The current status of risk-stratified breast screening

A stratified precision screening strategy for enhancing hepatitis B- and C-associated liver cancer detection: a prospective study

This study explores new screening strategies to enhance liver cancer screening effectiveness. In a prospective study, 2605 participants underwent baseline, 6-months self-reported, and 1-year follow-up screenings using abdominal ultrasonography, AFP, AFP-L3%, and DCP. The results demonstrated the GALADUS protocol exhibited superior performance with higher AUC (0.935 vs. 0.836; DeLong P < 0.001), sensitivity (91.0% vs. 70.8%; P < 0.001), detection (3.1% vs. 2.4%; P < 0.001), and early diagnosis rates (64.2% vs. 58.7%) compared to the AFP/US protocol. Notably, among individuals with an aMAP score ≥ 60, GALADUS had significantly outperformed AFP/US in AUC (0.923 vs. 0.826; DeLong P < 0.001), sensitivity (94.2% vs. 69.6%; P < 0.001), detection (9.7% vs. 7.2%; P < 0.001), and early diagnosis rates (63.1% vs. 54.2%). However, for those with an aMAP score < 60, GALADUS offered no significant advantages. Introducing the "aMAP triage" protocol, combining GALADUS for aMAP ≥ 60 and AFP/US for aMAP < 60, further enhanced AUC to 0.925 (DeLong P < 0.001), improved sensitivity by 19.1% (89.9% vs. 70.8%; P < 0.001), and increased detection (3.1% vs. 2.4%; P < 0.001) and early diagnosis rates (65.0% vs. 58.7%), being cost-effective compared to GALADUS. In conclusion, this study highlights the potential of a stratified precision screening strategy in identifying high-risk individuals, applying tailored early detection protocols to improve liver cancer screening efficacy.

Systems epigenetic approach towards non-invasive breast cancer detection

No study has systematically compared the suitability of DNA methylation (DNAme) profiles in non-invasive samples for the detection of breast cancer (BC). We assess non-tumour DNAme in 1,100 cervical, buccal, and blood samples from BC cases and controls and find that cervical samples exhibit the largest nuber of differentially methylated sites, followed by buccal samples. No sites were significant in blood after FDR adjustment. Deriving DNAme-based classifiers for BC detection in each sample type (WID-buccal-, cervical-, or blood-BC), we achieve validation AUCs of 0.75, 0.66, and 0.51, respectively. Buccal and cervical BC-associated DNAme alterations distinguish between BC cases and controls in both surrogate and breast tissue (AUC > 0.88), yet individual sites and the directionality of methylation changes are not identical between these two sample types, and buccal sample DNAme aligns with breast methylation changes more closely. Pending additional validation, these insights may have the potential to improve non-invasive personalized BC prevention.

Personalized screening based on risk and density: prevalence data from the RIBBS study

PURPOSE

To present the prevalence screening results of the RIsk-Based Breast Screening (RIBBS) study (ClinicalTrials.gov NCT05675085), a quasi-experimental population-based study evaluating a personalized screening model for women aged 45-49. This model uses digital breast tomosynthesis (DBT) and stratifies participants by risk and breast density, incorporating tailored screening intervals with or without supplemental imaging (ultrasound, US, and breast MRI), with the goal of reducing advanced breast cancer (BC) incidence compared to annual digital mammography (DM).

MATERIALS AND METHODS

An interventional cohort of 10,269 women aged 45 was enrolled (January 2020-December 2021. Participants underwent DBT and completed a BC risk questionnaire. Volumetric breast density and lifetime risk were used to assign five subgroups to tailored screening regimens: low-risk low-density (LR-LD), low-risk high-density (LR-HD), intermediate-risk low-density (IR-LD), intermediate-risk high-density (IR-HD), and high-risk (HR). Screening performance was compared with an observational control cohort of 43,838 women undergoing annual DM.

RESULTS

Compared to LR-LD, intermediate-risk groups showed a 4.9- (IR-LD) and 4.6-fold (IR-HD) higher prevalence of BC, driven by a 7.1- and 7.1-fold higher prevalence of pT1c tumors. The interventional cohort had lower recall rate (rate ratio, 0.5), higher surgery rate (1.9) and increased prevalence of DCIS (2.9), pT1c (2.3) and grade 3 tumors (2.4), compared to controls.

CONCLUSION

The prevalence screening demonstrated the feasibility of using DBT and -in high-density subgroups- supplemental US. The stratification criteria effectively identified subpopulations with different BC prevalence. Increasing the detection rate of pT1c tumors is not sufficient but necessary to achieve a reduction in advanced BC incidence.

Assessment of breast composition in MRI using artificial intelligence - A systematic review

INTRODUCTION

Magnetic Resonance Imaging (MRI) performs a critical role in breast cancer diagnosis, especially for high-risk populations e.g. family history. MRI could take advantage of the implementation of artificial intelligence (AI). AI assessment of breast composition factors is less studied than those of lesion detection and classification. These factors are breast density, background parenchymal enhancement (BPE) and fibroglandular tissue (FGT), which are recognized breast cancer phenotypes.

METHODS

Following PRISMA guidelines, the PROSPERO registered review examined the role of AI in assessing breast composition in MRI. A search of articles from Pubmed, Ovid, Embase, Web of Science, Cochrane, and Google scholar from 2010 to 2022 was conducted. Peer-reviewed, in-vivo studies were included based on defined search categories. Adapted QUADAS-2, CASP and Covidence tools were utilized for quality assessment.

RESULTS

Seven studies were identified as being of sufficiently high quality. The studies showed that AI has the potential to provide a comparable level of accuracy against the relevant reference standard. There were limited performance results when delineating BPE and FGT BI-RADs categories. The review highlighted the variability in AI models while the range of statistical methods and small cohort sizes limited cross study compatibility.

CONCLUSIONS

AI has potential in assessing breast composition in MRI. However, variability in AI systems deployed and statistical measurements alongside limited validation across diverse populations remain an issue. AI systems may perform better with binary categorizations rather than the quaternary spectrum of BI-RADS.

IMPLICATIONS FOR PRACTICE

AI could assist in developing personalized breast composition assessments. Future developments could focus on better delineation of breast composition categories. AI models that have trained on more diverse and larger populations should result in more robust and effective clinical applications.

Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction

Objective.State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the model's BCR discrimination performance, calibration, and robustness.Approach.First, the Original deep learning model (MIRAI), trained on the US (Massachusetts General Hospital) data, was validated, and the relative contribution of PM to BCR predictions was evaluated using saliency maps. Additionally, 23 792 mammograms from the Slovenian screening program were collected and two datasets were created, with and without screening positive exams. The original MIRAI was then fine-tuned on the training/fine-tuning set of Slovenian mammograms with and without PM, creating Fine-tuned MIRAI models. In total, four models (Original MIRAI with PM, Original MIRAI without PM, Fine-tuned MIRAI with PM, Fine-tuned MIRAI without PM) were compared on a test set in terms of discrimination performance for 1-5 Year BCR (evaluating area under the curve), calibration performance (measured with expected calibration error-ECE) and robustness to incremental PM removals/additions, and to incremental breast tissue removals.Results.The relative contribution of PM to the BCR prediction on the Original MIRAI model was low (∼5%); however, there were significant outliers where the relative contribution was more than 50%. The removal of PM did not impact the 1-5 Year BCR discrimination performance of the Original MIRAI (with screening positive exams: 0.77-0.91, without screening positive exams: 0.64-0.67). Fine-tuned MIRAI on mammograms with PM removed achieved significantly higher 1-5 Year BCR discrimination performance (with screening positive exams: 0.82-0.93, without screening positive exams: 0.71-0.79). After recalibration, all models had similar ECE (with screening positive exams: 0.04-0.05, without screening positive exams: 0.02-0.03).Significance.Improved BCR discrimination performance can be achieved when the model is trained/fine-tuned on mammograms with PM removed.

Causative Genes of Homologous Recombination Deficiency (HRD)-Related Breast Cancer and Specific Strategies at Present

Recently, homologous recombination deficiency (HRD) has become a new target for hereditary cancers. Molecular-based approaches for hereditary cancers in the clinical setting have been reviewed. In particular, the efficacy of the PARP inhibitor has been considered by several clinical trials for various kinds of hereditary cancers. This indicates that the PARP inhibitor can be effective for any kind of BRCA mutated cancers, regardless of the organ-specific cancer. Homologous recombination deficiency (HRD) has become a new target for hereditary cancers, indicating the necessity to confirm the status of HRD-related genes. ARID1A, ATM, ATRX, PALB2, BARD1, RAD51C and CHEK2 are known as HRD-related genes for which simultaneous examination as part of panel testing is more suitable. Both surgical and medical oncologists should learn the basis of genetics including HRD. An understanding of the basic mechanism of homologous repair recombination (HRR) in BRCA-related breast cancer is mandatory for all surgical or medical oncologists because PARP inhibitors may be effective for these cancers and a specific strategy of screening for non-cancers exists. The clinical behavior of each gene should be clarified based on a large-scale database in the future, or, in other words, on real-world data. Firstly, HRD-related genes should be examined when the hereditary nature of a cancer is placed in doubt after an examination of the relevant family history. Alternatively, HRD score examination is a solution by which to identify HRD-related genes at the first step. If lifetime risk is estimated at over 20%, an annual breast MRI is necessary for high-risk screening. However, there are limited data to show its benefit compared with BRCA. Therefore, a large-scale database, including clinical information and a long-term follow-up should be established, after which a periodical assessment is mandatory. The clinical behavior of each gene should be clarified based on a large-scale database, or, in other words, real-world data.

A practical work around for breast density distribution discrepancies between mammographic images from different vendors

OBJECTIVES

Investigate the impact of mammography device grouped by vendor on volumetric breast density and propose a method that mitigates biases when determining the proportion of high-density women.

MATERIALS AND METHODS

Density grade class and volumetric breast density distributions were obtained from mammographic images from three different vendor devices in different centers using breast density evaluation software in a retrospective study. Density distributions were compared across devices with a Mann-Whitney U test and breast density thresholds corresponding to distribution percentiles calculated. A method of matching density percentiles is proposed to determine women at potentially high risk while mitigating possible bias due to the device used for screening.

RESULTS

2083 (mean age 59 ± 5.4), 531 (mean age 58.8 ± 5.7) and 244 (mean age 60.7 ± 6.0) screened women were evaluated on three vendor devices, respectively. Both the density grade distribution and the volumetric breast density were different between Vendor 1 and Vendor 2 data (p < 0.001) and between Vendor 1 and Vendor 3 data (p < 0.001). Between Vendor 2 and Vendor 3, no significant difference was observed (p = 0.67 for density grade, p = 0.29 for volumetric density). To recruit the top 10% of women with extremely dense breasts required respective density thresholds of 16.1%, 13.6% and 13.8% for the three vendor devices.

CONCLUSION

Density grade class and volumetric breast density distributions differ between devices grouped by vendor and can result in statistically different breast density distributions. Percentile-dependent density thresholds can ensure unbiased selection of high-risk women.

KEY POINTS

Question Does the use of x-ray systems from different vendors influence breast density evaluation and the resulting selection of high-risk women during breast cancer screening? Findings Statistically significant differences were observed between breast density distributions of different vendors; a method of matching via percentiles is proposed to prevent biased density evaluations. Clinical relevance Measured breast density distributions differed between X-ray devices. A workaround is proposed that determines density thresholds corresponding to a specified population, allowing the same proportion of women to be selected with a density algorithm.

A Scoping Review of Primary Breast Cancer Risk Reduction Strategies in East and Southeast Asia

Breast cancer (BC) screening enables early detection and timely treatment of cancer. Improving the effectiveness of BC screening can be accomplished by personalizing screening schedules according to each woman's specific risk level. However, when informing women about their risk classification, especially those at high risk, it is important to give clear recommendations on how to lower their risk. BC risk reduction comprises lifestyle modifications, preventive surgery, and chemoprevention, with the latter two being particularly applicable to high-risk individuals. Public health guidance on risk-reducing interventions is heterogeneous and context-dependent. We conducted a scoping review on BC surgical interventions and chemoprevention in East and Southeast Asia in publications between 2010 and 2024. We searched two databases and identified 23 publications relevant for inclusion. The highest number of publications came from South Korea (n = 9). More publications discussed surgical interventions compared to pharmacological interventions. The studies were largely observational and utilized data from medical records. Most studies defined high-risk individuals as BRCA carriers, many of whom previously had cancer. The field would benefit from randomized studies of BC prevention strategies focusing on Asian populations. Future research could explore women's sentiments towards chemoprevention compared to prophylactic surgery and could extend the definition of high-risk individuals beyond BRCA carriers.

Using a behaviour-change approach to support uptake of population genomic screening and management options for breast or prostate cancer

As the possibility of implementing population genomic screening programs for the risk of developing hereditary cancers in health systems increases, understanding how to support individuals who wish to have genomic screening is essential. This qualitative study aimed to link public perceived barriers to a) taking up the offer of population genomic screening for breast or prostate cancer risk and b) taking up risk-management options following their result, with possible theory-informed behaviour-change approaches that may support implementation. Ten focus groups were conducted with a total of 25 members of the Australian public to identify and then categorise barriers within the behaviour-change Capability, Opportunity, Motivation - Behaviour (COM-B) model. Ten COM-B categorised barriers were identified as perceived influences on an individual's intentions to take-up the offer, including Capability (e.g., low public awareness), Opportunity (e.g., inconvenient sample collection procedure) and Motivation (e.g., genomic screening not perceived as relevant to an individual). Ten barriers for taking up risk-management options included Motivation (e.g., concerns about adverse health impact) and Opportunity (e.g., social opportunity and cost incurred to the individual). Our findings demonstrate that a nuanced approach is required to support people to take-up the offer of population genomic screening and, where appropriate, to adopt risk-management options. Even amongst participants who were enthusiastic about a population genomic screening program, needs were varied, demanding a range of implementation strategies. Promulgating equitable uptake of genomic screening and management options for breast and prostate cancer risk will require a needs-based approach.

