相关实验视频

Updated: Jun 12, 2025

Modeling Astrocytoma Pathogenesis In Vitro and In Vivo Using Cortical Astrocytes or Neural Stem Cells from Conditional, Genetically Engineered Mice

Modeling Astrocytoma Pathogenesis In Vitro and In Vivo Using Cortical Astrocytes or Neural Stem Cells from Conditional, Genetically Engineered Mice

Published on: August 12, 2014

对膜瘤的条件生存估计揭示了预后的动态性质

Chenjun Sun¹, Zhihao Yang¹, Zhiwei Gu¹

¹Department of Neurosurgery, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, China.

Discover oncology

|September 18, 2024

概括

条件生存 (CS) 分析显示,随着时间的推移,尾瘤 (EPN) 患者的预后得到了改善. 一个新的CS-nomogram模型为EPN提供了个性化,实时的生存预测,增强了患者管理.

科学领域:

神经瘤学神经瘤学
癌症预后癌症预后
医学中的统计建模.

背景情况:

对于长期幸存者来说,传统的膜瘤 (EPN) 生存分析可能缺乏详细的见解.
在EPN患者中,有条件生存 (CS) 模式需要进一步调查以提高预后准确性.

研究的目的:

用CS分析评估EPN患者随着时间的推移而改善的生存率.
开发一种基于CS的名谱,用于动态实时估计EPN患者的存活率.

主要方法:

利用SEER数据库获取EPN患者数据,随机划分为培训 (7:3) 和验证队列.
使用LASSO回归与交叉验证来确定预后预测因子和多变量Cox回归用于CS-nomogram开发.
定义CS为在诊断后"y"年生存的概率,给定"x"年生存的概率.

主要成果:

CS分析显示,随着时间的推移,EPN患者的整体存活率 (OS) 逐渐改善.
10年生存率从74%增加到98%的每一个额外的生存年诊断后 (1-9年).
开发并验证了一种包含7个预测因素 (年龄,等级,位置,延伸,尺寸,手术,放射治疗) 的CS-nomogram.

结论:

CS分析强调了长期EPN幸存者的显著生存改善.
开发的CS-nomogram提供了一个新的工具,用于个性化,实时预测EPN的预后.
患者咨询应强调,个体结果可能与名谱预测有所不同.

关键词:

有条件的生存时间.垂体瘤是什么意思这个名字叫做Nomogram.预测预后预测预测塞尔·塞尔 (Seer) 是一个看得见的人.

更多相关视频

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

相关概念视频

Cancer Survival Analysis

Cancer Survival Analysis

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...

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

Actuarial Approach

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

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...