The Application of Gail Model to Predict the Risk of Developing Breast Cancer among Jordanian Women

Hikmat Abdel-Razeq, Luna Zaru, Ahmed Badheeb, Shadi Hijjawi

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2 Scopus citations

Abstract

Background and Objectives. Breast cancer has been the most common cancer affecting women in Jordan. In the process of implementing breast cancer prevention and early detection programs, individualized risk assessment can add to the cost-effectiveness of such interventions. Gail model is a widely used tool to stratify patients into different risk categories. However, concerns about its applicability across different ethnic groups do exist. In this study, we report our experience with the application of a modified version of this model among Jordanian women. Methods. The Gail risk assessment model (RAM) was modified and used to calculate the 5-year and lifetime risk for breast cancer. Patients with known breast cancer were used to test this model. Medical records and hospital database were utilized to collect information on known risk factors. The mean calculated risk score for women tested was 0.65. This number, which corresponds to the Gail original score of 1.66, was used as a cutoff point to categorize patients as high risk. Results. A total of 1786 breast cancer patients with a mean age of 50 (range: 19-93) years were included. The modified version of the Gail RAM was applied on 1213 patients aged 35-59.9 years. The mean estimated risk for developing invasive breast cancer over the following five years was 0.54 (95% CI: 0.52, 0.56), and the lifetime risk was 3.42 (95% CI: 3.30, 3.53). Only 210 (17.3%) women had a risk score >0.65 and thus categorized as high risk. First-degree family history of breast cancer was identified among 120 (57.1%) patients in this high-risk group. Conclusions. Among a group of patients with an established diagnosis of breast cancer, a modified Gail risk assessment model would have been able to stratify only 17% into the high-risk category. The family history of breast cancer contributed the most to the risk score.

Original languageEnglish (US)
Article number9608910
JournalJournal of Oncology
Volume2020
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Oncology

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