Breast cancer risk assessment based on susceptibility genes and polygenic risk score in Vietnamese women

BACKGROUND

Breast screening recommendation based on individual risk assessment is emerging as an alternative approach to improve compliance and efficiency to detect breast cancer (BC) early. In Vietnam, prior knowledge to stratify risk based on genetic factors is currently lacking.

METHODS

This study recruited 892 BC patients and 735 healthy Vietnamese women from 2016 to 2021. DNA from blood samples of BC patients was first analyzed for pathogenic variants associated with hereditary breast and ovarian cancer syndrome (HBOC). For patients with no HBOC and healthy participants, DNA was genotyped for 398 BC susceptibility single-nucleotide polymorphism (SNPs) by next-generation sequencing to identify significantly associated SNPs and construct a polygenic risk score (PRS).

RESULTS

The prevalence of HBOC predisposition gene mutations in Vietnamese women with BC was 5.4%. HBOC cases were significantly younger and enriched in the age group of 20-39 years old. In patients with no HBOC, we found 36 SNPs significantly associated with BC that were mostly similar to other Asian ethnicities; 34 of them were used to build a PRS model achieving an area under the receiver operating characteristics curve of 0.61 (95% CI: 0.56-0.68). Women in the top 1% PRS percentile had an odds ratio of 5.09 (95% CI: 3.10-7.86) while those in the bottom 1% had an odds ratio of 0.21 (95% CI: 0.09-0.39) to develop BC.

CONCLUSIONS

This study provides the first large datasets for HBOC gene analysis, BC susceptibility SNP association testing, and PRS modeling for Vietnamese women. Together, these data could aid the development of personalized BC screening recommendations for women in Vietnam.

Mobile breast screening services in Australia: A qualitative exploration of perceptions and experiences among rural and remote women aged ≥75 years

OBJECTIVE

This qualitative descriptive study draws on data collected from a sub-sample of 15 women participating in a national study (n = 60) exploring the breast cancer screening motivations and behaviours of women aged ≥75 years. The study aimed to understand why women living in rural and remote areas might continue accessing mobile breast cancer screening despite being outside the targeted age range.

SETTING

Settings ranged from large towns to very remote communities (according to Monash Modified Model (MMM) classification 3-7) where BreastScreen Australia mobile screening services were available.

PARTICIPANTS

Interview data from 15 women aged ≥75 years living in rural and remote locations who had used mobile screening services was utilised for this study.

DESIGN

In-depth individual interviews were conducted via telephone or online platform (Zoom). These were transcribed verbatim and imported into NVivo software to enable thematic analysis to identify key themes.

RESULTS

Many women aged ≥75 years in rural and remote areas expressed clear intentions to continue breast cancer screening, despite no longer being invited to do so. They perceived great value in the mobile service and were highly appreciative for it yet acknowledged limited sources of information about the process of ongoing screening.

CONCLUSION

Few women in rural and remote areas had discussed ongoing breast cancer screening with their general practitioner (GP). More information is required to inform women about the risks and benefits of ongoing screening. Without an invitation to attend screening rural women reported difficulty in knowing when the service would be available. Ongoing notification of the availability of mobile services for women aged ≥75 years in rural areas is recommended.

Radiomics in breast cancer: Current advances and future directions

Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.

Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening

Importance

Previous research has shown good discrimination of short-term risk using an artificial intelligence (AI) risk prediction model (Mirai). However, no studies have been undertaken to evaluate whether this might translate into economic gains.

Objective

To assess the cost-effectiveness of incorporating risk-stratified screening using a breast cancer AI model into the United Kingdom (UK) National Breast Cancer Screening Program.

Design, Setting, and Participants

This study, conducted from January 1, 2023, to January 31, 2024, involved the development of a decision analytical model to estimate health-related quality of life, cancer survival rates, and costs over the lifetime of the female population eligible for screening. The analysis took a UK payer perspective, and the simulated cohort consisted of women aged 50 to 70 years at screening.

Exposures

Mammography screening at 1 to 6 yearly screening intervals based on breast cancer risk and standard care (screening every 3 years).

Main Outcomes and Measures

Incremental net monetary benefit based on quality-adjusted life-years (QALYs) and National Health Service (NHS) costs (given in pounds sterling; to convert to US dollars, multiply by 1.28).

Results

Artificial intelligence-based risk-stratified programs were estimated to be cost-saving and increase QALYs compared with the current screening program. A screening schedule of every 6 years for lowest-risk individuals, biannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million, with QALY values set at £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Even in scenarios where decision-makers hesitate to allocate additional NHS resources toward screening, implementing the proposed strategies at a QALY value of £1 (US $1.28) was estimated to generate a yearly monetary benefit of approximately £10.6 (US $13.6) million.

Conclusions and Relevance

In this decision analytical model study of integrating risk-stratified screening with a breast cancer AI model into the UK National Breast Cancer Screening Program, risk-stratified screening was likely to be cost-effective, yielding added health benefits at reduced costs. These results are particularly relevant for health care settings where resources are under pressure. New studies to prospectively evaluate AI-guided screening appear warranted.

Targeted Screening for Cancer: Learnings and Applicability to Melanoma: A Scoping Review

This scoping review aims to systematically gather evidence from personalized cancer-screening studies across various cancers, summarize key components and outcomes, and provide implications for a future personalized melanoma-screening strategy. Peer-reviewed articles and clinical trial databases were searched for, with restrictions on language and publication date. Sixteen distinct studies were identified and included in this review. The studies' results were synthesized according to key components, including risk assessment, risk thresholds, screening pathways, and primary outcomes of interest. Studies most frequently reported about breast cancers (n = 7), followed by colorectal (n = 5), prostate (n = 2), lung (n = 1), and ovarian cancers (n = 1). The identified screening programs were evaluated predominately in Europe (n = 6) and North America (n = 4). The studies employed multiple different risk assessment tools, screening schedules, and outcome measurements, with few consistent approaches identified across the studies. The benefit-harm assessment of each proposed personalized screening program indicated that the majority were feasible and effective. The establishment of a personalized screening program is complex, but results of the reviewed studies indicate that it is feasible, can improve participation rates, and screening outcomes. While the review primarily examines screening programs for cancers other than melanoma, the insights can be used to inform the development of a personalized melanoma screening strategy.

Correlations of Imaging and Therapy in Breast Cancer Based on Molecular Patterns: An Important Issue in the Diagnosis of Breast Cancer

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer.

