will be required.

CONCLUSIONS

Despite the expressed difficulties, participants supported the implementation of risk-based screening. They highlighted its benefits, especially for women at high risk of breast cancer and those under 50 years of age, and assumed a greater efficiency of the risk-based program compared to the aged-based one. Future studies should assess the efficiency and feasibility of risk-based breast cancer screening for its transfer to clinical practice.

Cancer screening simulation models: a state of the art review

Background

Nowadays, various simulation approaches for evaluation and decision making in cancer screening can be found in the literature. This paper presents an overview of approaches used to assess screening programs for breast, lung, colorectal, prostate, and cervical cancers. Our main objectives are to describe methodological approaches and trends for different cancer sites and study populations, and to evaluate quality of cancer screening simulation studies.

Methods

A systematic literature search was performed in Medline, Web of Science, and Scopus databases. The search time frame was limited to 1999–2018 and 7101 studies were found. Of them, 621 studies met inclusion criteria, and 587 full-texts were retrieved, with 300 of the studies chosen for analysis. Finally, 263 full texts were used in the analysis (37 were excluded during the analysis). A descriptive and trend analysis of models was performed using a checklist created for the study.

Results

Currently, the most common methodological approaches in modeling cancer screening were individual-level Markov models (34% of the publications) and cohort-level Markov models (41%). The most commonly evaluated cancer types were breast (25%) and colorectal (24%) cancer. Studies on cervical cancer evaluated screening and vaccination (18%) or screening only (13%). Most studies have been conducted for North American (42%) and European (39%) populations. The number of studies with high quality scores increased over time.

Conclusions

Our findings suggest that future directions for cancer screening modelling include individual-level Markov models complemented by screening trial data, and further effort in model validation and data openness.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01713-5.

Algorithms and methods for computerized analysis of mammography images in breast cancer risk assessment

BACKGROUND AND OBJECTIVES

The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today.

METHODS

We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face.

RESULTS

Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered.

CONCLUSIONS

Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.

Patient, provider, and clinic factors associated with the use of cervical cancer screening

Cervical cancer screening delivery remains suboptimal. Understanding the multiple influences on use of screening is important to designing interventions. We describe the influence of patient, primary care provider (PCP), and clinic characteristics on whether a woman is up-to-date with cervical screening as of December 2016. PCPs (n = 194) and their female screen-eligible patients age 21-65 years (n = 32,115) were included in this cross-sectional analysis of patients from two primary care networks linked to a contemporaneous PCP survey. Principal independent variables for patients included: age, race, insurance, continuity of care; for PCP included: overall satisfaction with the practice of medicine, gender, hours worked per week, financial support for achieving clinical targets; and for clinic included: routine receipt of data on preventive care performance and language translation resources. Overall, 66.6% of women were up-to-date. Women were less likely to be up-to-date with cervical cancer screening if they were younger and were more likely to be screened if they were Black, Hispanic or Asian vs. White. Women with greater continuity of primary care or with a female PCP were more likely to be up-to-date (1.52; 1.33-1.75); those who received care in a clinic that was less prepared to manage language translation were less likely to be up-to-date (0.78; 0.65-0.95). Patient, provider, and clinic factors all influence use of cervical cancer screening. Systems interventions like improving continuity of care, promoting translation services, or enhanced efforts to track screening among patients of male PCPs may improve delivery.

Using Risk Stratification to Optimize Mammography Screening in Chinese Women

Background

The cost-effectiveness of mammography screening among Chinese women remains contentious. Here, we characterized breast cancer (BC) epidemiology in Hong Kong and evaluated the cost-effectiveness of personalized risk-based screening.

Methods

We used the Hong Kong Breast Cancer Study (a case-control study with 3501 cases and 3610 controls) and Hong Kong Cancer Registry to develop a risk stratification model based on well-documented risk factors. We used the Shanghai Breast Cancer Study to validate the model. We considered risk-based programs with different screening age ranges and risk thresholds under which women were eligible to join if their remaining BC risk at the starting age exceeded the threshold.

Results

The lifetime risk (15-99 years) of BC ranged from 1.8% to 26.6% with a mean of 6.8%. Biennial screening was most cost-effective when the starting age was 44 years, and screening from age 44 to 69 years would reduce breast cancer mortality by 25.4% (95% credible interval [CrI] = 20.5%-29.4%) for all risk strata. If the risk threshold for this screening program was 8.4% (the average remaining BC risk among US women at their recommended starting age of 50 years), the coverage was 25.8%, and the incremental cost-effectiveness ratio (ICER) was US$18 151 (95% CrI = $10 408-$27 663) per quality-of-life-year (QALY) compared with no screening. The ICER of universal screening was $34 953 (95% CrI = $22 820-$50 268) and $48 303 (95% CrI = $32 210-$68 000) per QALY compared with no screening and risk-based screening with 8.4% threshold, respectively.

Conclusion

Organized BC screening in Chinese women should commence as risk-based programs. Outcome data (e.g., QALY loss because of false-positive mammograms) should be systemically collected for optimizing the risk threshold.

