Breast Cancer Risk Prediction Research Articles

A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography

Breast cancer is among the most prevalent cancers in the female population globally. Therefore, screening campaigns as well as approaches to identify patients at risk are particularly important for the early detection of suspect lesions. This study aims to propose a workflow for the automatic classification of patients based on one of the most relevant risk factors in breast cancer, which is represented by breast density. The proposed classification methodology takes advantage of the features automatically extracted from mammographic images, as digital mammography represents the major screening tool in women. Textural features were extracted from the breast parenchyma through a radiomics approach, and they were used to train different machine learning algorithms and neural network models to classify the breast density according to the standard Breast Imaging Reporting and Data System (BI-RADS) guidelines. Both binary and multiclass tasks have been carried out and compared in terms of performance metrics. Preliminary results show interesting classification accuracy (93.55% for the binary task and 82.14% for the multiclass task), which are promising compared to the current literature. As the proposed workflow relies on straightforward and computationally efficient algorithms, it could serve as a basis for a fast-track protocol for the screening of mammograms to reduce the radiologists’ workload.

Machine Learning Assessment of Background Parenchymal Enhancement in Breast Cancer and Clinical Applications: A Literature Review.

Background Parenchymal Enhancement (BPE) on breast MRI holds promise as an imaging biomarker for breast cancer risk and prognosis. The ability to identify those at greatest risk can inform clinical decisions, promoting early diagnosis and potentially guiding strategies for prevention such as risk-reduction interventions with the use of selective estrogen receptor modulators and aromatase inhibitors. Currently, the standard method of assessing BPE is based on the Breast Imaging-Reporting and Data System (BI-RADS), which involves a radiologist's qualitative categorization of BPE as minimal, mild, moderate, or marked on contrast-enhanced MRI. This approach can be subjective and prone to inter/intra-observer variability, and compromises accuracy and reproducibility. In addition, this approach limits qualitative assessment to 4 categories. More recently developed methods using machine learning/artificial intelligence (ML/AI) techniques have the potential to quantify BPE more accurately and objectively. This paper will review the current machine learning/AI methods to determine BPE, and the clinical applications of BPE as an imaging biomarker for breast cancer risk prediction and prognosis.

Clinical recommendations for polygenic risk score-enhanced breast cancer screening: Can.Heal project

Abstract Background The application of polygenic risk score (PRS) in breast cancer (BC) screening presents promising opportunities. Developing recommendations for future use and research on this topic is a key focus of the EU4Health project: Building the EU cancer and public health genomics platform (Can.Heal). We aim to provide these recommendations based on the analysis of available evidence through a transparent and rigorous development process. Methods The recommendations adopt the GRADE evidence to decision methodology, leveraging an evidence review team and a multidisciplinary panel of nine experts. A systematic review is being conducted to evaluate the evidence for PRS in BC screening, in the domains of benefits and harms, acceptability, feasibility, equity and cost-effectiveness. Results Regarding benefits and harms of adding PRS to BC standard screening, we identified 63 relevant articles. Forty-five (71%) discussed benefits, while 14 (22%) addressed harms. Forty-two (67%) were observational studies, 18 (28%) modeling studies and 1 non-randomised control trial (2%) that examined the diagnostic accuracy of PRS-enhanced screening for relative BC risk prediction using measures such as net reclassification index and area under the curve. Lastly, two modeling studies (3%) assessed the clinical utility of PRS-enhanced screening in terms of life years gained, BC deaths averted. The other domains are under examination. The panel convened to redefine the research question and outcomes of interest and will reconvene to assess the certainty of evidence collected, and subsequently to draft recommendations. Conclusions The integration of PRS into BC screening shows potential benefits in improving risk prediction. Ongoing trials, such as Wisdom and MyPEBS, are studying the clinical utility of integrating PRS in BC screening. The approach taken by the Can.Heal aims to ensure that the recommendations are based on a thorough and balanced evaluation of the available evidence. Key messages • The Can.Heal project aims to formulate policy and research recommendations for breast cancer screening using PRS through a thorough evidence review. • Upcoming PRS recommendations rely on expert opinion and modeling studies.

