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

Factors influencing U.S. women's interest and preferences for breast cancer risk communication: a cross-sectional study from a large tertiary care breast imaging center

BACKGROUND

Breast imaging clinics in the United States (U.S.) are increasingly implementing breast cancer risk assessment (BCRA) to align with evolving guideline recommendations but with limited uptake of risk-reduction care. Effectively communicating risk information to women is central to implementation efforts, but remains understudied in the U.S. This study aims to characterize, and identify factors associated with women's interest in and preferences for breast cancer risk communication.

METHODS

This is a cross-sectional survey study of U.S. women presenting for a mammogram between January and March of 2021 at a large, tertiary breast imaging clinic. Survey items assessed women's interest in knowing their risk and preferences for risk communication if considered to be at high risk in hypothetical situations. Multivariable logistic regression modeling assessed factors associated with women's interest in knowing their personal risk and preferences for details around exact risk estimates.

RESULTS

Among 1119 women, 72.7% were interested in knowing their breast cancer risk. If at high risk, 77% preferred to receive their exact risk estimate and preferred verbal (52.9% phone/47% in-person) vs. written (26.5% online/19.5% letter) communications. Adjusted regression analyses found that those with a primary family history of breast cancer were significantly more interested in knowing their risk (OR 1.5, 95% CI 1.0, 2.1, p = 0.04), while those categorized as "more than one race or other" were significantly less interested in knowing their risk (OR 0.4, 95% CI 0.2, 0.9, p = 0.02). Women 60 + years of age were significantly less likely to prefer exact estimates of their risk (OR 0.6, 95% CI 0.5, 0.98, p < 0.01), while women with greater than a high school education were significantly more likely to prefer exact risk estimates (OR 2.5, 95% CI 1.5, 4.2, p < 0.001).

CONCLUSION

U.S. women in this study expressed strong interest in knowing their risk and preferred to receive exact risk estimates verbally if found to be at high risk. Sociodemographic and family history influenced women's interest and preferences for risk communication. Breast imaging centers implementing risk assessment should consider strategies tailored to women's preferences to increase interest in risk estimates and improve risk communication.

Considering clonal hematopoiesis of indeterminate potential in space radiation risk analysis for hematologic cancers and cardiovascular disease

BACKGROUND

Expanding human presence in space through long-duration exploration missions and commercial space operations warrants improvements in approaches for quantifying crew space radiation health risks. Currently, risk assessment models for radiogenic cancer and cardiovascular disease consider age, sex, and tobacco use, but do not incorporate other modifiable (e.g., body weight, physical activity, diet, environment) and non-modifiable individual risk factors (e.g., genetics, medical history, race/ethnicity, family history) that may greatly influence crew health both in-mission and long-term. For example, clonal hematopoiesis of indeterminate potential (CHIP) is a relatively common age-related condition that is an emerging risk factor for a variety of diseases including cardiovascular disease and cancer. CHIP carrier status may therefore exacerbate health risks associated with space radiation exposure.

METHODS

In the present study, published CHIP hazard ratios were used to modify background hazard rates for coronary heart disease, stroke, and hematologic cancers in the National Aeronautics and Space Administration space radiation risk assessment model. The risk of radiation exposure-induced death for these endpoints was projected for a future Mars exploration mission scenario.

RESULTS

Here we show appreciable increases in the lifetime risk of exposure-induced death for hematologic malignancies, coronary heart disease, and stroke, which are observed as a function of age after radiation exposure for male and female crew members that are directly attributable to the elevated health risks for CHIP carriers.

CONCLUSIONS

We discuss the importance of evaluating individual risk factors such as CHIP as part of a comprehensive space radiation risk assessment strategy aimed at effective risk communication and disease surveillance for astronauts embarking on future exploration missions.

Predicting five-year interval second breast cancer risk in women with prior breast cancer

BACKGROUND

Annual surveillance mammography is recommended for women with a personal history of breast cancer. Risk prediction models that estimate mammography failures such as interval second breast cancers could help to tailor surveillance imaging regimens to women's individual risk profiles.

