SynthesisBreast cancer research : BCR2018
Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.
Synthesis in Breast cancer research : BCR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 6 of them syntheses that pooled it.
What it found
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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
56 citing papers in PubMed, 6 syntheses or guidelines pooled it, 110 citations in OpenAlex.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Risk assessment tools for women with breast atypia: A systematic review.American journal of surgery · 2025Pooled it
- An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review.JMIR public health and surveillance · 2022Pooled it
- Global guidelines for breast cancer screening: A systematic review.Breast (Edinburgh, Scotland) · 2022Pooled it
- Clinical practice guidelines for risk assessment to identify women at high risk of breast cancer: Chinese Society of Breast Surgery (CSBrS) practice guidelines 2021.Chinese medical journal · 2021Guideline
- A systematic review and quality assessment of individualised breast cancer risk prediction models.British journal of cancer · 2019Pooled it
- Genetic and lifestyle factors for breast cancer risk assessment in Southeast China.Cancer medicine · 2023Trial
- CBCRisk-Mastectomy: A Risk Prediction Tool to Aid Contralateral Prophylactic Mastectomy Decision Making.medRxiv : the preprint server for health sciences · 2026Article
- Development of a risk prediction model for second primary thyroid cancer in female breast cancer patients based on machine learning algorithms.Scientific reports · 2026Article
- Development and External Validation of an Interpretable Machine Learning Model Using Routine Blood Biomarkers for Differentiating Malignant Breast Nodules.Journal of inflammation research · 2026Article
- Tyrer-Cuzick Lifetime Risk Is Not Associated With Non-BRCA1/2 Pathogenic Variants for Breast Carcinoma.The breast journal · 2026Article
- Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules.The breast journal · 2026Article
- Factors associated with receipt of surveillance breast MRI among racially and ethnically diverse women with operable breast cancer.Breast cancer research and treatment · 2025Article
- Validation of a blood test for multi-cancer risk stratification in a lung cancer screening cohort.medRxiv : the preprint server for health sciences · 2025Article
- Profile of the BREATHE cohort for risk-based breast screening in Singapore.Scientific reports · 2025Article
- Current insights into breast and ovarian cancer risk: a contemporary review.Minerva surgery · 2025Review
- Article
- Breast Cancer Screening and Prophylactic Mastectomy for High-Risk Women in Romania.Medicina (Kaunas, Lithuania) · 2024Review
- Analysis of Breast Cancer Screening Results and Influencing Factors of Breast Cancer in Guangdong Province from 2017 to 2021.Journal of epidemiology and global health · 2024Article
- Risk-Stratified Breast Cancer Screening in Malaysia: Challenges and Opportunities.Asian Pacific journal of cancer prevention : APJCP · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA).
methodsThree systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive.
resultsGail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively.
conclusionsThe Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses.
trial registrationPROSPERO CRD42016047215 .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.