Identification of sequence polymorphism in the D-loop region of mitochondrial DNA as a risk factor for breast cancer.

Mitochondrial DNA (mtDNA) variations affect the efficiency of the electron transport chain and production of reactive oxygen species, contributing to carcinogenesis. The D-loop region of mtDNA has emerged as a variation hotspot region in human neoplasia; however, the potential contribution of these variations in breast cancer risk prediction remains unknown. We investigated the relationship between germline single nucleotide polymorphisms (SNPs) in the entire D-loop region and breast cancer risk in Chinese women. Peripheral blood-isolated mtDNA from 2329 patients with breast cancer and 2328 cancer-free controls was examined for SNPs. In the combined cohort, we used traditional risk factors, susceptibility germline polymorphisms, and logistic regression analysis to evaluate the predictive value of susceptibility variants for breast cancer risk. We calculated the area under the receiver operating characteristic curve (AUC) as a measure. We also measured the content of 8-hydroxy-2'-deoxyguanosine (8-OHdG). Individual polymorphisms SNP573 were significantly associated with breast cancer risk in both the discovery and validation cohorts. In the combined cohort, the AUC of the traditional risk factors was 64.3%; after adding susceptibility variants, the AUC was 64.9% (DeLong test, p = 0.007). 8-OHdG levels were significantly higher in patients with breast cancer than in controls and higher in individuals with SNP573 than in those negative for this variation. Overall, oxidative stress might be associated with the risk of breast cancer, and SNP573 might be associated with oxidative stress. Our results indicate the risk potential of polymorphisms in the D-loop region in breast cancer in Southern China.

Validation of the Mirai model for predicting breast cancer risk in Mexican women

ObjectivesTo validate the performance of Mirai, a mammography-based deep learning model, in predicting breast cancer risk over a 1–5-year period in Mexican women.MethodsThis retrospective single-center study included mammograms in Mexican women who underwent screening mammography between January 2014 and December 2016. For women with consecutive mammograms during the study period, only the initial mammogram was included. Pathology and imaging follow-up served as the reference standard. Model performance in the entire dataset was evaluated, including the concordance index (C-Index) and area under the receiver operating characteristic curve (AUC). Mirai’s performance in terms of AUC was also evaluated between mammography systems (Hologic versus IMS). Clinical utility was evaluated by determining a cutoff point for Mirai’s continuous risk index based on identifying the top 10% of patients in the high-risk category.ResultsOf 3110 patients (median age 52.6 years ± 8.9), throughout the 5-year follow-up period, 3034 patients remained cancer-free, while 76 patients developed breast cancer. Mirai achieved a C-index of 0.63 (95% CI: 0.6–0.7) for the entire dataset. Mirai achieved a higher mean C-index in the Hologic subgroup (0.63 [95% CI: 0.5–0.7]) versus the IMS subgroup (0.55 [95% CI: 0.4–0.7]). With a Mirai index score > 0.029 (10% threshold) to identify high-risk individuals, the study revealed that individuals in the high-risk group had nearly three times the risk of developing breast cancer compared to those in the low-risk group.ConclusionsMirai has a moderate performance in predicting future breast cancer among Mexican women.Critical relevance statementProspective efforts should refine and apply the Mirai model, especially to minority populations and women aged between 30 and 40 years who are currently not targeted for routine screening.Key PointsThe applicability of AI models to non-White, minority populations remains understudied.The Mirai model is linked to future cancer events in Mexican women.Further research is needed to enhance model performance and establish usage guidelines.Graphical

Are women's breast cancer risk appraisals in line with updated clinical risk estimates communicated? Results from a UK Family History Risk and Prevention Clinic.