Ductal Carcinoma In Situ

In breast cancer (BC) pathogenesis models, normal cells acquire somatic mutations and there is a stepwise progression from high-risk lesions and ductal carcinoma in situ to invasive cancer. The precancer biology of mammary tissue warrants better characterization to understand how different BC subtypes emerge. Primary methods for BC prevention or risk reduction include lifestyle changes, surgery, and chemoprevention. Surgical intervention for BC prevention involves risk-reducing prophylactic mastectomy, typically performed either synchronously with the treatment of a primary tumor or as a bilateral procedure in high-risk women. Chemoprevention with endocrine therapy carries adherence-limiting toxicity.

Addressing Global Gaps in Mammography Screening for Improved Breast Cancer Detection: A Review of the Literature

Breast cancer is the second most common cancer globally, with 2.3 million new cases annually, constituting 11.6% of all cancer cases. It is also the fourth leading cause of cancer deaths, claiming 670,000 lives a year. This high incidence of breast cancer morbidity worldwide has increased the urgent need for standardized and adequate screening methods, including clinical breast examination, self-breast examination, and mammography screening tests for non-symptomatic individuals. Mammography is considered the gold standard for breast cancer screening, with early randomized control trials showing significant reductions in mortality rates in women aged 50 and over (International Agency for Research on Cancer and American College of Radiology). Despite this, discrepancies in mammography practices across different healthcare settings regarding adherence to international standards raise concerns. A comprehensive review of the vast literature looking at the practices and norms of mammography screening worldwide highlighted several domains that present limitations to screening. These include epidemiological data deficits, lack of educational training offered to radiographers and varied image quality indices, exposure technique, method of breast compression, dose calculation, reference levels, screening frequency intervals, and diverse distribution of resources, particularly in developing countries. These factors shed light on the substantial discrepancies in the implementation and efficacy of screening programs, underscoring the necessity for future research endeavors to collaborate in creating coherent, standardized, evidence-based guidelines. Addressing these issues can enhance the feasibility, sensitivity, and accessibility of screening programs, resulting in favorable impacts on the early diagnosis and survival of breast cancer on a global scale.

Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors

PURPOSE

We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort.

METHODS

This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs.

RESULTS

Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC.

CONCLUSION

CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

IMPLICATIONS

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

Implementation of risk assessment process for breast cancer risk in primary care

Background

Current cancer prevention guidelines recommend assessing breast cancer risk using validated risk calculators such as Tyrer-Cuzick and assessing genetic testing eligibility with NCCN. Women at high-risk of breast cancer may be recommended to undergo additional or earlier screening. Risk assessment is not consistently implemented in the primary care setting resulting in increased morbidity and mortality in unidentified high-risk individuals.

Methods

A single-arm interventional study was conducted in an academic primary care clinic for women 25-50 years old presenting for primary care appointments. Pre-visit workflows evaluated breast cancer risk using the Cancer Risk Assessment (CRA) Tool and information was provided to the clinician with guideline-based recommendations. Post-visit questionnaires and chart review were conducted.

Results

The survey response rate was 24.5% (144/587) with 80.3% of responses completed online (94/117). The average age of respondents was 35.8 years with 50.4% White and 35.9% Black. There were no differences in response rate based on race. Risk discussion was documented in the medical record in 15.4% of cases with a higher rate of documentation in high-risk patient based on risk assessment as compared with average risk respondents (34.6% vs. 9.7%, p<0.01). In the high-risk women identified 11.4% (4/35) were seen by the high-risk breast clinic, and 5.7% (2/35) were referred for genetic evaluation. None had previously obtained MRI screening or genetic testing.

Conclusions

There is limited identification and evaluation of women at high risk for breast cancer. Pre-visit surveys can be used as a tool to assess breast cancer risk in the primary care setting; however additional strategies are needed to implement systematic risk assessment and facilitate appropriate treatment based on risk level.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

The risk-based breast screening (RIBBS) study protocol: a personalized screening model for young women

The optimal mammography screening strategy for women aged 45-49 years is a matter of debate. We present the RIBBS study protocol, a quasi-experimental, prospective, population-based study comparing a risk- and breast density-stratified screening model (interventional cohort) with annual digital mammography (DM) screening (observational control cohort) in a real-world setting. The interventional cohort consists of 10,269 women aged 45 years enrolled between 2020 and 2021 from two provinces of the Veneto Region (northen Italy). At baseline, participants underwent two-view digital breast tomosynthesis (DBT) and completed the Tyrer-Cuzick risk prediction model. Volumetric breast density (VBD) was calculated from DBT and the lifetime risk (LTR) was estimated by including VBD among the risk factors. Based on VBD and LTR, women were classified into five subgroups with specific screening protocols for subsequent screening rounds: (1) LTR ≤ 17% and nondense breast: biennial DBT; (2) LTR ≤ 17% and dense breast: biennial DBT and ultrasound; (3) LTR 17-30% or LTR > 30% without family history of BC, and nondense breast: annual DBT; (4) LTR 17-30% or > 30% without family history of BC, and dense breast: annual DBT and ultrasound; and (5) LTR > 30% and family history of BC: annual DBT and breast MRI. The interventional cohort is still ongoing. An observational, nonequivalent control cohort of 43,000 women aged 45 years participating in an annual DM screening programme was recruited in three provinces of the neighbouring Emilia-Romagna Region. Cumulative incidence rates of advanced BC at three, five, and ten years between the two cohorts will be compared, adjusting for the incidence difference at baseline.Trial registration This study is registered on Clinicaltrials.gov (NCT05675085).

Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women

PURPOSE

Family history (FH) and pathogenic variants (PVs) are used for guiding risk surveillance in selected high-risk women but little is known about their impact for breast cancer screening on population level. In addition, polygenic risk scores (PRSs) have been shown to efficiently stratify breast cancer risk through combining information about common genetic factors into one measure.

METHODS

In longitudinal real-life data, we evaluate PRS, FH, and PVs for stratified screening. Using FinnGen (N = 117,252), linked to the Mass Screening Registry for breast cancer (1992-2019; nationwide organized biennial screening for age 50-69 years), we assessed the screening performance of a breast cancer PRS and compared its performance with FH of breast cancer and PVs in moderate- (CHEK2)- to high-risk (PALB2) susceptibility genes.

RESULTS

Effect sizes for FH, PVs, and high PRS (>90th percentile) were comparable in screening-aged women, with similar implications for shifting age at screening onset. A high PRS identified women more likely to be diagnosed with breast cancer after a positive screening finding (positive predictive value [PPV], 39.5% [95% CI, 37.6 to 41.5]). Combinations of risk factors increased the PPVs up to 45% to 50%. A high PRS conferred an elevated risk of interval breast cancer (hazard ratio [HR], 2.78 [95% CI, 2.00 to 3.86] at age 50 years; HR, 2.48 [95% CI, 1.67 to 3.70] at age 60 years), and women with a low PRS (<10th percentile) had a low risk for both interval- and screen-detected breast cancers.