METHODS

In a cohort of women with a history of breast cancer receiving surveillance mammography in the Breast Cancer Surveillance Consortium in 1996-2019, we used Least Absolute Shrinkage and Selection Operator (LASSO)-penalized regression to estimate the probability of an interval second cancer (invasive cancer or ductal carcinoma in situ) in the 1 year after a negative surveillance mammogram. Based on predicted risks from this one-year risk model, we generated cumulative risks of an interval second cancer for the five-year period after each mammogram. Model performance was evaluated using cross-validation in the overall cohort and within race and ethnicity strata.

RESULTS

In 173 290 surveillance mammograms, we observed 496 interval cancers. One-year risk models were well-calibrated (expected/observed ratio = 1.00) with good accuracy (area under the receiver operating characteristic curve = 0.64). Model performance was similar across race and ethnicity groups. The median five-year cumulative risk was 1.20% (interquartile range 0.93%-1.63%). Median five-year risks were highest in women who were under age 40 or pre- or perimenopausal at diagnosis and those with estrogen receptor-negative primary breast cancers.

CONCLUSIONS

Our risk model identified women at high risk of interval second breast cancers who may benefit from additional surveillance imaging modalities. Risk models should be evaluated to determine if risk-guided supplemental surveillance imaging improves early detection and decreases surveillance failures.

Risk-Adapted Breast Screening for Women at Low Predicted Risk of Breast Cancer: An Online Discrete Choice Experiment

BACKGROUND

A risk-stratified breast screening program could offer low-risk women less screening than is currently offered by the National Health Service. The acceptability of this approach may be enhanced if it corresponds to UK women's screening preferences and values.

OBJECTIVES

To elicit and quantify preferences for low-risk screening options.

METHODS

Women aged 40 to 70 y with no history of breast cancer took part in an online discrete choice experiment. We generated 32 hypothetical low-risk screening programs defined by 5 attributes (start age, end age, screening interval, risk of dying from breast cancer, and risk of overdiagnosis), the levels of which were systematically varied between the programs. Respondents were presented with 8 choice sets and asked to choose between 2 screening alternatives or no screening. Preference data were analyzed using conditional logit regression models. The relative importance of attributes and the mean predicted probability of choosing each program were estimated.

RESULTS

Participants (N = 502) preferred all screening programs over no screening. An older starting age of screening, younger end age of screening, longer intervals between screening, and increased risk of dying had a negative impact on support for screening programs (P < 0.01). Although the risk of overdiagnosis was of low relative importance, a decreased risk of this harm had a small positive impact on screening choices. The mean predicted probabilities that risk-adapted screening programs would be supported relative to current guidelines were low (range, 0.18 to 0.52).

CONCLUSIONS

A deintensified screening pathway for women at low risk of breast cancer, especially one that recommends a later screening start age, would run counter to women's breast screening preferences. Further research is needed to enhance the acceptability of offering less screening to those at low risk of breast cancer.

HIGHLIGHTS

Risk-based breast screening may involve the deintensification of screening for women at low risk of breast cancer.Low-risk screening pathways run counter to women's screening preferences and values.Longer screening intervals may be preferable to a later start age.Work is needed to enhance the acceptability of a low-risk screening pathway.

Radial Scars on Screening Digital Breast Tomosynthesis: Upstaging Rates and Management Strategies

BACKGROUND. Radial scars are more commonly identified on digital breast tomosynthesis (DBT) than on digital mammography (DM). Nonetheless, universal guidelines for radial scar management in the current era of DBT are lacking. OBJECTIVE. The purpose of this study was to determine the upstaging rates of screening DBT-detected radial scars with and without atypia and to identify features related to upstaging risk. METHODS. This retrospective study included patients who underwent core needle biopsy (CNB) showing a radial scar after screening DBT and DM from January 1, 2013, to December 31, 2020. Patients without surgical excision or at least 2 years of imaging follow-up after CNB were excluded. Rates of upstaging to breast cancer (ductal carcinoma in situ [DCIS] or invasive disease) were compared between radial scars with and without atypia at CNB. Associations of upstaging with patient, imaging, and pathologic variables were explored using standard statistical tests. RESULTS. Of 165 women with 171 radial scars, the final study sample included 153 women (mean age, 56 years; range, 33-83 years) with 159 radial scars that underwent surgical excision (80.5%, 128/159) or at least 2 years of imaging follow-up (19.5%, 31/159). Seven radial scars were upstaged to DCIS and one to invasive disease. Therefore, the up-staging rate of radial scars to cancer was 5.0% (8/159). The upstaging rate of radial scars without atypia at CNB was 1.6% (2/129) and that of radial scars with atypia was 20.0% (6/30) (p < .001). On multivariable analysis, features associated with higher upstaging risk included a prior breast cancer diagnosis (62.5% vs 4.8%; p = .01) and the presence of atypia at CNB (75.0% vs 15.9%; p = .02). The upstaging rate according to mammographic finding type was 7.1% (1/14) for asymmetries, 12.5% (2/16) for masses, 5.3% (5/95) for architectural distortion, and 0.0% (0/34) for calcifications. CONCLUSION. Screening-detected radial scars without atypia at CNB have a low upstaging rate to breast cancer of 1.6%. CLINICAL IMPACT. Imaging surveillance rather than surgery is a reasonable approach for radial scars without atypia, particularly for those presenting as calcifications.