The incorporation of breast density and a polygenic risk score (PRS) into breast cancer risk prediction models can alter previously communicated risk estimates. Previous research finds that risk communication does not usually change personal risk appraisals. This study aimed to examine how women from the Family History Risk (FH-Risk) study appraise their breast cancer risk following communication of an updated risk estimate. In the FH-Risk study 323 women attended a consultation to receive an updated breast cancer risk estimate. A subset (n=190) completed a questionnaire, assessing their subjective breast cancer risk appraisals, satisfaction with the information provided and cancer related worry. One hundred and three were notified of a decrease risk, 34 an increase and 53 an unchanged risk. Women's subjective risk appraisals were in line with the updated risk estimates provided, with age, a PRS and breast density explaining most of the variance in these appraisals. Those notified of an increased risk demonstrated higher subjective risk perceptions compared to those whose risk remained unchanged or decreased. Women's subjective breast cancer risk appraisals are amenable to change following updated risk feedback, with new information breast density and a PRS accepted and integrated into existing risk appraisals. Trust in the service, the analogies and visual communication strategies used may have positively influenced the integration of this new information. Further research is warranted to assess whether similar patterns emerge for other illnesses and in different clinical contexts to determine the best strategies for communicating updated risk estimates.

Post-operative breast imaging: a management dilemma. Can mammographic artificial intelligence help?

BackgroundImaging of the postoperative breast is a challenging issue for the interpreting physician with many variable findings that may require additional assessment through targeted ultrasound, more mammography views, or other investigations. Artificial intelligence (AI) is a fast-developing field with various applications in the breast imaging including the detection and classification of lesions, the prediction of therapy response, and the prediction of breast cancer risk. This study aimed to identify whether Artificial Intelligence improves the mammographic detection and diagnosis of breast post-operative changes and hence improves follow-up and diagnostic workflow and reduces the need for additional exposure to extra radiation or contrast material doses as in Contrast Enhanced Mammography, and the need for interventional procedures as biopsy.MethodsThis cross-sectional analytic study included 66 female patients following breast-conserving surgeries coming with breast complaints or for follow-up, with mammographically diagnosed changes.ResultsMammography had a sensitivity of 91.7%, a specificity of 94.4%, a positive predictive value (PPV) of 78.6%, a negative predictive value (NPV) of 98.1%, and an accuracy of 93.9%, while the AI method indices were sensitivity 91.7%, specificity 92.6%, (PPV) 73.3%, (NPV) 98%, and accuracy 92.4%. The calculated cut-off point for the quantitative AI (probability of malignancy “POM” score) was 51.5%. There was a statistically significantly higher average in the percentage of POM in malignant cases (76.5 ± 27.3%) compared to benign cases (27.1 ± 19.7%). However, the final indices for the combined use of mammography and (AI) were sensitivity 100%, specificity 88.9%, (PPV) 66.7%, (NPV) 100%, and accuracy 90.9%.ConclusionApplying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers.

Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?

Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?

Development of a Breast Cancer Risk Prediction Model Integrating Monogenic, Polygenic, and Epidemiologic Risk.

Breast cancer has been associated with monogenic, polygenic, and epidemiologic (clinical, reproductive, and lifestyle) risk factors, but studies evaluating the combined effects of these factors have been limited. We extended previous work in breast cancer risk modeling, incorporating pathogenic variants (PV) in six breast cancer predisposition genes and a 105-SNP polygenic risk score (PRS), to include an epidemiologic risk score (ERS) in a sample of non-Hispanic White women drawn from prospective cohorts and population-based case-control studies, with 23,518 cases and 22,832 controls, from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium. The model predicts 4.4-fold higher risk of breast cancer for postmenopausal women with no predisposition PV and median PRS, but with the highest versus lowest ERS. Overall, women with CHEK2 PVs had >20% lifetime risk of breast cancer. However, 15.6% of women with CHEK2 PVs and a family history of breast cancer, and 45.1% of women with CHEK2 PVs but without a family history of breast cancer, had low (<20%) predicted lifetime risk and thus were below the threshold for MRI screening. CHEK2 PV carriers at the 10th percentile of the joint distribution of ERS and PRS, without a family history of breast cancer, had a predicted lifetime risk similar to the general population. These results illustrate that an ERS, alone and combined with the PRS, can contribute to clinically relevant risk stratification. Integrating monogenic, polygenic, and epidemiologic risk factors in breast cancer risk prediction models may inform personalized screening and prevention efforts.