CONCLUSION

Using real-life screening data, this study demonstrates the effectiveness of a breast cancer PRS for risk stratification, alone and combined with FH and PVs. Further research is required to evaluate their impact in a prospective risk-stratified screening program, including cost-effectiveness.

Women's Knowledge, Attitudes, and Perception on Personalized Risk-Stratified Breast Cancer Screening: A Cross-Sectional Study in Malaysia

AIM

Breast cancer is commonest cancer among Malaysian women and screening is essential for the early detection. Therefore our study aimed at measuring the levels of knowledge, attitude and perception towards personalized risk stratified breast cancer screening in Malaysia.

METHODS

A cross-sectional study was carried out in Malaysia to assess the knowledge, perception and attitudes of the women in Malaysia. The study was conducted using an online questionnaire, and samples were obtained using convenience sampling. The questionnaire was distributed trilingual in English, Bahasa Malaysia and Chinese. The data was collected with content validated questionnaire. Data was analyzed with descriptive statistics and General Linear Model analysis in SPSS (Version 27).

RESULTS

A total of 201 respondents' data were analyzed. From our study we were able to summarize that the women in Malaysia have a suboptimal knowledge towards personalized risk-stratified breast cancer screening as only 48.9% aware of the term for personalized risk-stratified breast cancer screening. Meanwhile, the majority of the respondents (96.7%) showed positive attitudes towards the importance of risk assessment and screening. Experience of participating in health education programmes about breast cancer and personalized risk-stratified screening was found to be significantly associated with knowledge, attitude and perception towards personalized risk-stratified breast cancer screening.

CONCLUSION

General population's awareness of individualized risk-stratified breast cancer screening was insufficient despite their favourable attitude towards the disease. A multimodal strategy may be used to improve women's knowledge, attitude, and perception of individualized risk-stratified breast cancer screening.

Breast cancer risk prediction using machine learning: a systematic review

Background

Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies.

Objective and methods

This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers.

Results

A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI.

Conclusion

This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.

Risk-stratified screening for the early detection of kidney cancer

Earlier detection and screening for kidney cancer has been identified as a key research priority, however the low prevalence of the disease in unselected populations limits the cost-effectiveness of screening. Risk-stratified screening for kidney cancer may improve early detection by targeting high-risk individuals whilst limiting harms in low-risk individuals, potentially increasing the cost-effectiveness of screening. A number of models have been identified which estimate kidney cancer risk based on both phenotypic and genetic data, and while several of the former have been shown to identify individuals at high-risk of developing kidney cancer with reasonable accuracy, current evidence does not support including a genetic component. Combined screening for lung cancer and kidney cancer has been proposed, as the two malignancies share some common risk factors. A modelling study estimated that using lung cancer risk models (currently used for risk-stratified lung cancer screening) could capture 25% of patients with kidney cancer, which is only slightly lower than using the best performing kidney cancer-specific risk models based on phenotypic data (27%-33%). Additionally, risk-stratified screening for kidney cancer has been shown to be acceptable to the public. The following review summarises existing evidence regarding risk-stratified screening for kidney cancer, highlighting the risks and benefits, as well as exploring the management of potential harms and further research needs.

Development and quality appraisal of a new English breast screening linked data set as part of the age, test threshold, and frequency of mammography screening (ATHENA-M) study

OBJECTIVES

To build a data set capturing the whole breast cancer screening journey from individual breast cancer screening records to outcomes and assess data quality.

METHODS

Routine screening records (invitation, attendance, test results) from all 79 English NHS breast screening centres between January 1, 1988 and March 31, 2018 were linked to cancer registry (cancer characteristics and treatment) and national mortality data. Data quality was assessed using comparability, validity, timeliness, and completeness.

RESULTS

Screening records were extracted from 76/79 English breast screening centres, 3/79 were not possible due to software issues. Data linkage was successful from 1997 after introduction of a universal identifier for women (NHS number). Prior to 1997 outcome data are incomplete due to linkage issues, reducing validity. Between January 1, 1997 and March 31, 2018, a total of 11 262 730 women were offered screening of whom 9 371 973 attended at least one appointment, with 139 million person-years of follow-up (a median of 12.4 person years for each woman included) with 73 810 breast cancer deaths and 1 111 139 any-cause deaths. Comparability to reference data sets and internal validity were demonstrated. Data completeness was high for core screening variables (>99%) and main cancer outcomes (>95%).

CONCLUSIONS

The ATHENA-M project has created a large high-quality and representative data set of individual women's screening trajectories and outcomes in England from 1997 to 2018, data before 1997 are lower quality.

ADVANCES IN KNOWLEDGE

This is the most complete data set of English breast screening records and outcomes constructed to date, which can be used to evaluate and optimize screening.

Rethinking screening mammography in Japan: next-generation breast cancer screening through breast awareness and supplemental ultrasonography

Breast cancer mortality has not been reduced in Japan despite more than 20 years of population-based screening mammography. Screening mammography might not be suitable for Japanese women who often have dense breasts, thus decreasing mammography sensitivity because of masking. The J-START study showed that breast ultrasonography increases the sensitivity and the detection rate for early invasive cancers and lowers the rate of interval cancers for Japanese women in their 40 s. Breast awareness and breast cancer survival are directly correlated; however, breast awareness is not widely known in Japan. Next-generation breast cancer screening in Japan should consist of breast awareness campaigns for improving breast cancer literacy and supplemental breast ultrasonography to address the problem of false-negative mammograms attributable to dense breasts.

Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence

GOALS

To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40-49, in a Central European population with BC incidence below EU average.

METHODS

502 women aged 40-49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18.

RESULTS

The AUC for PRS18 was 0.613 (95 % CI 0.570-0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included.

CONCLUSION

BC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC.

Risk of cancer versus risk of cancer diagnosis? Accounting for diagnostic bias in predictions of breast cancer risk by race and ethnicity

OBJECTIVES

Cancer risk prediction may be subject to detection bias if utilization of screening is related to cancer risk factors. We examine detection bias when predicting breast cancer risk by race/ethnicity.

METHODS

We used screening and diagnosis histories from the Breast Cancer Surveillance Consortium to estimate risk of breast cancer onset and calculated relative risk of onset and diagnosis for each racial/ethnic group compared with non-Hispanic White women.

RESULTS

Of 104,073 women aged 40-54 receiving their first screening mammogram at a Breast Cancer Surveillance Consortium facility between 2000 and 2018, 10.2% (n = 10,634) identified as Asian, 10.9% (n = 11,292) as Hispanic, and 8.4% (n = 8719) as non-Hispanic Black. Hispanic and non-Hispanic Black women had slightly lower screening frequencies but biopsy rates following a positive mammogram were similar across groups. Risk of cancer diagnosis was similar for non-Hispanic Black and White women (relative risk vs non-Hispanic White = 0.90, 95% CI 0.65 to 1.14) but was lower for Asian (relative risk = 0.70, 95% CI 0.56 to 0.97) and Hispanic women (relative risk = 0.82, 95% CI 0.62 to 1.08). Relative risks of disease onset were 0.78 (95% CI 0.68 to 0.88), 0.70 (95% CI 0.59 to 0.83), and 0.95 (95% CI 0.84 to 1.09) for Asian, Hispanic, and non-Hispanic Black women, respectively.