Toward a Population-Based Breast Cancer Risk Stratification Approach? The Needs and Concerns of Healthcare Providers

Given the expanding knowledge base in cancer genomics, risk-based screening is among the promising avenues to improve breast cancer (BC) prevention and early detection at the population level. Semi-structured interviews were conducted to explore the perceptions of healthcare professionals (HPs) regarding the implementation of such an approach and identify tools that can support HPs. After undertaking an in-depth thematic content analysis of the responses, 11 themes were identified. These were embedded into a logical model to distinguish the potential eligible participants (who?), the main clinical activities (how?) and associated tools (what?), the key factors of acceptability (which?), and the expected effects of the strategy (why?). Overall, it was found that the respondents positively welcomed the implementation of this strategy and agreed on some of the benefits that could accrue to women from tailored risk-based screening. Some important elements, however, deserve clarification. The results also highlight three main conditions that should be met to foster the acceptability of BC risk stratification: respecting the principle of equity, paying special attention to knowledge management, and rethinking human resources to capitalize on the strengths of the current workforce. Because the functioning of BC risk-based screening is not yet well defined, important planning work is required before advancing this organizational innovation, and outstanding issues must be resolved to get HPs on board.

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.

Developing and validating an individualized breast cancer risk prediction model for women attending breast cancer screening

BACKGROUND

Several studies have proposed personalized strategies based on women's individual breast cancer risk to improve the effectiveness of breast cancer screening. We designed and internally validated an individualized risk prediction model for women eligible for mammography screening.

METHODS

Retrospective cohort study of 121,969 women aged 50 to 69 years, screened at the long-standing population-based screening program in Spain between 1995 and 2015 and followed up until 2017. We used partly conditional Cox proportional hazards regression to estimate the adjusted hazard ratios (aHR) and individual risks for age, family history of breast cancer, previous benign breast disease, and previous mammographic features. We internally validated our model with the expected-to-observed ratio and the area under the receiver operating characteristic curve.

RESULTS

During a mean follow-up of 7.5 years, 2,058 women were diagnosed with breast cancer. All three risk factors were strongly associated with breast cancer risk, with the highest risk being found among women with family history of breast cancer (aHR: 1.67), a proliferative benign breast disease (aHR: 3.02) and previous calcifications (aHR: 2.52). The model was well calibrated overall (expected-to-observed ratio ranging from 0.99 at 2 years to 1.02 at 20 years) but slightly overestimated the risk in women with proliferative benign breast disease. The area under the receiver operating characteristic curve ranged from 58.7% to 64.7%, depending of the time horizon selected.

CONCLUSIONS

We developed a risk prediction model to estimate the short- and long-term risk of breast cancer in women eligible for mammography screening using information routinely reported at screening participation. The model could help to guiding individualized screening strategies aimed at improving the risk-benefit balance of mammography screening programs.

The P.I.N.K. Study Approach for Supporting Personalized Risk Assessment and Early Diagnosis of Breast Cancer

Breast cancer is a clear example of excellent survival when it is detected and properly treated in the early stage. Currently, screening of this cancer relies on mammography, which may be integrated by new imaging techniques for more exhaustive evaluation. The Personalized, Integrated, Network, Knowledge (P.I.N.K.) study is a longitudinal multicentric study involving several diagnostic centres across Italy, co-ordinated by the Italian National Research Council and co-funded by the Umberto Veronesi Foundation. Aim of the study is to evaluate the increased diagnostic accuracy in detecting cancers obtained with different combinations of imaging technologies, and find the most effective diagnostic pathway matching the characteristics of an individual patient. The study foresees the enrolment of 50,000 women over the age of 40 years presenting for breast examination and providing informed consent to data handling. So far, the 15 participating centres across Italy have recruited a total of 22,848 patients. Based on the analyses of the first 175 histopathological-proven breast cancers, mammographic sensitivity was estimated to be 61.7% (n = 108 cancers), whereas diagnostic accuracy increased by 35.5% (n = 44 cancers) when mammography was integrated with other imaging modalities (ultrasound and/or digital breast tomosynthesis). Increase was mainly determined by ultrasound alone. Given the ongoing data collection and recruitment, the number of cancers detected is too low to allow any further in-depth analysis to explore links to patient characteristics. Past studies show that the uniform approach of population screening guidelines should be revised in favour of more personalised regimens, where known standards are integrated by imaging techniques most suitable for the individual's characteristics. With the ultimate goal of identifying early breast cancer detection strategies, our preliminary results suggest that integrated diagnostic approach could lead to a paradigm shift from an age-based regimen toward more specific and effective risk-based personalised screening regimens, in order to reduce mortality from breast cancer.

Introducing a low-risk breast screening pathway into the NHS Breast Screening Programme: Views from healthcare professionals who are delivering risk-stratified screening

OBJECTIVES

Proposals to stratify breast screening by breast cancer risk aim to produce a better balance of benefits to harms. Notably, risk estimation calculated from common risk factors and a polygenic risk score would enable high-risk women to benefit from more frequent screening or preventive medication. This service would also identify low-risk women who experience fewer benefits from attending, as lower grade and in situ cancers may be treated unnecessarily. It may therefore be appropriate for low-risk women to attend screening less. This study aimed to elicit views regarding implementing less frequent screening for low-risk women from healthcare professionals who implement risk-stratified screening.

METHODS

Healthcare professionals involved in the delivery of risk-stratified breast screening were invited to participate in a focus group within the screening setting in which they work or have a telephone interview. Primary care staff were also invited to provide their perspective. Three focus groups and two telephone interviews were conducted with 28 healthcare professionals. To identify patterns across the sample, data were analysed as a single dataset using reflexive thematic analysis.

RESULTS

Analysis yielded three themes: Reservations concerning the introduction of less frequent screening, highlighting healthcare professionals' unease and concerns towards implementing less frequent screening; Considerations for the management of public knowledge, providing views on media impact on public opinion and the potential for a low-risk pathway to cause confusion and raise suspicion regarding implementation motives; and Deliberating service implications and reconfiguration management, where the practicalities of implementation are discussed.

