scholarly journals Polygenic Breast Cancer Risk for Women Veterans in the Million Veteran Program

2021 ◽  
pp. 1178-1191
Author(s):  
Jessica Minnier ◽  
Nallakkandi Rajeevan ◽  
Lina Gao ◽  
Byung Park ◽  
Saiju Pyarajan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prediction Models ◽  
Assessment Tool ◽  
Cancer Risk Assessment ◽  
Risk Models ◽  
Breast Cancer Risk Assessment ◽  
Incident Cases

PURPOSE Accurate breast cancer (BC) risk assessment allows personalized screening and prevention. Prospective validation of prediction models is required before clinical application. Here, we evaluate clinical- and genetic-based BC prediction models in a prospective cohort of women from the Million Veteran Program. MATERIALS AND METHODS Clinical BC risk prediction models were validated in combination with a genetic polygenic risk score of 313 (PRS313) single-nucleotide polymorphisms in genetic females without prior BC diagnosis (n = 35,130, mean age 49 years) with 30% non-Hispanic African ancestry (AA). Clinical risk models tested were Breast and Prostate Cancer Cohort Consortium, literature review, and Breast Cancer Risk Assessment Tool, and implemented with or without PRS313. Prediction accuracy and association with incident breast cancer was evaluated with area under the receiver operating characteristic curve (AUC), hazard ratios, and proportion with high absolute lifetime risk. RESULTS Three hundred thirty-eight participants developed incident breast cancers with a median follow-up of 3.9 years (2.5 cases/1,000 person-years), with 196 incident cases in women of European ancestry and 112 incident cases in AA women. Individualized Coherent Absolute Risk Estimator-literature review in combination with PRS313 had an AUC of 0.708 (95% CI, 0.659 to 0.758) in women with European or non-African ancestries and 0.625 (0.539 to 0.711) in AA women. Breast Cancer Risk Assessment Tool with PRS313 had an AUC of 0.695 (0.62 to 0.729) in European or non-AA and 0.675 (0.626 to 0.723) in AA women. Incorporation of PRS313 with clinical models improved prediction in European but not in AA women. Models estimated up to 9% of European and 18% of AA women with absolute lifetime risk > 20%. CONCLUSION Clinical and genetic BC risk models predict incident BC in a large prospective multiracial cohort; however, more work is needed to improve genetic risk estimation in AA women.

scholarly journals Using an Internet-Based Breast Cancer Risk Assessment Tool to Improve Social-Cognitive Precursors of Physical Activity

2017 ◽  
Vol 37 (6) ◽  
pp. 657-669 ◽  
Author(s):  
Stephanie L. Fowler ◽  
William M. P. Klein ◽  
Linda Ball ◽  
Jaclyn McGuire ◽  
Graham A. Colditz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Social Cognitive ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment

scholarly journals Model for Individualized Prediction of Breast Cancer Risk After a Benign Breast Biopsy

2015 ◽  
Vol 33 (8) ◽  
pp. 923-929 ◽  
Author(s):  
V. Shane Pankratz ◽  
Amy C. Degnim ◽  
Ryan D. Frank ◽  
Marlene H. Frost ◽  
Daniel W. Visscher ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Cancer Risk Assessment ◽  
Increased Risk ◽  
Breast Cancer Risk Assessment ◽  
Benign Biopsy

Purpose Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.

scholarly journals Performance of the Breast Cancer Risk Assessment Tool Among Women Age 75 Years and Older

2015 ◽  
Vol 108 (3) ◽  
pp. djv348 ◽  
Author(s):  
Mara A. Schonberg ◽  
Vicky W. Li ◽  
A. Heather Eliassen ◽  
Roger B. Davis ◽  
Andrea Z. LaCroix ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment

Breast cancer risk assessment and chemoprevention use among VA primary care.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 1543-1543
Author(s):  
Joseph Merriman ◽  
Balmatee Bidassie ◽  
Amanda Kovach ◽  
Marissa Vallette ◽  
Yeun-Hee Anna Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Primary Care ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Comfort Level ◽  
Cancer Risk Assessment ◽  
Health Providers ◽  
Risk Models ◽  
Breast Cancer Risk Assessment