1558P Independent validation of the Breast Cancer Risk Assessment Tool (Gail model) for predicting breast cancer risk in Egyptian population

1558P Independent validation of the Breast Cancer Risk Assessment Tool (Gail model) for predicting breast cancer risk in Egyptian population

Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.

With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree-based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree-based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI-enabled health systems and medical devices and, ultimately, the quality of care and outcomes.

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.

Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191 488 risk-negative examinations, training (n = 89 285), validation (n = 2106), and test (n = 39 351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities, or across age groups. Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, United Kingdom-specific dataset. Keywords: Deep Learning, Artificial Intelligence, Breast Cancer, Screening, Risk Prediction Supplemental material is available for this article. ©RSNA, 2024.

Artificial intelligence on breast cancer risk prediction

Breast Cancer is currently the most commonly diagnosed type of cancer worldwide. While the impacts of this disease can be mitigated through early diagnosis, generalized screening programs based on full-field digital mammography present several limitations regarding lesion obscurity and false positive diagnosis. Given that, women could benefit from a risk analysis for the development of BC that would allow their healthcare professionals to adapt screening in a personalized fashion, not only in terms of frequency but also regarding the imaging modality used. This study aims to develop a medical application, based on Artificial Intelligence (AI), that receives images from different modalities as input and outputs a personalized risk prediction for BC development. The final goal is to have an AI model that allocates each analyzed case to a risk group (1/2-year risk, 3/4-year risk, 5/more-year risk) based on characteristics present in the medical images. A solution like the one proposed would allow not only the previously mentioned screening adaptation but also some preventive measures taken both by the healthcare professional and by the patient. Finally, the development of a computerized medical application allows its use in any type of medical facility, despite the socio-economical characteristics of the patients.

Mammographic Texture versus Conventional Cumulus Measure of Density in Breast Cancer Risk Prediction: A Literature Review.

Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, there is a lack of comprehensive evidence to determine the relative strengths as risk predictor of textures and density and the reliability of texture-based measures. We searched the PubMed database for research published up to November 2023, which assessed breast cancer risk associations [odds ratios (OR)] with texture-based measures and percent mammographic density (PMD), and their discrimination [area under the receiver operating characteristics curve (AUC)], using same datasets. Of 11 publications, for textures, six found stronger associations (P < 0.05) with 11% to 508% increases on the log scale by study, and four found weaker associations (P < 0.05) with 14% to 100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications found greater AUCs for textures than PMD (P < 0.05); increases were 0.04 to 0.25 by study. Discrimination from PMD and these textures jointly was significantly higher than from PMD alone (P < 0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Some textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field.

Predicting breast cancer risk in women of African ancestry

Predicting breast cancer risk in women of African ancestry

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers

BackgroundNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA...

Functional partial least squares with censored outcomes: Prediction of breast cancer risk with mammogram images

Functional partial least squares with censored outcomes: Prediction of breast cancer risk with mammogram images

Improving a polygenic risk score (PRS) for breast cancer (BC) risk assessment in diverse ancestries.

