通过SEER数据库分析,评估用于预测胃胃肠道 stromal 瘤生存结果的 nomogram 模型
Liuliang Yong1,2, Lanjun Li3, Jun Wu3
1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China.
Scientific reports
|May 20, 2024
概括
一个新的Cox (双阶段) 诺米图准确地预测胃肠道 stromal 瘤 (GISTs) 的存活率. 这种简单,稳定的模型在整体和癌症特异性生存方面表现优于其他模型,具有临床前景.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 胃肠道 stromal 瘤 (GIST) 是胃肠道中介细胞瘤最常见的一种,主要发生在胃中.
- 准确的预后模型对于指导胃GIST患者的治疗决策至关重要.
- 现有的诺莫格拉姆比较方法缺乏对分类数据的定制和胃GISTs中的模型选择.
研究的目的:
- 开发和验证一种优质的诺米图,用于预测胃GIST患者的整体存活率 (OS) 和癌症特异性存活率 (CSS).
- 为了比较五种不同的考克斯名录模型的性能,这些模型包含了各种类别变量选择和建模技术.
- 解决处理分类数据和选择胃GIST预后模型的挑战.
主要方法:
- 利用了SEER Research Plus数据库 (2000-2020年) 对5463名胃GIST患者的数据.
- 为分类变量开发了一种先进的归算算法.
- 使用50个随机的7:3列车测试分割,构建并比较了五个Cox名图模型 (Cox二阶段,Lasso-Cox,Ridge-Cox,弹性网-Cox,Cox With Lasso).
主要成果:
- 考克斯 (双阶段) 命名图模型确定了五种模型中最少的特征.
- 考克斯 (双阶段) 展示了卓越的性能,在短期,中期和长期间隔中,在OS和CSS任务中实现了最高的曲线下面面积 (AUC),顶级-1和顶级-3比率.
- 该模型在多个验证分割中表现出稳定性和准确性.
结论:
- 考克斯 (双阶段) 诺图是胃GIST存活的简单,稳定和准确的预测工具.
- 这种模型显示出临床应用在分层患者风险和指导治疗方面的重大前景.
- 为了提高全球临床医生和研究人员的可访问性,开发了名录的在线版本.
相关概念视频
01:21Cancer Survival Analysis
343
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
343
01:20Comparing the Survival Analysis of Two or More Groups
177
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
177
01:24Kaplan-Meier Approach
132
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
132