Gail model utilization in predicting breast cancer risk in Egyptian women: a cross-sectional study

Breast Cancer Res Treat. 2021 Aug;188(3):749-758. doi: 10.1007/s10549-021-06200-z. Epub 2021 Apr 14.

Abstract

Purpose: Herein, our purpose was to calculate the 5-year and lifetime risk of breast cancer and to assess new breast cancer potential contributors among Egyptian women utilizing the modified Gail model, while presenting a global comparison of risk assessment.

Methods: This study included 7009 women from both urban and rural areas scattered across 40% of the Egyptian provinces. The 5-year risk categories were defined as low risk (≤ 1.66%) or high risk (> 1.66%), whereas the lifetime risk categories were defined as low risk (≤ 20%) or high risk (> 20%). Pearson's Chi-squared test was performed to determine the association between participants' characteristics and distinct risk categories. Binary logistic regression was carried out for correlation analysis.

Results: The mean estimated risk for developing invasive breast cancer over 5 years was 0.86 (± 0.67), whereas the mean lifetime breast cancer risk score was 11.26 (± 5.7). Accordingly, only 614 (8.75%) and 470 (6.7%) women were categorized as individuals with high risk of breast cancer incidence in 5-year and lifetime, respectively. Only 192 participants (2.7%) conferred both high 5-year and high lifetime risk scores. Marital status, method of feeding, physical activity behavior, contraceptive use, menopause and number of children were found to have a statistically significant association with both 5-year and lifetime breast cancer risk categories.

Conclusion: We revealed that modified Gail model had a well-fitting and discrimination accuracy in Egyptian women when compared with other countries.

Keywords: Breast cancer; Egypt; Gail Model; Risk assessment.

MeSH terms

  • Breast
  • Breast Neoplasms* / epidemiology
  • Child
  • Cross-Sectional Studies
  • Egypt / epidemiology
  • Female
  • Humans
  • Models, Statistical
  • Risk Assessment
  • Risk Factors