CONCLUSIONS

Healthcare professionals broadly supported less frequent screening but had concerns about implementation. It will be essential to address concerns regarding risk estimate accuracy, healthcare professional confidence, service infrastructure and public communication prior to introducing less frequent screening for low-risk women.

Mammography features for early markers of aggressive breast cancer subtypes and tumor characteristics: A population-based cohort study

Current breast cancer risk models identify mostly less aggressive tumors, although only women developing fatal breast cancer will greatly benefit from early identification. Here, we evaluated the use of mammography features (microcalcification clusters, computer-generated Breast Imaging Reporting and Data System [cBIRADS] density and lack of breast density reduction) as early markers of aggressive subtypes and tumor characteristics. Mammograms were retrieved from a population-based cohort of women that were diagnosed with breast cancer from 2001 to 2008 in Stockholm-Gotland County, Sweden. Tumor and patient characteristics were obtained from Stockholm Breast Cancer Quality Register and the Swedish Cancer Registry. Multinomial logistic regression was used to individually model each mammographic feature as a function of molecular subtypes, tumor characteristics and detection mode. A total of 4546 women with invasive breast cancer were included in the study. Women with microcalcification clusters in the affected breast were more likely to have human epidermal growth factor receptor 2 subtype (odds ratio [OR] 1.78; 95% confidence interval [CI] 1.24-2.54) and potentially less likely to have basal subtype (OR 0.54; 0.30-0.96) compared to Luminal A subtype. High mammographic cBIRADS showed association with larger tumor size and interval vs screen-detected cancers. Lack of density reduction was associated with interval vs screen-detected cancers (OR 1.43; 1.11-1.83) and potentially of Luminal B subtype vs Luminal A subtype (OR 1.76; 1.04-2.99). In conclusion, microcalcification clusters, cBIRADS density and lack of breast density reduction could serve as early markers of particular subtypes and tumor characteristics of breast cancer. This information has the potential to be integrated into risk models to identify women at risk for developing aggressive breast cancer in need of supplemental screening.

Differences in breast cancer risk after benign breast disease by type of screening diagnosis

Introduction

We aimed to assess differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens.

Materials and methods

We conducted a retrospective cohort study with data from 629,087 women participating in a long-standing population-based breast cancer screening program in Spain. Each benign breast disease was classified as non-proliferative, proliferative without atypia, or proliferative with atypia, and whether it was diagnosed in a prevalent or incident screen. We used partly conditional Cox hazard regression to estimate the adjusted hazard ratios of the risk of breast cancer.

Results

Compared with women without benign breast disease, the risk of breast cancer was significantly higher (p-value = 0.005) in women with benign breast disease diagnosed in an incident screen (aHR, 2.67; 95%CI: 2.24–3.19) than in those with benign breast disease diagnosed in a prevalent screen (aHR, 1.87; 95%CI: 1.57–2.24). The highest risk was found in women with a proliferative benign breast disease with atypia (aHR, 4.35; 95%CI: 2.09–9.08, and 3.35; 95%CI: 1.51–7.40 for those diagnosed at incident and prevalent screens, respectively), while the lowest was found in women with non-proliferative benign breast disease (aHR, 2.39; 95%CI: 1.95–2.93, and 1.63; 95%CI: 1.32–2.02 for those diagnosed at incident and prevalent screens, respectively).

Conclusion

Our study showed that the risk of breast cancer conferred by a benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyse the impact of the screening type on benign breast disease prognosis.

•

Breast cancer risk after a benign breast disease varied with the screening type.

•

Incident benign breast disease had a higher breast cancer risk than prevalent.

•

The risk remained increased regardless of benign breast disease subtype.

Evaluating Screening Participation, Follow-up, and Outcomes for Breast, Cervical, and Colorectal Cancer in the PROSPR Consortium

BACKGROUND

Cancer screening is a complex process encompassing risk assessment, the initial screening examination, diagnostic evaluation, and treatment of cancer precursors or early cancers. Metrics that enable comparisons across different screening targets are needed. We present population-based screening metrics for breast, cervical, and colorectal cancers for nine sites participating in the Population-based Research Optimizing Screening through Personalized Regimens consortium.

METHODS

We describe how selected metrics map to a trans-organ conceptual model of the screening process. For each cancer type, we calculated calendar year 2013 metrics for the screen-eligible target population (breast: ages 40-74 years; cervical: ages 21-64 years; colorectal: ages 50-75 years). Metrics for screening participation, timely diagnostic evaluation, and diagnosed cancers in the screened and total populations are presented for the total eligible population and stratified by age group and cancer type.

RESULTS

The overall screening-eligible populations in 2013 were 305 568 participants for breast, 3 160 128 for cervical, and 2 363 922 for colorectal cancer screening. Being up-to-date for testing was common for all three cancer types: breast (63.5%), cervical (84.6%), and colorectal (77.5%). The percentage of abnormal screens ranged from 10.7% for breast, 4.4% for cervical, and 4.5% for colorectal cancer screening. Abnormal breast screens were followed up diagnostically in almost all (96.8%) cases, and cervical and colorectal were similar (76.2% and 76.3%, respectively). Cancer rates per 1000 screens were 5.66, 0.17, and 1.46 for breast, cervical, and colorectal cancer, respectively.