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

A contemporary review of breast cancer risk factors and the role of artificial intelligence

Background

Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics.

Methods

A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses.

Results

Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies.

Conclusion

The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.

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.

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

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

Early life body size and puberty markers as predictors of breast cancer risk later in life: A neural network analysis

BACKGROUND

The early life factors of birthweight, child weight, height, body mass index (BMI) and pubertal timing are associated with risks of breast cancer. However, the predictive value of these factors in relation to breast cancer is largely unknown. Therefore, using a machine learning approach, we examined whether birthweight, childhood weights, heights, BMIs, and pubertal timing individually and in combination were predictive of breast cancer.

METHODS

We used information on birthweight, childhood height and weight, and pubertal timing assessed by the onset of the growth spurt (OGS) from 164,216 girls born 1930-1996 from the Copenhagen School Health Records Register. Of these, 10,002 women were diagnosed with breast cancer during 1977-2019 according to a nationwide breast cancer database. We developed a feed-forward neural network, which was trained and tested on early life body size measures individually and in various combinations. Evaluation metrics were examined to identify the best performing model.

RESULTS

The highest area under the receiver operating curve (AUC) was achieved in a model that included birthweight, childhood heights, weights and age at OGS (AUC = 0.600). A model based on childhood heights and weights had a comparable AUC value (AUC = 0.598), whereas a model including only childhood heights had the lowest AUC value (AUC = 0.572). The sensitivity of the models ranged from 0.698 to 0.760 while the precision ranged from 0.071 to 0.076.

CONCLUSION

We found that the best performing network was based on birthweight, childhood weights, heights and age at OGS as the input features. Nonetheless, this performance was only slightly better than the model including childhood heights and weights. Further, although the performance of our networks was relatively low, it was similar to those from previous studies including well-established risk factors. As such, our results suggest that childhood body size may add additional value to breast cancer prediction models.

Risk Prediction Models for Oral Cancer: A Systematic Review

In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (n = 20), alcohol consumption (n = 18) and smoking (n = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (n = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.

Multi-Instance Classification of Breast Tumor Ultrasound Images Using Convolutional Neural Networks and Transfer Learning

BACKGROUND

Breast cancer is arguably one of the leading causes of death among women around the world. The automation of the early detection process and classification of breast masses has been a prominent focus for researchers in the past decade. The utilization of ultrasound imaging is prevalent in the diagnostic evaluation of breast cancer, with its predictive accuracy being dependent on the expertise of the specialist. Therefore, there is an urgent need to create fast and reliable ultrasound image detection algorithms to address this issue.

METHODS

This paper aims to compare the efficiency of six state-of-the-art, fine-tuned deep learning models that can classify breast tissue from ultrasound images into three classes: benign, malignant, and normal, using transfer learning. Additionally, the architecture of a custom model is introduced and trained from the ground up on a public dataset containing 780 images, which was further augmented to 3900 and 7800 images, respectively. What is more, the custom model is further validated on another private dataset containing 163 ultrasound images divided into two classes: benign and malignant. The pre-trained architectures used in this work are ResNet-50, Inception-V3, Inception-ResNet-V2, MobileNet-V2, VGG-16, and DenseNet-121. The performance evaluation metrics that are used in this study are as follows: Precision, Recall, F1-Score and Specificity.

