开发和验证用于预测早期发生子宫内膜癌患者的整体存活率的诺姆图
Meng Zhang1, Ruiping Li1, Jiaxi Zhang1
1Department of Gynecology, Second Hospital of Lanzhou University, No.82 Cui Ying Gate, Cheng guan District, Lanzhou, Gansu, 730030, China.
BMC cancer
|December 15, 2023
概括
这项研究开发了一种名谱,用于预测早期发生子宫内膜癌 (EC) 患者的整体存活率. 该工具有助于评估年轻EC患者的预后和指导临床治疗.
科学领域:
- 妇科瘤学 妇科瘤学
- 癌症预后 癌症预后
- 流行病学 流行病学
背景情况:
- 子宫内膜癌 (EC) 在年轻和年长的患者群体之间呈现出明显的临床病理差异.
- 了解这些差异对于定制治疗策略和预后评估至关重要.
研究的目的:
- 为了研究年轻人与老年EC患者的临床病理变异.
- 开发和验证一个预测性名谱,用于早期发病的EC的整体存活率.
主要方法:
- 利用监测,流行病学和最终结果 (SEER) 数据库 (2004-2015) 来获取患者数据.
- 采用考克斯回归来确定年轻EC患者整体存活期的独立预后因素.
- 使用内部和外部数据集构建和验证了一个名录,通过C指数,决策曲线,ROC和校准分析评估预测准确性.
主要成果:
- 包括1042名年轻和12991名老年EC患者.
- 确定年龄,瘤大小,等级,FIGO阶段和手术作为整体生存的独立预测因素.
- 诺米图表显示出强大的预测性能,C指数为0.832 (训练) 和0.839 (内部验证).
结论:
- 成功开发了一种经过验证的nomogram,用于预测早期发病的子宫内膜癌的整体存活率.
- 这种基于SEER数据的工具可以帮助临床医生评估年轻EC患者的预后并为治疗决策提供信息.
相关概念视频
Cancer Survival Analysis
355
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...
355
Kaplan-Meier Approach
150
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,...
150