CONCLUSIONS

Racial/ethnic differences in mammography and biopsy utilization did not induce substantial detection bias; relative risks of disease onset were similar to or modestly different than relative risks of diagnosis. Asian and Hispanic women have lower risks of developing breast cancer than non-Hispanic Black and White women, who have similar risks.

A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population

Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.

External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population

Purpose

To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures.

Materials and Methods

A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.2 [SD]) enrolled in a hospital-based case-control study from 2006 to 2020 were retrospectively evaluated. Pathologically confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance index analyses.

Results

Mirai achieved 1- and 5-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (95% CI: 0.64, 0.67), respectively. One-year AUCs for nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUC, 0.73 vs 0.68; P = .34). For longer-term prediction (2-5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 vs 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all time points), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns.

Conclusion

A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that the model performance is likely driven by the detection of precancerous changes.Keywords: Breast, Cancer, Computer Applications, Convolutional Neural Network, Deep Learning Algorithms, Informatics, Epidemiology, Machine Learning, Mammography, Oncology, Radiomics Supplemental material is available for this article. © RSNA, 2023See also commentary by Kontos and Kalpathy-Cramer in this issue.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

A plea for more careful scholarship in reviewing evidence: the case of mammographic screening

Objectives

To identify issues of principle and practice giving rise to misunderstandings in reviewing evidence, to illustrate these by reference to the Nordic Cochrane Review (NCR) and its interpretation of two trials of mammographic screening, and to draw lessons for future reviewing of published results.

Methods

A narrative review of the publications of the Nordic Cochrane Review of mammographic screening (NCR), the Swedish Two-County Trial (S2C) and the Canadian National Breast Screening Study 1 and 2 (CNBSS-1 and CNBSS-2).

Results

The NCR concluded that the S2C was unreliable, despite the review's complaints being shown to be mistaken, by direct reference to the original primary publications of the S2C. Repeated concerns were expressed by others about potential subversion of randomisation in CNBSS-1 and CNBSS-2; however, the NCR continued to rely heavily on the results of these trials. Since 2022, however, eyewitness evidence of such subversion has been in the public domain.

Conclusions

An over-reliance on nominal satisfaction of checklists of criteria in systematic reviewing can lead to erroneous conclusions. This occurred in the case of the NCR, which concluded that mammographic screening was ineffective or minimally effective. Broader and more even-handed reviews of the evidence show that screening confers a substantial reduction in breast cancer mortality.

Advances in knowledge

Those carrying out systematic reviews should be aware of the dangers of over-reliance on checklists and guidelines. Readers of systematic reviews should be aware that a systematic review is just another study, with the capability that all studies have of coming to incorrect conclusions. When a review seems to overturn the current position, it is essential to revisit the publications of the primary research.

A Novel Fuzzy Relative-Position-Coding Transformer for Breast Cancer Diagnosis Using Ultrasonography

Breast cancer is a leading cause of death in women worldwide, and early detection is crucial for successful treatment. Computer-aided diagnosis (CAD) systems have been developed to assist doctors in identifying breast cancer on ultrasound images. In this paper, we propose a novel fuzzy relative-position-coding (FRPC) Transformer to classify breast ultrasound (BUS) images for breast cancer diagnosis. The proposed FRPC Transformer utilizes the self-attention mechanism of Transformer networks combined with fuzzy relative-position-coding to capture global and local features of the BUS images. The performance of the proposed method is evaluated on one benchmark dataset and compared with those obtained by existing Transformer approaches using various metrics. The experimental outcomes distinctly establish the superiority of the proposed method in achieving elevated levels of accuracy, sensitivity, specificity, and F1 score (all at 90.52%), as well as a heightened area under the receiver operating characteristic (ROC) curve (0.91), surpassing those attained by the original Transformer model (at 89.54%, 89.54%, 89.54%, and 0.89, respectively). Overall, the proposed FRPC Transformer is a promising approach for breast cancer diagnosis. It has potential applications in clinical practice and can contribute to the early detection of breast cancer.

Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study

BACKGROUND

Identifying female individuals at highest risk of developing life-threatening breast cancers could inform novel stratified early detection and prevention strategies to reduce breast cancer mortality, rather than only considering cancer incidence. We aimed to develop a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in female individuals without breast cancer at baseline.

METHODS

In this model development and validation study, we used an open cohort study from the QResearch primary care database, which was linked to secondary care and national cancer and mortality registers in England, UK. The data extracted were from female individuals aged 20-90 years without previous breast cancer or ductal carcinoma in situ who entered the cohort between Jan 1, 2000, and Dec 31, 2020. The primary outcome was breast cancer-related death, which was assessed in the full dataset. Cox proportional hazards, competing risks regression, XGBoost, and neural network modelling approaches were used to predict the risk of breast cancer death within 10 years using routinely collected health-care data. Death due to causes other than breast cancer was the competing risk. Internal-external validation was used to evaluate prognostic model performance (using Harrell's C, calibration slope, and calibration in the large), performance heterogeneity, and transportability. Internal-external validation involved dataset partitioning by time period and geographical region. Decision curve analysis was used to assess clinical utility.

FINDINGS

We identified data for 11 626 969 female individuals, with 70 095 574 person-years of follow-up. There were 142 712 (1·2%) diagnoses of breast cancer, 24 043 (0·2%) breast cancer-related deaths, and 696 106 (6·0%) deaths from other causes. Meta-analysis pooled estimates of Harrell's C were highest for the competing risks model (0·932, 95% CI 0·917-0·946). The competing risks model was well calibrated overall (slope 1·011, 95% CI 0·978-1·044), and across different ethnic groups. Decision curve analysis suggested favourable clinical utility across all age groups. The XGBoost and neural network models had variable performance across age and ethnic groups.

INTERPRETATION

A model that predicts the combined risk of developing and then dying from breast cancer at the population level could inform stratified screening or chemoprevention strategies. Further evaluation of the competing risks model should comprise effect and health economic assessment of model-informed strategies.

FUNDING

Cancer Research UK.

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.

Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe

BACKGROUND

Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups.

PURPOSE

To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC.

METHODS

A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots.

RESULTS

From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models.

CONCLUSIONS

Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.

A Diagnostic Model for Breast Lesions With Enlarged Enhancement Extent on Contrast-Enhanced Ultrasound Improves Malignancy Prediction

OBJECTIVE

The aim of the work described here was to develop a diagnostic model based on contrast-enhanced ultrasound (CEUS) features to improve performance in predicting the probability of malignancy for breast lesions with an enlarged enhancement extent on CEUS.

