4.5 Review

Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review

期刊

HELIYON
卷 6, 期 4, 页码 -

出版社

CELL PRESS
DOI: 10.1016/j.heliyon.2020.e03794

关键词

Cancer research; Health sciences; Public health; Epidemiology; Women's health; Breast cancer risk; Gail model; Systematic review

资金

  1. Kemenristek Dikti (Ministry of Research, Technology and Higher Education of Republic Indonesia) [PD-016/SKPP.TJ/LPPM UAD/III/2019]

向作者/读者索取更多资源

Introduction: Currently, the Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model (GM) has been widely recognized and adapted for to study disparity in racial and ethnic groups in America including Asian and Pacific Islander American females. However, its applicability outside America remains uncertain due to diversity in epidemiology and risk factors of breast cancer in populations especially in Asian females. We sought to evaluate the performance of the GM to predict breast cancer risk in Asian countries. Material and methods: This study identified articles published from 2010 by searching PubMed, MEDLINE, Scopus, Web of Science, Google Scholar and gray literature. The initial search terms were breast cancer, mammary, carcinoma, tumor, neoplasm, risk assessment tool, BCRAT, breast cancer prediction, Gail model, Asia, and Asian. Results: The search yielded 20 articles, with 7 articles addressing the AUC and/or the expected (E) to observed (O) ratio of predicted breast cancer risk, representing the accuracy of the GM in the Asian population. One publication reported the sensitivity and specificity but no AUC. None of the studies were accepted as the standard for reporting prognostic models. Several studies reported good prognostic testing and likely developed a new model modifying the items in the instrument. Conclusion: The results are not strong enough to develop breast cancer risk in the setting of Asian countries. Involving the breast cancer risk of the Asian population in developing a prognostic model with good statistical understanding is particularly important and can reduce flawed or biased models. Identifying the best methods to achieve well-suited prognostic models in the Asian population should be a priority.

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