CONCLUSIONS

Comprehensive assessment of metrics by the Population-based Research Optimizing Screening through Personalized Regimens consortium enabled systematic identification of screening process steps in need of improvement. We encourage widespread use of common metrics to allow interventions to be tested across cancer types and health-care settings.

Quantitative analysis of breast tumours aided by three-dimensional photoacoustic/ultrasound functional imaging

In this pilot study, we explored a quantitative method to analyse characteristics of breast tumours using 3D volumetric data obtained from a three-dimensional (3D) photoacoustic/ultrasound (PA/US) functional imaging system. Imaging results from 24 Asian patients with maximum tumour diameters less than 2 cm, including 8 benign tumours, 16 T1 stage invasive breast cancers (IBCs), and 22 normal breasts, were analysed. We found that the volumetric mean oxygenation saturation (SO₂) in tumour regions of T1 stage IBCs was 7.7% lower than that of benign tumours (P = 0.016) and 3.9% lower than that of healthy breasts (P = 0.010). The volumetric mean SO₂ in tumour surrounding regions of T1 stage IBCs was 4.9% lower than that of benign tumours (P = 0.009). For differentiating T1 stage IBCs and benign tumours, with a cut-off SO₂ value of 78.2% inside tumours, we obtained a sensitivity of 100%, a specificity of 62.5%, and an AUC of 0.81; with a cut-off SO₂ value of 77.9% in regions surrounding tumours, we obtained a sensitivity of 100%, a specificity of 75% and an AUC of 0.84. Our preliminary results demonstrate that 3D PA/US functional imaging has the potential to provide valuable quantitative physiological information that may be useful for the detection and evaluation of breast tumours.

Do Mammographic Systems Affect the Performance of Computerized Parenchymal Analysis?

Early identification of women at high risk of developing breast cancer is fundamental for timely diagnosis and treatment. Recently, researchers have demonstrated that the computerized analysis of parenchymal (breast tissue) patterns in mammograms can be utilized to assess the risk level of patients. However, parenchymal analysis being an image-based biomarker, its performance may be affected by the acquisition parameters of the mammogram. Unfortunately, research on the effect of the mammographic system on the performance of parenchymal analysis is very scarce. In this paper, we implement a parenchymal analysis algorithm and study the effect of different mammographic systems on its performance. We show in a setting of 286 women that the use of different mammographic systems can yield differences of up to 24% in the area under the ROC curve. Results suggest the the construction of models for risk assessment based on parenchymal analysis should incorporate the imaging technologies into the analysis.

Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era

Breast cancer is one of the most leading causes of death for women worldwide. According to statistics published by the International Agency for Research on Cancer (IARC), the incidence of breast cancer is on the rise year by year in most parts of the world. The existence of heterogeneity limits the early diagnosis and targeted therapy of breast cancer. Nowadays, precision medicine brings a new perspective to personalized diagnosis and targeted therapy, overcomes the heterogeneity of different patients, and provides an opportunity for screening of high-risk populations. As a clinician, we are committed to using genomic to provide a favorable perspective in the field of breast cancer. The current review describes the recent advances in the understanding of precision medicine for breast cancer in the aspect of the genomics which could be applied to improve our ability to diagnose and treat breast cancer individually and effectively.

Risk-Adapted Starting Age of Screening for Relatives of Patients With Breast Cancer

Importance

Breast cancer screening guidelines acknowledge the need for earlier screening for women at increased risk but provide limited guidance for women with a family history of breast cancer. A risk-adapted starting age of screening for relatives of patients with breast cancer may help supplement current screening guidelines.

Objective

To identify the risk-adapted starting age of breast cancer screening on the basis of a woman's detailed family history.

Design, Setting, and Participants

This nationwide cohort study analyzed data recorded in the Swedish family-cancer data sets. All women born from 1932 onward and with at least 1 known first-degree relative (FDR) were included (N = 5 099 172). Data from January 1, 1958, to December 31, 2015, were collected. Data were analyzed from October 1, 2017, to March 31, 2019.

Exposures

Family history of breast cancer in FDRs and second-degree relatives (SDRs).

Main Outcomes and Measures

Primary invasive breast cancer diagnosis and the age at which women with different constellations of family history attained the risk level at which breast screening is usually recommended.

Results

Of the 5 099 172 women included in the study, 118 953 (2.3%) received a diagnosis of primary invasive breast cancer. A total of 102 751 women (86.4%; mean [SD] age at diagnosis, 55.9 [11.1] years) did not have family history of breast cancer in FDRs and SDRs at the time of their diagnosis. Risk-adapted starting age of breast cancer screening varied by number of FDRs and SDRs with breast cancer diagnosis and the age at diagnosis of the FDRs. For example, for screening recommendation at age 50 years for the general population (2.2% 10-year cumulative risk), women with multiple affected FDRs, with the youngest affected relative receiving a diagnosis before age 50 years, reached the benchmark risk level at age 27 years. When the youngest relative received a diagnosis after age 50 years, however, this risk level was attained at age 36 years.

Conclusions and Relevance

This study identifies possible risk-based starting ages for breast cancer screening based on population-based registers. These results may serve as high-quality evidence to supplement current screening guidelines for relatives of patients with breast cancer.

Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction

Background Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm² vs 34.2 cm²; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers. Conclusion Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Bahl in this issue.

