ReviewHeliyon2020
Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review.
Review in Heliyon, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Data preparation method for machine learning-based breast cancer risk prediction: A Cuban case study.MethodsX · 2025Article
- Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers.BMC cancer · 2025Article
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Genetic Factors in the Screening and Imaging for Breast Cancer.Korean journal of radiology · 2023Review
- Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population?Cancers · 2023Article
- Combining Breast Cancer Risk Prediction Models.Cancers · 2023Article
- Identify Breast Cancer Risk Factors Using the Gail Assessment Model in Iraq.Archives of Razi Institute · 2022Article
- Data disaggregation: the case of Asian and Pacific Islander data and the role of health sciences librarians.Journal of the Medical Library Association : JMLA · 2022Article
- Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore.Cancer medicine · 2021Article
- Awareness of Breast Cancer Screening among the Medical and General Population of the North Region of Cameroon.International journal of breast cancer · 2021Article
- Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study.Journal of Cancer · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCurrently, 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
methodsThis 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
resultsThe 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.
conclusionThe 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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