ArticleBreast cancer research : BCR2012
Validation of the Gail model for predicting individual breast cancer risk in a prospective nationwide study of 28,104 Singapore women.
Article in Breast cancer research : BCR, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
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31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Association between CASC16 rs4784227 polymorphism and breast cancer susceptibility: A meta-analysis.Medicine · 2021Pooled it
- Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.Breast cancer research : BCR · 2018Pooled it
- Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data.Cancer reports (Hoboken, N.J.) · 2025Article
- Article
- The Impact of a Breast Cancer Risk Assessment on the Decision for Gender-Affirming Chest Masculinization Surgery in Transgender and Gender-Diverse Individuals: A Pilot Single-Arm Educational Intervention Trial.Annals of surgical oncology · 2024Observational
- What do women want to see in a personalized breast cancer risk report? A qualitative study of Asian women of two countries.Journal of community genetics · 2024Article
- Predicting five-year interval second breast cancer risk in women with prior breast cancer.Journal of the National Cancer Institute · 2024Article
- Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population?Cancers · 2023Article
- Breast Cancer Risk Assessment Tools for Stratifying Women into Risk Groups: A Systematic Review.Cancers · 2023Review
- Factors Associated with Screening Mammogram Uptake among Women Attending an Urban University Primary Care Clinic in Malaysia.International journal of environmental research and public health · 2022Article
- Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification.BMC medicine · 2022Article
- Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore.Cancer medicine · 2021Article
- Development and evaluation of the screening performance of a low-cost high-risk screening strategy for breast cancer.Cancer biology & medicine · 2021Article
- Accuracy of self-perceived risk perception of breast cancer development in Iranian women.BMC women's health · 2021Article
- Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women.Iranian journal of public health · 2020Article
- Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review.Heliyon · 2020Review
- Gail Model Underestimates Breast Cancer Risk in Thai Population.Asian Pacific journal of cancer prevention : APJCP · 2019Article
- Off-label use of tamoxifen in a Chinese tertiary care hospital.International journal of clinical pharmacy · 2019Article
- Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study.Cancer management and research · 2019Article
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Authors and funding
10 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe Gail model (GM) is a risk-assessment model used in individual estimation of the absolute risk of invasive breast cancer, and has been applied to both clinical counselling and breast cancer prevention studies. Although the GM has been validated in several Western studies, its applicability outside North America and Europe remains uncertain. The Singapore Breast Cancer Screening Project (SBCSP) is a nation-wide prospective trial of screening mammography conducted between Oct 1994 and Feb 1997, and is the only such trial conducted outside North America and Europe to date. With the long-term outcomes from this study, we sought to evaluate the performance of GM in prediction of individual breast cancer risk in a Asian developed country.
methodsThe study population consisted of 28,104 women aged 50 to 64 years who participated in the SBSCP and did not have breast cancer detected during screening. The national cancer registry was used to identify incident cases of breast cancer. To evaluate the performance of the GM, we compared the expected number of invasive breast cancer cases predicted by the model to the actual number of cases observed within 5-year and 10-year follow-up. Pearson's Chi-square test was used to test the goodness of fit between the expected and observed cases of invasive breast cancers.
resultsThe ratio of expected to observed number of invasive breast cancer cases within 5 years from screening was 2.51 (95% confidence interval 2.14 - 2.96). The GM over-estimated breast cancer risk across all age groups, with the discrepancy being highest among older women aged 60 - 64 years (E/O = 3.53, 95% CI = 2.57-4.85). The model also over-estimated risk for the upper 80% of women with highest predicted risk. The overall E/O ratio for the 10-year predicted breast cancer risk was 1.85 (1.68-2.04).
conclusionsThe GM over-predicts the risk of invasive breast cancer in the setting of a developed Asian country as demonstrated in a large prospective trial, with the largest difference seen in older women aged between 60 and 64 years old. The reason for the discrepancy is likely to be multifactorial, including a truly lower prevalence of breast cancer, as well as lower mammographic screening prevalence locally.
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