Field cancerization in the understanding of parenchymal analysis of mammograms for breast cancer risk assessment

In recent years, mammographic image analysis has shown great potential for breast cancer risk assessment. The aim of risk assessment is to predict how likely a woman is to develop breast cancer in the future. Several studies suggest that computerized parenchymal analysis of mammograms can be utilized as an independent imaging biomarker of breast cancer. Parenchymal analysis consists of the quantitative assessment of visual texture patterns in mammograms to infer the level of risk. In spite of substantial evidence of the association between parenchymal patterns and breast cancer risk, its biological foundations remain poorly understood. In this work, we draw a hypothesis that links the field cancerization (FC) with breast cancer risk assessment based on the parenchymal analysis. In the literature, the FC is interpreted as a biochemical anomaly amplification in otherwise healthy cells due to the effect of pre-cancerous transformed cells in surrounding regions. Our hypothesis is that these biochemical anomaly amplifications change the cellular micro-environment which, in turn, alter tissue responses to X-ray radiation. As a result, it is reasonable to think that these changes influence the interaction of X-rays with parenchymal - the functional - breast tissue thus enabling cancer prediction by analyzing X-ray images of the breast. We believe that our hypothesis provides an actionable explanation as to how computerized parenchymal analysis of apparently normal mammograms can be successfully utilized for the stratification of breast cancer risk.

Valuing preferences for treating screen detected ductal carcinoma in situ

BACKGROUND

Mammographic screening reduces breast cancer mortality but may lead to the overdiagnosis and overtreatment of low-risk breast cancers. Conservative management may reduce the potential harm of overtreatment, yet little is known about the impact upon quality of life.

OBJECTIVES

To quantify women's preferences for managing low-risk screen detected ductal carcinoma in situ (DCIS), including the acceptability of active monitoring as an alternative treatment.

METHODS

Utilities (cardinal measures of quality of life) were elicited from 172 women using visual analogue scales (VASs), standard gambles, and the Euro-Qol-5D-5L questionnaire for seven health states describing treatments for low-risk DCIS. Sociodemographics and breast cancer history were examined as predictors of utility.

RESULTS

Both patients and non-patients valued active monitoring more favourably on average than conventional treatment. Utilities were lowest for DCIS treated with mastectomy (VAS: 0.454) or breast conserving surgery (BCS) with adjuvant radiotherapy (VAS: 0.575). The utility of active monitoring was comparable to BCS alone but was rated more favourably as progression risk was reduced from 40% to 10%. Disutility for active monitoring was likely driven by anxiety around progression, whereas conventional management impacted other dimensions of quality of life. The heterogeneity between individual preferences could not be explained by sociodemographic variables, suggesting that the factors influencing women's preferences are complex.

CONCLUSIONS

Active monitoring of low-risk DCIS is likely to be an acceptable alternative for reducing the impact of overdiagnosis and overtreatment in terms of quality of life. Further research is required to determine subgroups more likely to opt for conservative management.

Risk-tailored starting age of breast cancer screening based on women's reproductive profile: A nationwide cohort study

BACKGROUND

Although reproductive history is recognised to affect the risk of breast cancer, current breast cancer screening guidelines do not consider risk differences by this important factor. As there is a need for an earlier screening in women at increased risk of breast cancer, we provided evidence-based risk-adapted starting age of screening based on different reproductive profiles.

MATERIAL AND METHODS

We conducted a nationwide cohort study including 5,099,172 Swedish women born after 1931. Records of study participants in Swedish Cancer Registry, Multi-generation Register, Cause of Death Register, and national censuses (follow-up, 1958-2015) have been linked. We used 10-year cumulative risk of breast cancer curves to determine the age at which women with different reproductive factors attained the risk level at which breast screening is usually recommended.

RESULTS

The 10-year cumulative risk of breast cancer at age 40, 45 and 50 years in the general population, at which current screening guidelines recommend screening was calculated. We found that women with various reproductive factors (defined by parity and age at first birth) obtained this level of risk at different ages. The difference was between nine years later and three years earlier.

CONCLUSIONS

This study provides the age at which women with particular reproductive profile could start risk-adapted breast cancer screening. This supplies novel information for clinicians and women about when to start breast cancer screening and is an important step towards a personalised screening.

Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample

PURPOSE

To assess the association between breast cancer risk and mammographic parenchymal measures obtained using a fully-automated, publicly available software, OpenBreast.

METHODS

This retrospective case-control study involved screening mammograms of asymptomatic women diagnosed with breast cancer between 2016 and 2017. The 114 cases were matched with corresponding healthy controls by birth and screening years and the mammographic system used. Parenchymal analysis was performed using OpenBreast, a software implementing a computerized parenchymal analysis algorithm. Breast percent density was measured with an interactive thresholding method. The parenchymal measures were Box-Cox transformed and adjusted for age and percent density. Changes in the odds ratio per standard deviation (OPERA) with 95% confidence intervals (CIs) and the area under the ROC curve (AUC) for parenchymal measures and percent densities were used to evaluate the discrimination between cases and controls. Differences in AUCs were assessed using DeLong's test.

RESULTS

The adjusted OPERA value of parenchymal measures was 2.49 (95% CI: 1.79-3.47). Parenchymal measures using OpenBreast were more accurate (AUC = 0.779) than percent density (AUC = 0.609) in discriminating between cases and controls (p < 0.001).

CONCLUSIONS

Parenchymal measures obtained with the evaluated software were positively associated with breast cancer risk and were more accurate than percent density in the prediction of risk.

