scholarly journals Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 45
Author(s):  
Anne Marie McCarthy ◽  
Yi Liu ◽  
Sarah Ehsan ◽  
Zoe Guan ◽  
Jane Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prediction Models ◽  
Screening Mammography ◽  
Significant Difference ◽  
Breast Cancer Risk Assessment ◽  
History Of

(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.

Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme

2013 ◽  
Vol 13 (2) ◽  
pp. 189-196 ◽  
Author(s):  
D. Gareth R. Evans ◽  
Sarah Ingham ◽  
Sarah Dawe ◽  
L. Roberts ◽  
F. Lalloo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Screening Programme ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment

scholarly journals Assessing Breast Cancer Risk Estimates Based on the Gail Model and Its Predictors in Qatari Women

2017 ◽  
Vol 8 (3) ◽  
pp. 180-187 ◽  
Author(s):  
Abdulbari Bener ◽  
Funda Çatan ◽  
Hanadi R. El Ayoubi ◽  
Ahmet Acar ◽  
Wanis H. Ibrahim
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Arab Women ◽  
Gail Model ◽  
Breast Cancer Risk Assessment

Background: The Gail model is the most widely used breast cancer risk assessment tool. An accurate assessment of individual’s breast cancer risk is very important for prevention of the disease and for the health care providers to make decision on taking chemoprevention for high-risk women in clinical practice in Qatar. Aim: To assess the breast cancer risk among Arab women population in Qatar using the Gail model and provide a global comparison of risk assessment. Subjects and Methods: In this cross-sectional study of 1488 women (aged 35 years and older), we used the Gail Risk Assessment Tool to assess the risk of developing breast cancer. Sociodemographic features such as age, lifestyle habits, body mass index, breast-feeding duration, consanguinity among parents, and family history of breast cancer were considered as possible risks. Results: The mean age of the study population was 47.8 ± 10.8 years. Qatari women and Arab women constituted 64.7% and 35.3% of the study population, respectively. The mean 5-year and lifetime breast cancer risks were 1.12 ± 0.52 and 10.57 ± 3.1, respectively. Consanguineous marriage among parents was seen in 30.6% of participants. We found a relationship between the 5-year and lifetime risks of breast cancer and variables such as age, age at menarche, gravidity, parity, body mass index, family history of cancer, menopause age, occupation, and level of education. The linear regression analysis identified the predictors for breast cancer in women such as age, age at menarche, age of first birth, family history and age of menopausal were considered the strong predictors and significant contributing risk factors for breast cancer after adjusting for ethnicity, parity and other variables. Conclusion: The current study is the first to evaluate the performance of the Gail model for Arab women population in the Gulf Cooperation Council. Gail model is an appropriate breast cancer risk assessment tool for female population in Qatar.

Clinical Assessment of Breast Cancer Risk Based on Family History

2010 ◽  
Vol 8 (10) ◽  
pp. 1148-1155 ◽  
Author(s):  
Kaylene Ready ◽  
Banu Arun
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Clinical Practice ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Reduction ◽  
Assessment Model ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment

Family history is a key component of breast cancer risk assessment. Family history provides clues as to the likelihood of a hereditary breast cancer syndrome and the need for a cancer genetics referral and can be used in the setting of a breast cancer risk assessment model to estimate a woman's risk. Appropriate breast cancer screening and risk reduction management plans rely on an accurate assessment of a patient's family history. This article reviews the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Breast Cancer Risk Reduction and provides insight into the application of the guidelines in clinical practice.

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.

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.

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