Evidence map›Paper›PMID 22289271›Full record

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.

Wen Yee Chay, Whee Sze Ong, Puay Hoon Tan, Nicholas Qi Jie Leo, Gay Hui Ho, Chia Siong Wong, Kee Seng Chia, Khuan Yew Chow, Minhan Tan, Peter Ang

Open access · goldAbstract readValidation Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 pooled it
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 citations in OpenAlex.

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  18. Gail Model Underestimates Breast Cancer Risk in Thai Population.Asian Pacific journal of cancer prevention : APJCP · 2019
    Article
  19. Off-label use of tamoxifen in a Chinese tertiary care hospital.International journal of clinical pharmacy · 2019
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 1 country.

Wen Yee ChayDepartment of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Drive, Singapore 169610, Republic of Singapore.
Whee Sze Ong
Puay Hoon Tan
Nicholas Qi Jie Leo
Gay Hui Ho
Chia Siong Wong
Kee Seng Chia
Khuan Yew Chow
Minhan Tan
Peter Ang
National Cancer Centre Singapore · SGNational University of Singapore · SGGleneagles Hospital · SGHealth Promotion Board · SGInstitute of Bioengineering and Nanotechnology · SGSingapore General Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Models, BiologicalBreast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, Intraductal, NoninfiltratingCohort StudiesEarly Detection of CancerFemaleHumansIncidenceMammographyMass ScreeningMiddle AgedRiskRisk AssessmentSingapore

Identifiers

PMID22289271
PMCPMC3496137
OpenAlexW1978285026

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.