Survey of primary care physicians' views about breast and ovarian cancer screening for true BRCA1/2 non-carriers

Despite some controversy, true BRCA1/2 non-carriers are generally considered to be at an average risk for breast and ovarian cancer. Primary care physicians are then expected to encourage their non-carrier patients to adopt cancer screening practices appropriate to women of the same age in the general population. This study aimed to describe breast and ovarian cancer screening recommendations that primary care physicians would consider advisable for young true BRCA1/2 non-carriers. One hundred thirty-four family physicians and 123 gynecologists (response rate 45%) completed a cross-sectional mailed survey administered in the Province of Quebec, Canada. The survey included questions about basic genetic knowledge and screening recommendations for two fictitious cases (< 40 years), one carrier and one non-carrier, from a BRCA1/2 mutation-positive family. Screening exams considered advisable did not differ significantly between family physicians and gynecologists. More than 75% of physicians considered the cancer risks of true non-carriers to be comparable with that of the general population and 14% to be a little higher. Still, 53% would prescribe a biennial and or even an annual (27%) mammography to a non-carrier woman before the recommended starting age. Physician considerations of non-carriers' expectations or requests for screening were associated with more screening prescriptions. More than half of primary care physicians would recommend more mammography screenings than expected for a young true BRCA1/2 non-carrier. Personalized cancer risk assessment may help primary care physicians tailor screening of women from BRCA1/2 mutation-positive families and allow these women to make more informed choices regarding cancer risk management options.

Effects of Personalized Risk Information on Patients Referred for Lung Cancer Screening with Low-Dose CT

Background. Low-dose computed tomography (LDCT) screening for lung cancer is a preference-sensitive intervention that should ideally be individualized according to patients' likelihood of benefit and personal values. Personalized cancer risk information (PCRI) may facilitate this goal, but its effects are unknown. Objective. To evaluate the effects of providing PCRI to patients referred for LDCT screening. Design. Mixed-methods, pre-post study using surveys administered to patients before and after provision of PCRI-calculated by the PLCOm2012 risk prediction model-in shared decision-making consultations, and postvisit qualitative interviews. Setting. Centralized specialty-based LDCT screening program at a tertiary care hospital. Participants. Convenience sample of eligible patients referred for LDCT screening. Measurements. Pre- and postvisit surveys assessed patients' 1) perceived lung cancer risk, 2) uncertainty about their risk, 3) minimum risk threshold for wanting screening, 4) interest in LDCT screening, and 5) interest in smoking cessation. Qualitative interviews explored patients' perceptions of the value of PCRI. Screening uptake was assessed by chart review. Results. Sixty of 70 (86%) patients received PCRI and completed pre-post surveys, and 17 patients (28%) completed qualitative interviews. Perceived lung cancer risk decreased from 52% previsit to 31% postvisit (P < 0.0001). However, patients' minimum risk thresholds for screening decreased, their screening interest increased, and all patients completed screening. Qualitative interviews corroborated these effects, suggesting that patients discount and interpret PCRI according to preexisting beliefs and attitudes. Limitations. The study population was a relatively small, single-institution sample of patients referred for screening. Conclusions. Personalized cancer risk information decreases cancer risk perceptions of patients referred for LDCT screening, but has complex effects on screening-related judgments and decisions. The value of PCRI for patients considering LDCT screening requires further investigation.

Calibrated Breast Density Measurements

RATIONALE AND OBJECTIVES

Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study.

MATERIALS AND METHODS

A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated.

RESULTS

In adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60.

CONCLUSION

The calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.

Breast cancers missed during screening in a tertiary-care hospital mammography facility

BACKGROUND

Breast cancer is the most common cancer in females worldwide. Screening with mammography for early breast cancer detection is standard community practice in many countries.

OBJECTIVE

Identify causes of missed breast cancers during screening.

DESIGN

Retrospective, observational.

SETTING

Department of radiology at a tertiary-care hospital mammographic screening facility.

PATIENTS AND METHODS

All women who came with initial negative screens from July 2015 to July 2018 were retrospectively reviewed and followed-up for their second or subsequent mammographic screening. Missed breast cancer was defined as a cancer that was detected on a subsequent mammogram with an initial negative screen. Mammograms were interpreted by two radiologists as per BIRADS (Breast Imaging Reporting and Data System) lexicon. Causes of missed breast cancers were categorized as imaging acquisition (IA), imaging feature (IF) and imaging interpretation (II). True (occult) incident breast cancers were also documented. Percentage estimations for these causes were calculated.

MAIN OUTCOME MEASURES

Breast cancer detection on follow-up screening.

SAMPLE SIZE

943 women.

RESULTS

Of 15 (1.6%) screening-detected breast cancers, 7 cases (46.6%) were missed on the initial screen; 3 (43%) of these were II related, 2 (28.5%) of each were IA and IF. The remaining true (occult) cases were detected on either the second (5 cases) or third screens (3 cases).

CONCLUSION

Improved screening facilities, quality mammographic acquisition and interpretation, double reading, and implementation of an organized screening program may help to avoid missed breast cancers.

LIMITATIONS

Retrospective, small sample, single center, and short duration study.

CONFLICT OF INTEREST

None.