RESULTS

The experimental results show that the models trained on the augmented dataset with 7800 images obtained the best performance on the test set, having 94.95 ± 0.64%, 97.69 ± 0.52%, 97.69 ± 0.13%, 97.77 ± 0.29%, 95.07 ± 0.41%, 98.11 ± 0.10%, and 96.75 ± 0.26% accuracy for the ResNet-50, MobileNet-V2, InceptionResNet-V2, VGG-16, Inception-V3, DenseNet-121, and our model, respectively.

CONCLUSION

Our proposed model obtains competitive results, outperforming some state-of-the-art models in terms of accuracy and training time.

Implementing the National Dense Breast Reporting Standard, Expanding Supplemental Screening Using Current Guidelines, and the Proposed Find It Early Act

Thirty-eight states and the District of Columbia (DC) have dense breast notification laws that mandate varying levels of patient notification about breast density after a mammogram, and these cover over 90% of American women. On March 10, 2023, the Food and Drug Administration issued a final rule amending regulations under the Mammography Quality Standards Act for a national dense breast reporting standard for both patient results letters and mammogram reports. Effective September 10, 2024, letters will be required to tell a woman her breasts are "dense" or "not dense," that dense tissue makes it harder to find cancers on a mammogram, and that it increases the risk of developing cancer. Women with dense breasts will also be told that other imaging tests in addition to a mammogram may help find cancers. The specific density category can be added (eg, if mandated by a state "inform" law). Reports to providers must include the Breast Imaging Reporting and Data System density category. Implementing appropriate supplemental screening should be based on patient risk for missed breast cancer on mammography; such assessment should include consideration of breast density and other risk factors. This article discusses strategies for implementation. Currently 21 states and DC have varying insurance laws for supplemental breast imaging; in addition, Oklahoma requires coverage for diagnostic breast imaging. A federal insurance bill, the Find It Early Act, has been introduced that would ensure no-cost screening and diagnostic imaging for women with dense breasts or at increased risk and close loopholes in state laws.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

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.

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment model to that of traditional breast cancer risk models in patients who underwent supplemental screening with MRI. Materials and Methods This retrospective study included consecutive patients undergoing breast cancer screening MRI from September 2017 to September 2020 at four facilities. Risk was assessed using the Tyrer-Cuzick (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models as well as a DL 5-year model that generated a risk score based on the most recent screening mammogram. A risk score of 1.67% or higher defined increased risk for traditional 5-year models, a risk score of 20% or higher defined high risk for traditional lifetime models, and absolute scores of 2.3 or higher and 6.6 or higher defined increased and high risk, respectively, for the DL model. Model accuracy metrics including cancer detection rate (CDR) and positive predictive values (PPVs) (PPV of abnormal findings at screening [PPV1], PPV of biopsies recommended [PPV2], and PPV of biopsies performed [PPV3]) were compared using logistic regression models. Results This study included 2168 women who underwent 4247 high-risk screening MRI examinations (median age, 54 years [IQR, 48-60 years]). CDR (per 1000 examinations) was higher in patients at high risk according to the DL model (20.6 [95% CI: 11.8, 35.6]) than according to the TC (6.0 [95% CI: 2.9, 12.3]; P < .01) and BCRAT (6.8 [95% CI: 2.9, 15.8]; P = .04) lifetime models. PPV1, PPV2, and PPV3 were higher in patients identified as high risk by the DL model (PPV1, 14.6%; PPV2, 32.4%; PPV3, 36.4%) than those identified as high risk with the TC (PPV1, 5.0%; PPV2, 12.7%; PPV3, 13.5%; P value range, .02-.03) and BCRAT (PPV1, 5.5%; PPV2, 11.1%; PPV3, 12.5%; P value range, .02-.05) lifetime models. Conclusion Patients identified as high risk by a mammogram-based DL risk assessment model showed higher CDR at breast screening MRI than patients identified as high risk with traditional risk models. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer

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

A Step-by-Step Guide to Writing a Scientific Review Article

Scientific review articles are comprehensive, focused reviews of the scientific literature written by subject matter experts. The task of writing a scientific review article can seem overwhelming; however, it can be managed by using an organized approach and devoting sufficient time to the process. The process involves selecting a topic about which the authors are knowledgeable and enthusiastic, conducting a literature search and critical analysis of the literature, and writing the article, which is composed of an abstract, introduction, body, and conclusion, with accompanying tables and figures. This article, which focuses on the narrative or traditional literature review, is intended to serve as a guide with practical steps for new writers. Tips for success are also discussed, including selecting a focused topic, maintaining objectivity and balance while writing, avoiding tedious data presentation in a laundry list format, moving from descriptions of the literature to critical analysis, avoiding simplistic conclusions, and budgeting time for the overall process.

