Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases

Cancer Innovation Pub Date : 2024-10-25 DOI:10.1002/cai2.152
Yongxin Li, Xinlong Tao, Yinyin Ye, Yuyao Tang, Zhengbo Xu, Yaming Tian, Zhen Liu, Jiuda Zhao
{"title":"Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases","authors":"Yongxin Li,&nbsp;Xinlong Tao,&nbsp;Yinyin Ye,&nbsp;Yuyao Tang,&nbsp;Zhengbo Xu,&nbsp;Yaming Tian,&nbsp;Zhen Liu,&nbsp;Jiuda Zhao","doi":"10.1002/cai2.152","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (<i>n</i> = 15,227) and internal validation cohort (<i>n</i> = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (<i>n</i> = 90).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.</p>\n </section>\n </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11503687/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Innovation","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cai2.152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients.

Methods

Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90).

Results

We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group.

Conclusions

Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Abstract Image

年轻乳腺癌的预后提名图:基于 SEER 和 METABRIC 数据库的回顾性研究。
背景:年轻乳腺癌(YBC)是乳腺癌的一个亚型,通常更具侵袭性,但对其预后的了解较少。在这项研究中,我们旨在生成预测 YBC 患者总生存期(OS)和乳腺癌特异性生存期(BCSS)的提名图:方法:我们从监测、流行病学和最终结果(SEER)数据库中获取了2010年至2020年间被诊断为YBC的女性数据。患者按7:3的比例随机分配到训练队列(n=15227)和内部验证队列(n=6526)中。通过Cox回归模型,确定了重要的预后因素,并利用这些因素构建了3年、5年和10年的OS和BCSS提名图。来自国际乳腺癌分子分类联盟(METABRIC)数据库的数据被用作外部验证队列(n = 90):结果:我们构建了包含10个OS和BCSS预后因素的提名图。在训练队列中,这些提名图对OS和BCSS的预测准确性很高,C指数分别为0.806和0.813。校准曲线验证了提名图具有良好的预测准确性。决策曲线分析证明了它们在预测 YBC 患者生存率方面的实用临床价值。此外,我们还提供了动态提名图,以提高结果的可操作性。风险分层能力评估也显示,低风险组的OS和BCSS率明显优于高风险组:在此,我们生成并验证了比之前为 YBC 开发的模型更全面、更准确的 OS 和 BCSS 直方图。这些提名图可以帮助临床医生评估患者的预后并做出临床决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
0.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信