10533 Background: Accurate BC risk assessment is essential to identify women for whom screening and preventive interventions may be lifesaving. Incorporation of PRS into clinical models can improve risk prediction, but most PRS have shown suboptimal performance among non-Europeans. We previously described a multiple-ancestry PRS (MA-149) based on 56 ancestry-informative and 93 BC-associated single-nucleotide polymorphisms (SNPs). MA-149 achieved accuracy for diverse populations by characterizing genetic ancestry at each SNP in terms of fractions attributable to reference ancestries (African, East Asian, European) and applying ancestry-specific risks and frequencies. MA-149 significantly outperformed the Tyrer-Cuzick (TC) model, and integration of MA-149 with TC improved predictive accuracy by roughly two-fold over TC alone. Here, we aimed to improve MA-149 by expanding the set of BC SNPs and refining ancestry-specific risks. Methods: We developed a novel stepwise regression methodology accounting for linkage disequilibrium to select an optimal set of BC SNPs. Women referred for hereditary cancer testing and negative for pathogenic variants in BC genes were divided into consecutive cohorts to (1) refine ancestry-specific SNP risks ( N=58,191 Black/African; N=27,160 East Asian), (2) develop the PRS ( N=184,322), and (3) conduct independent validation ( N=146,110). Predictive accuracy and calibration of the new PRS were evaluated in the full cohort and subpopulations of different ancestries. Odds ratios (OR) from multivariable regression are reported per standard deviation. Results: A set of 385 SNPs (56 ancestry, 329 BC) was selected for the new PRS (MA-385). MA-385 improved upon clinical factors and outperformed MA-149 within each ancestry (Table). Among non-Europeans, MA-385 was a better BC risk predictor (OR=1.47, 95% CI: 1.42-1.52) than MA-149 (OR=1.40, 95% CI: 1.35-1.45). The strongest associations were observed in Ashkenazi Jewish and Hispanic women (Table). MA-385 identified more women at &gt;2-fold increased risk than MA-149 (6.5 % vs 2%). Goodness-of-fit tests showed that MA-385 was well-calibrated while a 385-SNP PRS with European weights was miscalibrated for non-Europeans. Conclusions: MA-385 was well-calibrated, improved upon clinical factors, and outperformed existing PRS in all tested ancestries. Incorporation of MA-385 into risk assessment could improve the early detection and prevention of BC. [Table: see text]

Association of polygenic-based breast cancer risk prediction with patient management.

10527 Background: Guidelines recommend individuals with ≥20% lifetime risk of breast cancer (BC) undergo enhanced management, including annual screening mammography (SM) as early as age 30, annual breast MRI, and genetic counseling (GC). Lifetime BC risk can be estimated using a validated risk model, such as Tyrer-Cuzick (TC). A risk predictor that combines TC with a polygenic risk score (“combined risk score” or CRS) significantly improves risk prediction over TC alone. Little is known about how clinicians manage patients based on CRS. Here, we describe management following receipt of CRS results. Methods: De-identified administrative claims data from the Optum Labs Data Warehouse were linked with de-identified TC and CRS results originally provided to ordering clinicians. Patients were divided into four subgroups based on their lifetime risk predicted by CRS and by TC alone (high risk: ≥20%, average risk: &lt;20%): A (CRS-high/TC-high), B (CRS-high/TC-avg), C (CRS-avg/TC-high), and D (CRS-avg/TC-avg). Patient management claims were compared 360 days before and after the CRS test date and differences evaluated with McNemar’s test. Differences in post-test management were analyzed using multivariable logistic regression with adjustment for clinical factors. Results: Before receiving risk results, 11.9%, 7.7%, 8.8%, and 5.0% of patients in A, B, C, and D received a SM before age 40, respectively; 3.4%, 2.6%, 2.3%, and 0.9% received an MRI, respectively; and 4.3%, 4.7%, 6.0%, and 3.6% received GC, respectively. After receiving risk results, SM in those under age 40 significantly increased 1.6-2.2-fold in A, B, and C; MRI significantly increased 4.7-5.6-fold in A, B, and C; and GC significantly increased 1.9-2.3-fold in A and B. SM, MRI, and GC did not increase in D. Prophylactic mastectomy occurred in only 0.17% of all patients after receiving results. After adjustment for confounders, those in A, B, and C were 3.8-5.2 times more likely to undergo SM; 11.6-23.1 times more likely to undergo MRI; and 2.0-2.9 times more likely to undergo GC, compared to those in Group D (all p&lt;0.001). Conclusions: Patients with a ≥20% lifetime risk for BC were more likely to undergo enhanced management compared to those with a lifetime risk &lt;20%, regardless of whether their risk was based on the CRS or on TC. These results suggest that clinicians recommended management aligned with guidelines for those with ≥20% lifetime risk, even when such risk was predicted by the CRS. [Table: see text]