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.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Goodness-of-fit two-phase sampling designs for time-to-event outcomes: a simulation study based on New York University Women's Health Study for breast cancer

BACKGROUND

Sub-cohort sampling designs such as a case-cohort study play a key role in studying biomarker-disease associations due to their cost effectiveness. Time-to-event outcome is often the focus in cohort studies, and the research goal is to assess the association between the event risk and risk factors. In this paper, we propose a novel goodness-of-fit two-phase sampling design for time-to-event outcomes when some covariates (e.g., biomarkers) can only be measured on a subgroup of study subjects.

METHODS

Assuming that an external model, which can be the well-established risk models such as the Gail model for breast cancer, Gleason score for prostate cancer, and Framingham risk models for heart diseases, or built from preliminary data, is available to relate the outcome and complete covariates, we propose to oversample subjects with worse goodness-of-fit (GOF) based on an external survival model and time-to-event. With the cases and controls sampled using the GOF two-phase design, the inverse sampling probability weighting method is used to estimate the log hazard ratio of both incomplete and complete covariates. We conducted extensive simulations to evaluate the efficiency gain of our proposed GOF two-phase sampling designs over case-cohort study designs.

RESULTS

Through extensive simulations based on a dataset from the New York University Women's Health Study, we showed that the proposed GOF two-phase sampling designs were unbiased and generally had higher efficiency compared to the standard case-cohort study designs.

CONCLUSION

In cohort studies with rare outcomes, an important design question is how to select informative subjects to reduce sampling costs while maintaining statistical efficiency. Our proposed goodness-of-fit two-phase design provides efficient alternatives to standard case-cohort designs for assessing the association between time-to-event outcome and risk factors. This method is conveniently implemented in standard software.

A Review of the Literature for Individualizing Women's Care Through Breast Cancer Risk Assessment

Breast cancer is well recognized as a leading type of cancer affecting women in the United States. Although breast cancer screening is well supported in the literature, there is a lack of clear agreement regarding which breast cancer risk calculating tools should be used to develop personalized screening regimens. In this review of 11 primary articles published from 2017 to 2022, we assess current evidence on breast cancer risk assessment in outpatient clinic and mammography settings and the pivotal role of health care providers in influencing patients' choices regarding individualized screenings. Risk assessment is strongly recommended by multiple clinical practice guidelines, yet there is inadequate evidence to endorse one risk assessment tool as best practice. Further research is needed to integrate risk assessment within the clinic workflow and screening encounters. Patient-centered communication and shared decision-making are critical components for managing each woman's perceived risk and objective risk for breast cancer.

Clinical applicability of the Polygenic Risk Score for breast cancer risk prediction in familial cases

BACKGROUND

Common low-risk variants are presently not used to guide clinical management of familial breast cancer (BC). We explored the additive impact of a 313-variant-based Polygenic Risk Score (PRS₃₁₃) relative to standard gene testing in non-BRCA1/2 Dutch BC families.

METHODS

We included 3918 BC cases from 3492 Dutch non-BRCA1/2 BC families and 3474 Dutch population controls. The association of the standardised PRS₃₁₃ with BC was estimated using a logistic regression model, adjusted for pedigree-based family history. Family history of the controls was imputed for this analysis. SEs were corrected to account for relatedness of individuals. Using the BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) V.5 model, lifetime risks were retrospectively calculated with and without individual PRS₃₁₃. For 2586 cases and 2584 controls, the carrier status of pathogenic variants (PVs) in ATM, CHEK2 and PALB2 was known.

RESULTS

The family history-adjusted PRS₃₁₃ was significantly associated with BC (per SD OR=1.97, 95% CI 1.84 to 2.11). Including the PRS₃₁₃ in BOADICEA family-based risk prediction would have changed screening recommendations in up to 27%, 36% and 34% of cases according to BC screening guidelines from the USA, UK and the Netherlands (National Comprehensive Cancer Network, National Institute for Health and Care Excellence, and Netherlands Comprehensive Cancer Organisation), respectively. For the population controls, without information on family history, this was up to 39%, 44% and 58%, respectively. Among carriers of PVs in known moderate BC susceptibility genes, the PRS₃₁₃ had the largest impact for CHEK2 and ATM.

