相关实验视频

Updated: Jul 4, 2025

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

诺米图预测三阴性乳腺癌的老年妇女的生存率:一个基于SEER人口的SEER研究

Ruigang Feng^1,2,3,4,5, Wenwen Huang^1,2,3,4,6, Bowen Liu^1,2,3,4

¹The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.

Technology and health care : official journal of the European Society for Engineering and Medicine

|February 2, 2024

概括

这项研究开发了一种名谱,用于预测三阴性乳腺癌 (TNBC) 患者的生存率. 该模型准确预测整体存活率和乳腺癌特异性存活率,性能优于TNM分期系统.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
癌症流行病学癌症流行病学

背景情况:

对老年乳腺癌患者的有效治疗仍然是一个挑战.
三重阴性乳腺癌 (TNBC) 存在独特的治疗困难.
准确的预后模型对于指导TNBC患者管理至关重要.

研究的目的:

开发一种名谱来预测三阴性乳腺癌 (TNBC) 患者的整体生存期 (OS).
为TNBC建立一个可靠的生存风险预测模型.
确定影响TNBC患者结果的关键预后因素.

主要方法:

利用来自SEER数据库 (2010-2015) 的5317名TNBC患者 (ER-, PR-, HER2-) 的数据.
采用单变量和多变量考克斯回归分析来确定预后因素.
使用培训和验证队列构建并验证了一个名录,通过C指数,校准曲线,AUC和DCA评估性能.

主要成果:

在训练组中,Nomograms表现出强大的预测性能,C指数为0.797 (OS) 和0.825 (BCSS),在验证组中为0.795 (OS) 和0.818 (BCSS).
与TNM分期系统相比,该名图显示出更高的灵敏度.
决策曲线分析证实了名图的临床实用性和适用性.

结论:

该研究为TNBC患者的临床特征和预后因素提供了宝贵的基于人口的见解.
一个可靠和准确的TNBC预后野图已经成功构建.
这个名图可以帮助临床医生预测生存结果,并优化TNBC的治疗策略.

关键词:

在 SEER 数据库中,我们可以看到三重阴性乳腺癌是什么?乳腺癌特异性生存率这个名字是名ogramogram.总体存活率总体存活率

更多相关视频

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

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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...

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...

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,...