METHODS

In total, 299 consecutive patients who underwent CEUS examination and had confirmed pathological results were retrospectively enrolled. Among the 299 patients, an enlarged enhancement extent on CEUS was found in 142 patients. In this special cohort, we analyzed the association of malignant pathologic results with perfusion patterns emphatically by reclassifying the patterns.

RESULTS

A diagnostic model was developed and presented as a nomogram, assessed with discrimination and calibration. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of the conventional perfusion and modified perfusion patterns were 0.58 and 0.76 (p < 0.001), respectively. A diagnostic model was built and exhibited good discrimination with a C-index of 0.95 (95% confidence interval: 0.91-0.98), which was confirmed to be 0.93 via internal bootstrapping validation.

CONCLUSION

The nomogram based on CEUS features provides radiologists with a quantitative tool to predict the probability of malignancy in this special cohort of breast lesions.

A novel non-invasive exhaled breath biopsy for the diagnosis and screening of breast cancer

BACKGROUND

Early detection is critical for improving the survival of breast cancer (BC) patients. Exhaled breath testing as a non-invasive technique might help to improve BC detection. However, the breath test accuracy for BC diagnosis is unclear.

METHODS

This multi-center cohort study consecutively recruited 5047 women from four areas of China who underwent BC screening. Breath samples were collected through standardized breath collection procedures. Volatile organic compound (VOC) markers were identified from a high-throughput breathomics analysis by the high-pressure photon ionization-time-of-flight mass spectrometry (HPPI-TOFMS). Diagnostic models were constructed using the random forest algorithm in the discovery cohort and tested in three external validation cohorts.

RESULTS

A total of 465 (9.21%) participants were identified with BC. Ten optimal VOC markers were identified to distinguish the breath samples of BC patients from those of non-cancer women. A diagnostic model (BreathBC) consisting of 10 optimal VOC markers showed an area under the curve (AUC) of 0.87 in external validation cohorts. BreathBC-Plus, which combined 10 VOC markers with risk factors, achieved better performance (AUC = 0.94 in the external validation cohorts), superior to that of mammography and ultrasound. Overall, the BreathBC-Plus detection rates were 96.97% for ductal carcinoma in situ, 85.06%, 90.00%, 88.24%, and 100% for stages I, II, III, and IV BC, respectively, with a specificity of 87.70% in the external validation cohorts.

CONCLUSIONS

This is the largest study on breath tests to date. Considering the easy-to-perform procedure and high accuracy, these findings exemplify the potential applicability of breath tests in BC screening.

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study

BACKGROUND

International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the applicability of deep learning-based anomaly detection to identify anomalous changes in negative breast CE-MRI screens associated with future lesion emergence.

METHODS

In this prospective study, we trained a generative adversarial network on dynamic CE-MRI of 33 high-risk women who participated in a screening program but did not develop BC. We defined an anomaly score as the deviation of an observed CE-MRI scan from the model of normal breast tissue variability. We evaluated the anomaly score's association with future lesion emergence on the level of local image patches (104,531 normal patches, 455 patches of future lesion location) and entire CE-MRI exams (21 normal, 20 with future lesion). Associations were analyzed by receiver operating characteristic (ROC) curves on the patch level and logistic regression on the examination level.

RESULTS

The local anomaly score on image patches was a good predictor for future lesion emergence (area under the ROC curve 0.804). An exam-level summary score was significantly associated with the emergence of lesions at any location at a later time point (p = 0.045).

CONCLUSIONS

Breast cancer lesions are associated with anomalous appearance changes in breast CE-MRI occurring before the lesion emerges in high-risk women. These early image signatures are detectable and may be a basis for adjusting individual BC risk and personalized screening.

RELEVANCE STATEMENT

Anomalies in screening MRI preceding lesion emergence in women at high-risk of breast cancer may inform individualized screening and intervention strategies.

KEY POINTS

• Breast lesions are associated with preceding anomalies in CE-MRI of high-risk women. • Deep learning-based anomaly detection can help to adjust risk assessment for future lesions. • An appearance anomaly score may be used for adjusting screening interval times.

Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk

Importance

Although breast density is an established risk factor for breast cancer, longitudinal changes in breast density have not been extensively studied to determine whether this factor is associated with breast cancer risk.

Objective

To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer.

Design, Setting, and Participants

This nested case-control cohort study was sampled from the Joanne Knight Breast Health Cohort of 10 481 women free from cancer at entry and observed from November 3, 2008, to October 31, 2020, with routine screening mammograms every 1 to 2 years, providing a measure of breast density. Breast cancer screening was provided for a diverse population of women in the St Louis region. A total of 289 case patients with pathology-confirmed breast cancer were identified, and approximately 2 control participants were sampled for each case according to age at entry and year of enrollment, yielding 658 controls with a total number of 8710 craniocaudal-view mammograms for analysis.

Exposures

Exposures included screening mammograms with volumetric percentage of density, change in volumetric breast density over time, and breast biopsy pathology-confirmed cancer. Breast cancer risk factors were collected via questionnaire at enrollment.

Main Outcomes and Measures

Longitudinal changes over time in each woman's volumetric breast density by case and control status.

Results

The mean (SD) age of the 947 participants was 56.67 (8.71) years at entry; 141 were Black (14.9%), 763 were White (80.6%), 20 were of other race or ethnicity (2.1%), and 23 did not report this information (2.4%). The mean (SD) interval was 2.0 (1.5) years from last mammogram to date of subsequent breast cancer diagnosis (10th percentile, 1.0 year; 90th percentile, 3.9 years). Breast density decreased over time in both cases and controls. However, there was a significantly slower decrease in rate of decline in density in the breast that developed breast cancer compared with the decline in controls (estimate = 0.027; 95% CI, 0.001-0.053; P = .04).

Conclusions and Relevance

This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management.

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study

OBJECTIVE

To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches.

DESIGN

Population based cohort study.

SETTING

QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers.

PARTICIPANTS

141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020.

MAIN OUTCOME MEASURES

Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility.

RESULTS

During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches.

CONCLUSION

In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.

Associations of a Breast Cancer Polygenic Risk Score With Tumor Characteristics and Survival

PURPOSE

A polygenic risk score (PRS) consisting of 313 common genetic variants (PRS313) is associated with risk of breast cancer and contralateral breast cancer. This study aimed to evaluate the association of the PRS313 with clinicopathologic characteristics of, and survival following, breast cancer.

METHODS

Women with invasive breast cancer were included, 98,397 of European ancestry and 12,920 of Asian ancestry, from the Breast Cancer Association Consortium (BCAC), and 683 women from the European MINDACT trial. Associations between PRS313 and clinicopathologic characteristics, including the 70-gene signature for MINDACT, were evaluated using logistic regression analyses. Associations of PRS313 (continuous, per standard deviation) with overall survival (OS) and breast cancer-specific survival (BCSS) were evaluated with Cox regression, adjusted for clinicopathologic characteristics and treatment.