CONCLUSIONS

Our results support the application of the PRS₃₁₃ in risk prediction for genetically uninformative BC families and families with a PV in moderate BC risk genes.

Public Preferences for Determining Eligibility for Screening in Risk-Stratified Cancer Screening Programs: A Discrete Choice Experiment

BACKGROUND

Risk stratification has been proposed to improve the efficiency of population-level cancer screening. We aimed to describe and quantify the relative importance of different attributes of potential screening programs among the public, focusing on stratifying eligibility.

METHODS

We conducted a discrete choice experiment in which respondents selected between 2 hypothetical screening programs in a series of 9 questions. We presented the risk factors used to determine eligibility (age, sex, or lifestyle or genetic risk scores) and anticipated outcomes based on eligibility criteria with different sensitivity and specificity levels. We performed conditional logit regression models and used the results to estimate preferences for different approaches. We also analyzed free-text comments on respondents' views on the programs.

RESULTS

A total of 1,172 respondents completed the survey. Sensitivity was the most important attribute (7 and 11 times more important than specificity and risk factors, respectively). Eligibility criteria based on age and sex or genetics were preferred over age alone and lifestyle risk scores. Phenotypic and polygenic risk prediction models would be more acceptable than screening everyone aged 55 to 70 y if they had high discrimination (area under the receiver-operating characteristic curve ≥0.75 and 0.80, respectively).

LIMITATIONS

Although our sample was representative with respect to age, sex, and ethnicity, it may not be representative of the UK population regarding other important characteristics. Also, some respondents may have not understood all the information provided to inform decision making.

CONCLUSIONS

The public prioritized lives saved from cancer over reductions in numbers screened or experiencing unnecessary follow-up. Incorporating personal-level risk factors into screening eligibility criteria is acceptable to the public if it increases sensitivity; therefore, maximizing sensitivity in model development and communication could increase uptake.

HIGHLIGHTS

The public prioritized lives saved when considering changing from age-based eligibility criteria to risk-stratified cancer screening over reductions in numbers of people being screened or experiencing unnecessary follow-up.The risk stratification strategy used to do this was the least important component, although age plus sex or genetics were relatively preferable to using age alone and lifestyle risk scores.Communication strategies that emphasize improvements in the numbers of cancers detected or not missed across the population are more likely to be salient than reductions in unnecessary investigations or follow-up among some groups.Future research should focus on developing implementation strategies that maximize gains in sensitivity within the context of resource constraints and how to present attributes relating to specificity to facilitate understanding and informed decision making.

Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.

Effect of an Educational Intervention on Women's Health Care Provider Knowledge Gaps About Breast Cancer Risk Model Use and High-risk Screening Recommendations

OBJECTIVE

To assess effectiveness of a web-based educational intervention on women's health care provider knowledge of breast cancer risk models and high-risk screening recommendations.

METHODS

A web-based pre- and post-test study including 177 U.S.-based women's health care providers was conducted in 2019. Knowledge gaps were defined as fewer than 75% of respondents answering correctly. Pre- and post-test knowledge differences (McNemar test) and associations of baseline characteristics with pre-test knowledge gaps (logistic regression) were evaluated.

RESULTS

Respondents included 131/177 (74.0%) physicians; 127/177 (71.8%) practiced obstetrics/gynecology. Pre-test, 118/177 (66.7%) knew the Gail model predicts lifetime invasive breast cancer risk; this knowledge gap persisted post-test [(121/177, 68.4%); P = 0.77]. Just 39.0% (69/177) knew the Gail model identifies women eligible for risk-reducing medications; this knowledge gap resolved. Only 48.6% (86/177) knew the Gail model should not be used to identify women meeting high-risk MRI screening guidelines; this deficiency decreased to 66.1% (117/177) post-test (P = 0.001). Pre-test, 47.5% (84/177) knew the Tyrer-Cuzick model is used to identify women meeting high-risk screening MRI criteria, 42.9% (76/177) to predict BRCA1/2 pathogenic mutation risk, and 26.0% (46/177) to predict lifetime invasive breast cancer risk. These knowledge gaps persisted but improved. For a high-risk 30-year-old, 67.8% (120/177) and 54.2% (96/177) pre-test knew screening MRI and mammography/tomosynthesis are recommended, respectively; 19.2% (34/177) knew both are recommended; and 53% (94/177) knew US is not recommended. These knowledge gaps resolved or reduced.