Age-based versus Risk-based Mammography Screening in Women 40-49 Years Old: A Cross-sectional Study

Background Risk-based screening in women 40-49 years old has not been evaluated in routine screening mammography practice. Purpose To use a cross-sectional study design to compare the trade-offs of risk-based and age-based screening for women 45 years of age or older to determine short-term outcomes. Materials and Methods A retrospective cross-sectional study was performed by using a database of 20 539 prospectively interpreted consecutive digital screening mammograms in 10 280 average-risk women aged 40-49 years who were screened at an academic medical center between January 1, 2006, and December 31, 2013. Two hypothetical screening scenarios were compared: an age-based (≥45 years) scenario versus a risk-based (a 5-year risk of breast cancer greater than that of an average 50-year-old) scenario. Risk factors for risk-based screening included family history, race, age, prior breast biopsy, and breast density. Outcomes included breast cancers detected at mammography, false-positive mammograms, and benign biopsy findings. Short-term outcomes were compared by using the χ² test. Results The screening population included 71 148 screening mammograms in 24 928 women with a mean age of 55.5 years ± 8.9 (standard deviation) (age range, 40-74 years). In women 40-49 years old, usual care included 50 screening-detected cancers, 1787 false-positive mammograms, and 384 benign biopsy results. The age-based (≥45 years) screening strategy revealed more cancers than did the risk-based strategy (34 [68%] vs 13 [26%] of 50; P < .001), while prompting more false-positive mammograms (899 [50.3%] vs 216 [12.1%] of 1787; P < .001) and benign biopsy results (175 [45.6%] vs 49 [12.8%] of 384; P < .001). The risk-based strategy demonstrated low levels of eligibility (few screenings) in the 40-44-year age group. Differences in outcomes in the 45-49-year age group explained the overall hypothetical screening strategy differences. Conclusion Risk-based screening for women 40-49 years old includes few women in the 40-44-year age range. Significant trade-offs in the 45-49-year age group explain the overall difference between hypothetical screening scenarios, both of which reduce the benefits as well as the harms of mammography for women 40-49 years old. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Joe and Hayward in this issue.

Risk-reducing medications for primary breast cancer: a network meta-analysis

BACKGROUND

Breast cancer is the most frequently occurring malignancy and the second cause of death for cancer in women. Cancer prevention agents (CPAs) are a promising approach to reduce the burden of breast cancer. Currently, two main types of CPAs are available: selective estrogen receptor modulators (SERMs, such as tamoxifen and raloxifene) and aromatase inhibitors (AIs, such as exemestane and anastrozole).

OBJECTIVES

To assess the efficacy and acceptability of single CPAs for the prevention of primary breast cancer, in unaffected women, at an above-average risk of developing breast cancer.Using a network meta-analysis, to rank single CPAs, based on their efficacy and acceptability (an endpoint that is defined as the inverse of CPA-related toxicity).

SEARCH METHODS

We searched the Cochrane Breast Cancer Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, World Health Organization's International Clinical Trials Registry Platform (WHO ICTRP), and ClinicalTrials.gov on 17 August 2018. We handsearched reference lists to identify additional relevant studies.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) that enrolled women without a personal history of breast cancer but with an above-average risk of developing a tumor. Women had to be treated with a CPA and followed up to record the occurrence of breast cancer and adverse events.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data and conducted risk of bias assessments of the included studies, and assessed the certainty of the evidence using GRADE. Outcome data included incidence of breast carcinoma (both invasive and in situ carcinoma) and adverse events (both overall and severe toxicity). We performed a conventional meta-analysis (for direct comparisons of a single CPA with placebo or a different CPA) and network meta-analysis (for indirect comparisons).

MAIN RESULTS

We included six studies enrolling 50,927 women randomized to receive one CPA (SERMs: tamoxifen or raloxifene, or AIs: exemestane or anastrozole) or placebo. Three studies compared tamoxifen and placebo, two studies compared AIs (exemestane or anastrozole) versus placebo, and one study compared tamoxifen versus raloxifene. The risk of bias was low for all RCTs.For the tamoxifen versus placebo comparison, tamoxifen likely resulted in a lower risk of developing breast cancer compared to placebo (risk ratio (RR) 0.68, 95% confidence interval (CI) 0.62 to 0.76; 3 studies, 22,832 women; moderate-certainty evidence). In terms of adverse events, tamoxifen likely increased the risk of severe toxicity compared to placebo (RR 1.28, 95% CI 1.12 to 1.47; 2 studies, 20,361 women; moderate-certainty evidence). In particular, women randomized to receive tamoxifen experienced a higher incidence of both endometrial carcinoma (RR 2.26, 95% CI 1.52 to 3.38; high-certainty evidence) and thromboembolism (RR 2.10, 95% CI 1.14 to 3.89; high-certainty evidence) compared to women who received placebo.For the AIs versus placebo comparison, AIs (exemestane or anastrozole) reduced the risk of breast cancer by 53% (RR 0.47, 95% CI 0.35 to 0.63; 2 studies, 8424 women; high-certainty evidence). In terms of adverse events, AIs increased the risk of severe toxicity by 18% (RR 1.18, 95% CI 1.09 to 1.28; 2 studies, 8352 women; high-certainty evidence). These differences were sustained especially by endocrine (e.g. hot flashes), gastrointestinal (e.g. diarrhea), and musculoskeletal (e.g. arthralgia) adverse events, while there were no differences in endometrial cancer or thromboembolism rates between AIs and placebo.For the tamoxifen versus raloxifene comparison, raloxifene probably performed worse than tamoxifen in terms of breast cancer incidence reduction (RR 1.25, 95% CI 1.09 to 1.43; 1 study, 19,490 women; moderate-certainty evidence), but its use was associated with lower toxicity rates (RR 0.87, 95% CI 0.80 to 0.95; 1 study, 19,490 women; moderate-certainty evidence), particularly relating to incidence of endometrial cancer and thromboembolism.An indirect comparison of treatment effects allowed us to compare the SERMs and AIs in this review. In terms of efficacy, AIs (exemestane or anastrozole) may have reduced breast cancer incidence slightly compared to tamoxifen (RR 0.67, 95% CI 0.46 to 0.98; 5 RCTs, 31,256 women); however, the certainty of evidence was low. A lack of model convergence did not allow us to analyze toxicity data.