RESULTS

The PRS313 was associated with more favorable tumor characteristics. In BCAC, increasing PRS313 was associated with lower grade, hormone receptor-positive status, and smaller tumor size. In MINDACT, PRS313 was associated with a low risk 70-gene signature. In European women from BCAC, higher PRS313 was associated with better OS and BCSS: hazard ratio (HR) 0.96 (95% CI, 0.94 to 0.97) and 0.96 (95% CI, 0.94 to 0.98), but the association disappeared after adjustment for clinicopathologic characteristics (and treatment): OS HR, 1.01 (95% CI, 0.98 to 1.05) and BCSS HR, 1.02 (95% CI, 0.98 to 1.07). The results in MINDACT and Asian women from BCAC were consistent.

CONCLUSION

An increased PRS313 is associated with favorable tumor characteristics, but is not independently associated with prognosis. Thus, PRS313 has no role in the clinical management of primary breast cancer at the time of diagnosis. Nevertheless, breast cancer mortality rates will be higher for women with higher PRS313 as increasing PRS313 is associated with an increased risk of disease. This information is crucial for modeling effective stratified screening programs.

Risk-Stratified Breast Cancer Screening Incorporating a Polygenic Risk Score: A Survey of UK General Practitioners’ Knowledge and Attitudes

A polygenic risk score (PRS) quantifies the aggregated effects of common genetic variants in an individual. A ‘personalised breast cancer risk assessment’ combines PRS with other genetic and nongenetic risk factors to offer risk-stratified screening and interventions. Large-scale studies are evaluating the clinical utility and feasibility of implementing risk-stratified screening; however, General Practitioners’ (GPs) views remain largely unknown. This study aimed to explore GPs’: (i) knowledge of risk-stratified screening; (ii) attitudes towards risk-stratified screening; and (iii) preferences for continuing professional development. A cross-sectional online survey of UK GPs was conducted between July–August 2022. The survey was distributed by the Royal College of General Practitioners and via other mailing lists and social media. In total, 109 GPs completed the survey; 49% were not familiar with the concept of PRS. Regarding risk-stratified screening pathways, 75% agreed with earlier and more frequent screening for women at high risk, 43% neither agreed nor disagreed with later and less screening for women at lower-than-average risk, and 55% disagreed with completely removing screening for women at much lower risk. In total, 81% felt positive about the potential impact of risk-stratified screening towards patients and 62% felt positive about the potential impact on their practice. GPs selected training of healthcare professionals as the priority for future risk-stratified screening implementation, preferring online formats for learning. The results suggest limited knowledge of PRS and risk-stratified screening amongst GPs. Training—preferably using online learning formats—was identified as the top priority for future implementation. GPs felt positive about the potential impact of risk-stratified screening; however, there was hesitance and disagreement towards a low-risk screening pathway.

Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis

PURPOSE OF REVIEW

This article aims to provide an updated overview of the indications for diagnostic breast magnetic resonance imaging (MRI), discusses the available and novel imaging exams proposed for breast cancer detection, and discusses considerations when performing breast MRI in the clinical setting.

RECENT FINDINGS

Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.

Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study

With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.

Estimating the Cost of 3 Risk Prediction Strategies for Potential Use in the United Kingdom National Breast Screening Program

Background

Economic evaluations have suggested that risk-stratified breast cancer screening may be cost-effective but have used assumptions to estimate the cost of risk prediction. The aim of this study was to identify and quantify the resource use and associated costs required to introduce a breast cancer risk-stratification approach into the English national breast screening program.

Methods

A micro-costing study, conducted alongside a cohort-based prospective trial (BC-PREDICT), identified the resource use and cost per individual (£; 2021 price year) of providing a risk-stratification strategy at a woman's first mammography. Costs were calculated for 3 risk-stratification approaches: Tyrer-Cuzick survey, Tyrer-Cuzick with Volpara breast-density measurement, and Tyrer-Cuzick with Volpara breast-density measurement and testing for 142 single nucleotide polymorphisms (SNP). Costs were determined for the intervention as implemented in the trial and in the health service.

Results

The cost of providing the risk-stratification strategy was calculated to be £16.45 for the Tyrer-Cuzick survey approach, £21.82 for the Tyrer-Cuzick with Volpara breast-density measurement, and £102.22 for the Tyrer-Cuzick with Volpara breast-density measurement and SNP testing.

Limitations

This study did not use formal expert elicitation methods to synthesize estimates.

Conclusion

The costs of risk prediction using a survey and breast density measurement were low, but adding SNP testing substantially increases costs. Implementation issues present in the trial may also significantly increase the cost of risk prediction.

Implications

This is the first study to robustly estimate the cost of risk-stratification for breast cancer screening. The cost of risk prediction using questionnaires and automated breast density measurement was low, but full economic evaluations including accurate costs are required to provide evidence of the cost-effectiveness of risk-stratified breast cancer screening.

Highlights

Economic evaluations have suggested that risk-stratified breast cancer screening may be a cost-effective use of resources in the United Kingdom.Current estimates of the cost of risk stratification are based on pragmatic assumptions.This study provides estimates of the cost of risk stratification using 3 strategies and when these strategies are implemented perfectly and imperfectly in the health system.The cost of risk stratification is relatively low unless single nucleotide polymorphisms are included in the strategy.

What are the views of three key stakeholder groups on extending the breast screening interval for low-risk women? A secondary qualitative analysis

INTRODUCTION

There is increasing interest in risk-stratified breast screening, whereby the prevention and early detection offers vary by a woman's estimated risk of breast cancer. To date, more focus has been directed towards high-risk screening pathways rather than considering women at lower risk, who may be eligible for extended screening intervals. This secondary data analysis aimed to compare the views of three key stakeholder groups on how extending screening intervals for low-risk women should be implemented and communicated as part of a national breast screening programme.

METHODS

Secondary data analysis of three qualitative studies exploring the views of distinct stakeholder groups was conducted. Interviews took place with 23 low-risk women (identified from the BC-Predict study) and 17 national screening figures, who were involved in policy-making and implementation. In addition, three focus groups and two interviews were conducted with 26 healthcare professionals. A multiperspective thematic analysis was conducted to identify similarities and differences between stakeholders.

FINDINGS

Three themes were produced: Questionable assumptions about negative consequences, highlighting how other stakeholders lack trust in how women are likely to understand extended screening intervals; Preserving the integrity of the programme, centring on decision-making and maintaining a positive reputation of breast screening and Negotiating a communication pathway highlighting communication expectations and public campaign importance.

CONCLUSIONS

A risk-stratified screening programme should consider how best to engage women assessed as having a low risk of breast cancer to ensure mutual trust, balance the practicality of change whilst ensuring acceptability, and carefully develop multilevel inclusive communication strategies.

PATIENT AND PUBLIC CONTRIBUTION

The research within this paper involved patient/public contributors throughout including study design and materials input.