CONCLUSION

Web-based education can reduce important provider knowledge gaps about breast cancer risk models and high-risk screening recommendations.

Association and performance of polygenic risk scores for breast cancer among French women presenting or not a familial predisposition to the disease

BACKGROUND

Three partially overlapping breast cancer polygenic risk scores (PRS) comprising 77, 179 and 313 SNPs have been proposed for European-ancestry women by the Breast Cancer Association Consortium (BCAC) for improving risk prediction in the general population. However, the effect of these SNPs may vary from one country to another and within a country because of other factors.

OBJECTIVE

To assess their associated risk and predictive performance in French women from (1) the CECILE population-based case-control study, (2) BRCA1 or BRCA2 (BRCA1/2) pathogenic variant (PV) carriers from the GEMO study, and (3) familial breast cancer cases with no BRCA1/2 PV and unrelated controls from the GENESIS study.

RESULTS

All three PRS were associated with breast cancer in all studies, with odds ratios per standard deviation varying from 1.7 to 2.0 in CECILE and GENESIS, and hazard ratios varying from 1.1 to 1.4 in GEMO. The predictive performance of PRS₃₁₃ in CECILE was similar to that reported in BCAC but lower than that in GENESIS (area under the receiver operating characteristic curve (AUC) = 0.67 and 0.75, respectively). PRS were less performant in BRCA2 and BRCA1 PV carriers (AUC = 0.58 and 0.54 respectively).

CONCLUSION

Our results are in line with previous validation studies in the general population and in BRCA1/2 PV carriers. Additionally, we showed that PRS may be of clinical utility for women with a strong family history of breast cancer and no BRCA1/2 PV, and for those carrying a predicted PV in a moderate-risk gene like ATM, CHEK2 or PALB2.

Mammography biomarkers of cardiovascular and musculoskeletal health: A review

Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.

An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review

BACKGROUND

Several studies have explored the predictive performance of machine learning-based breast cancer risk prediction models and have shown controversial conclusions. Thus, the performance of the current machine learning-based breast cancer risk prediction models and their benefits and weakness need to be evaluated for the future development of feasible and efficient risk prediction models.

OBJECTIVE

The aim of this review was to assess the performance and the clinical feasibility of the currently available machine learning-based breast cancer risk prediction models.

METHODS

We searched for papers published until June 9, 2021, on machine learning-based breast cancer risk prediction models in PubMed, Embase, and Web of Science. Studies describing the development or validation models for predicting future breast cancer risk were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and the clinical applicability of the included studies. The pooled area under the curve (AUC) was calculated using the DerSimonian and Laird random-effects model.

RESULTS

A total of 8 studies with 10 data sets were included. Neural network was the most common machine learning method for the development of breast cancer risk prediction models. The pooled AUC of the machine learning-based optimal risk prediction model reported in each study was 0.73 (95% CI 0.66-0.80; approximate 95% prediction interval 0.56-0.96), with a high level of heterogeneity between studies (Q=576.07, I²=98.44%; P<.001). The results of head-to-head comparison of the performance difference between the 2 types of models trained by the same data set showed that machine learning models had a slightly higher advantage than traditional risk factor-based models in predicting future breast cancer risk. The pooled AUC of the neural network-based risk prediction model was higher than that of the nonneural network-based optimal risk prediction model (0.71 vs 0.68, respectively). Subgroup analysis showed that the incorporation of imaging features in risk models resulted in a higher pooled AUC than the nonincorporation of imaging features in risk models (0.73 vs 0.61; P_{heterogeneity}=.001, respectively). The PROBAST analysis indicated that many machine learning models had high risk of bias and poorly reported calibration analysis.

CONCLUSIONS

Our review shows that the current machine learning-based breast cancer risk prediction models have some technical pitfalls and that their clinical feasibility and reliability are unsatisfactory.

Personalized breast cancer onset prediction from lifestyle and health history information

We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta's Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual's Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach-(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model-produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes-suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime.

