Suppr超能文献

盖尔模型在预测约旦女性患乳腺癌风险中的应用。

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

作者信息

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

机构信息

Department of Internal Medicine, Section of Hematology and Medical Oncology, King Hussein Cancer Center, Amman, Jordan.

School of Medicine, University of Jordan, Amman, Jordan.

出版信息

J Oncol. 2020 Feb 20;2020:9608910. doi: 10.1155/2020/9608910. eCollection 2020.

Abstract

UNLABELLED

. 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.

摘要

未标注

乳腺癌一直是约旦女性中最常见的癌症。在实施乳腺癌预防和早期检测项目的过程中,个性化风险评估可提高此类干预措施的成本效益。盖尔模型是一种广泛用于将患者分层到不同风险类别的工具。然而,确实存在对其在不同种族群体中适用性的担忧。在本研究中,我们报告了在约旦女性中应用该模型修改版的经验。

方法

对盖尔风险评估模型(RAM)进行修改,并用于计算乳腺癌的5年和终生风险。已知患有乳腺癌的患者用于测试该模型。利用病历和医院数据库收集有关已知风险因素的信息。接受测试的女性的平均计算风险评分为0.65。这个数字对应于盖尔原始分数1.66,被用作将患者分类为高风险的临界点。

结果

共纳入1786例平均年龄为50岁(范围:19 - 93岁)的乳腺癌患者。盖尔RAM的修改版应用于1213例年龄在35 - 59.9岁的患者。在接下来五年中发生浸润性乳腺癌的平均估计风险为0.54(95%置信区间:0.52,0.56),终生风险为3.42(95%置信区间:3.30,3.53)。只有210名(17.3%)女性的风险评分>0.65,因此被分类为高风险。在这个高风险组中,120名(57.1%)患者有乳腺癌一级家族史。

结论

在一组已确诊乳腺癌的患者中,修改后的盖尔风险评估模型仅能将17%的患者分层为高风险类别。乳腺癌家族史对风险评分的贡献最大。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30aa/7053471/242727dd696d/JO2020-9608910.001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验