1543 Background: Despite recommended guidelines and available medicationsto reduce breast cancer risk by up to 50-65%, <5% of the 10 million eligible women are offered chemoprevention in the U.S. The comfort level, practice patterns, and barriers to breast cancer risk assessment and chemoprevention use within the VA have not been reported. Methods: We assessed VA primary care providers using a REDcap survey. We obtained provider demographics, use and comfort level with breast cancer risk models and chemoprevention and knowledge about chemoprevention. Data was analyzed with Fishers exact or chi-square tests. Results: Of the 200 survey respondents, 167 were included for analysis. Overall, 30% used the Gail model monthly or more often, and 1.5 % prescribed chemoprevention in the last 2 years. Fewer than 30% correctly answered chemoprevention knowledge questions. Designated women's health providers were more comfortable with risk assessment and chemoprevention (p<.046, p<.004) and used risk models more often (p<.045). 63% expressed interest in education about breast cancer prevention. Conclusions: Breast cancer risk assessment and chemoprevention use by VA primary care is limited by lack of comfort and familiarity. Women's health providers are more comfortable and knowledgeable about breast cancer risk models and chemoprevention, offering an opportunity for partnership with high-risk oncologists to improve breast cancer risk assessment and chemoprevention use among female Veterans.[Table: see text]

scholarly journals Prospective Validation of the NCI Breast Cancer Risk Assessment Tool and the Autodensity Mammographic Density Tool on 40,000 Australian Screening Program Participants

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 49s-49s ◽  
Author(s):  
C. Nickson ◽  
P. Procopio ◽  
L. Devereux ◽  
S. Carr ◽  
G. Mann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mammographic Density ◽  
Assessment Tool ◽  
Screening Program ◽  
Interval Cancer ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment

Background: In Australia and elsewhere, there is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires reliable, feasible and accurate estimates of risk. The US National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) and the AutoDensity fully automated mammographic density measurement tool have each been shown to stratify women into groups according to their risk of breast cancer; the AutoDensity tool also provides information on the likely sensitivity and specificity of mammographic screening tests. The Australian 'lifepool' cohort of over 53,000 women recruited predominantly through BreastScreen Australia screening program offers an opportunity to validate these tools and examine how they can be combined to estimate various risks. Aim: To validate BCRAT and AutoDensity on a large Australian population, and examine how the tools can be combined to provide information on breast cancer risk and the accuracy of the screening test. Methods: We use lifepool cohort questionnaire data and linked screening records and mammograms, cancer registrations and death records to describe the association between BCRAT and AutoDensity scores assessed at the time of screening and future breast cancer diagnosis. We use hazards models to account for censoring and describe outcomes according to mode of detection (screen-detected, interval cancers or other). Our primary analysis is restricted to women in the historical screening target age range of 50-69 with no prevalent breast cancer diagnosis on entry to the lifepool cohort. Results: The primary analysis included approximately 40,000 women with a median follow-up period of 4.5 years (1.1-6.5 years). The BCRAT tool generated a median 5-year breast cancer risk score of 1.5% (range 0.6%-22.0%). Compared with women in the lowest quintile of this score, women in the highest quintile had a 2.3-fold risk (95% CI 1.7-3.0, P < 0.001) of incident invasive breast cancer. For the approximately 35,000 women with digital screening mammograms on enrolment, women in the highest quintile of AutoDensity values had a 1.5-fold risk (95% CI 1.1-2.0 P = 0.011) of incident invasive breast cancer and a 2.6-fold risk (95% CI 1.1-6.2, P = 0.034) of an interval cancer compared with women in the lowest quintile. With BRCAT and AutoDensity measurements weakly correlated (r2= 0.003, P = 0.05), we demonstrate various approaches to combining this information to stratify women according to breast cancer risk and risk of an interval cancer. Conclusion: The US National Cancer Institute Breast Cancer Risk Assessment Tool and the AutoDensity mammographic density tool can be used to stratify breast cancer screening participants into risk groups according to their future breast cancer risk and the risk of an interval cancer. This is likely to be of interest to screening program managers and policy-makers, and women considering screening participation.

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

2021 ◽  
Author(s):  
Geunwon Kim ◽  
Manisha Bahl
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Cancer Risk Assessment ◽  
Risk Prediction Models ◽  
Breast Cancer Risk Assessment

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

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