Burden, trends, and risk factors for breast cancer in China from 1990 to 2019 and its predictions until 2034: an up-to-date overview and comparison with those in Japan and South Korea

BACKGROUND

The difference in epidemiological characteristics of breast cancer (BC) across countries is valuable for BC management and prevention. The study evaluated the up-to-date burden, trends, and risk factors of BC in China, Japan and South Korea during 1990-2019 and predicted the BC burden until 2034.

METHODS

Data on incident cases, deaths, disability-adjusted life-years (DALYs) and age-standardized rate (ASR) of BC were extracted from the Global Burden of Disease Study 2019. Trend analysis and prediction until 2034 were conducted by estimated annual percentage change and a Bayesian age-period-cohort model, respectively. Besides, the attributable burden to BC risk factors was also estimated.

RESULTS

In 2019, the number of BC incident cases, deaths and DALYs in China were 375,484, 96,306 and 2,957,453, respectively. The ASR of incidence increased, while that of death and DALYs decreased for Chinese females and Japanese and South Korean males during 1990-2019. High body-mass-index (BMI) was the largest contributor to Chinese female BC deaths and DALYs, while alcohol use was the greatest risk factor for Japanese and South Korean as well as Chinese males. The incident cases and deaths were expected to continue increase during 2020-2034 (except for Japanese female incident cases).

CONCLUSIONS

China had the greatest burden of BC among the three countries. Incident cases and deaths of BC were projected to increase over the next 15 years in China, particularly among Chinese males. Effective prevention and management strategies are urgently necessary for BC control in China.

Breast Cancer Epidemiology and Contemporary Breast Cancer Care: A Review of the Literature and Clinical Applications

Substantial progress has been made in contemporary breast cancer care, resulting in a consistently declining breast cancer mortality rate and an improvement in quality of life. Advancements include deescalation of therapy in low-risk populations and refining systemic therapy options. Research into molecular biomarkers continues to evolve and holds the promise of achieving the goal of precision medicine, while guidelines for supportive care and survivorship have been created to address the needs of an ever-increasing number of breast cancer survivors. A collaborative, multidisciplinary team approach is essential for patients and survivors to achieve optimal outcomes and enjoy productive high-quality lives. Gynecologists, in particular, play a key role in screening and survivorship care.

Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention

Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.

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

BACKGROUND

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

BREAst screening Tailored for HEr (BREATHE)-A study protocol on personalised risk-based breast cancer screening programme

Routine mammography screening is currently the standard tool for finding cancers at an early stage, when treatment is most successful. Current breast screening programmes are one-size-fits-all which all women above a certain age threshold are encouraged to participate. However, breast cancer risk varies by individual. The BREAst screening Tailored for HEr (BREATHE) study aims to assess acceptability of a comprehensive risk-based personalised breast screening in Singapore. Advancing beyond the current age-based screening paradigm, BREATHE integrates both genetic and non-genetic breast cancer risk prediction tools to personalise screening recommendations. BREATHE is a cohort study targeting to recruit ~3,500 women. The first recruitment visit will include questionnaires and a buccal cheek swab. After receiving a tailored breast cancer risk report, participants will attend an in-person risk review, followed by a final session assessing the acceptability of our risk stratification programme. Risk prediction is based on: a) Gail model (non-genetic), b) mammographic density and recall, c) BOADICEA predictions (breast cancer predisposition genes), and d) breast cancer polygenic risk score. For national implementation of personalised risk-based breast screening, exploration of the acceptability within the target populace is critical, in addition to validated predication tools. To our knowledge, this is the first study to implement a comprehensive risk-based mammography screening programme in Asia. The BREATHE study will provide essential data for policy implementation which will transform the health system to deliver a better health and healthcare outcomes.

Artificial Intelligence (AI) for Screening Mammography, From the AI Special Series on AI Applications

Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment, and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.

Advances and Future Directions in Molecular Breast Imaging

Molecular breast imaging (MBI) using ^99mTc-sestamibi has advanced rapidly over the past decade. Technical advances allow lower-dose, higher-resolution imaging and biopsy capability. MBI can be used for supplemental breast cancer screening with mammography for women with dense breasts, as well as to assess neoadjuvant therapy response, evaluate disease extent, and predict breast cancer risk. This article highlights the current state of the art and future directions in MBI.