AUTHORS' CONCLUSIONS

For women with an above-average risk of developing breast cancer, CPAs can reduce the incidence of this disease. AIs appear to be more effective than SERMs (tamoxifen) in reducing the risk of developing breast cancer. AIs are not associated with an increased risk of endometrial cancer and thromboembolic events. However, long-term data on toxicities from tamoxifen are available while the follow-up toxicity data on unaffected women taking AIs is relatively short. Additional data from direct comparisons are needed to fully address the issues of breast cancer prevention by risk-reducing medications, with special regards to acceptability (i.e. the benefit/harm ratio).

"A Natural Progression": Australian Women's Attitudes About an Individualized Breast Screening Model

Individualized screening is our logical next step to improve population breast cancer screening in Australia. To explore breast screening participants' views of the current program in Victoria, Australia, examine their openness to change, and attitudes toward an individualized screening model, this qualitative work was performed from a population-based breast screening cohort. This work was designed to inform the development of a decision aid to facilitate women's decisions about participating in individualized screening, and to elicit Australian consumer perspectives on the international movement toward individualized breast screening. A total of 52 women participated in one of four focus groups, and were experienced with screening with 90% of participants having had more than three mammograms. Focus group discussion was facilitated following three main themes: (i) experience of breast screening; (ii) breast cancer risk perception, and (iii) views on individualized screening. Participants had strong, positive, emotional ties to breast screening in its current structure but were supportive, with some reservations, of the idea of individualized screening. There was good understanding about the factors contributing to personalized risk and a wide range of opinions about the inclusion of genetic testing with genetic testing being considered a foreign and evolving domain. Individualized breast screening that takes account of risk factors such as mammographic density, lifestyle, and genetic factors would be acceptable to a population of women who are invested in the current system. The communication and implementation of a new program would be critical to its acceptance and potential success. Reservations may be had in regards to uptake of genetic testing, motivations behind the change, and management of the women allocated to a lower risk category.

Implementing a Population-Based Breast Cancer Risk Assessment Program

BACKGROUND

Personalized breast cancer risk assessment is important in identifying and managing women at increased risk for breast cancer. However, there has been little evaluation of the practical aspects of implementing a population-based program that identifies and refers high-risk patients for further evaluation.

PATIENTS AND METHODS

We implemented a semiautomated approach to collect personal and family history to identify women at high risk of breast cancer. On the basis of the survey, women identified as elevated risk received letters inviting them to telephone consultations with licensed breast health genetic counselors (BHGCs). High-risk women's history was verified and counseling and referrals provided, as appropriate.

RESULTS

Among 20,558 women screened, 2000 (9.7%) women were identified as high risk on the basis of patient initial report. However, most (1,580) were excluded from receiving risk communication after BHGC review of risk information with the woman or because of previous attention to breast cancer risk or an abnormal mammogram. Among 420 subjects who received risk letters, 225 received a BHGC consultation. Of these 225 women, 63 were reclassified as average risk, 158 were referred to high-risk clinics, and 5 consultations were incomplete after determining that further information was needed. Of the 158 women referred to high-risk breast clinics, 51 attended an appointment.

CONCLUSION

This study highlights the complex nature of a population-based breast cancer screening program in a clinical setting and shows the substantial effort needed to identify newly discovered women at high risk for breast cancer and refer them to appropriate services.

Health professionals' perspectives on breast cancer risk stratification: understanding evaluation of risk versus screening for disease

BACKGROUND

Younger women at higher-than-population-average risk for breast cancer may benefit from starting screening earlier than presently recommended by the guidelines. The Personalized Risk Stratification for Prevention and Early Detection of Breast Cancer (PERSPECTIVE) approach aims to improve the prevention of breast cancer through differential screening recommendations based on a personal risk estimate. In our study, we used deliberative stakeholder consultations to engage health professionals in an in-depth dialog to explore the feasibility of the proposed implementation strategies for this new personalized breast cancer screening approach.

METHODS

Deliberative stakeholder consultation is a qualitative descriptive study design used to engage health professionals in the discussion, while the mediators play a more passive role. A purposeful sample of 11 health professionals (family physicians and genetic counselors) working in Montreal was used. The deliberations were organized in two phases, including small group deliberations according to the deliberants' health profession and a mixed group deliberation combining participants from the small groups. Inductive thematic content analysis was performed on the transcripts by two coders to create the deliberative and analytic outputs. Quality of deliberations was assessed quantitatively using the de Vries method and qualitatively using participant observation.

RESULTS

One of our key findings was that health professionals lacked understanding of the two steps of the screening approach: risk stratification "screening," which is an evaluation for the level of risk and screening for disease. As part of this confusion, the main topic of concern was a justification of program implementation as a population-wide screening, based on their uncertainty that it will be beneficial for women with near-population risks. Despite the noted difficulties concerning implementation, health professionals acknowledged the substantial benefits of the proposed PERSPECTIVE program.

CONCLUSIONS

Our study was the first to evaluate the perspectives of health professionals on the implementation and benefits of a new program for breast cancer risk stratification with the purpose of personalizing screening for disease. This new multi-step approach to screening requires more clarity in communication with health professionals. To implement and maintain effective screening, engagement of family physicians with other health professionals or even development of a centralized public health system may be needed.