对B型肝炎病毒相关的肝细胞癌患者的生存分析和预后诺基图的开发
Jianjun Wang1, Kexin Wang2, Chun Chen3
1Senior Department of Hepatology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China.
Heliyon
|October 23, 2023
概括
这项研究确定了B型肝炎病毒相关肝细胞癌 (HBV-HCC) 患者整体生存的关键风险因素. 开发了一个名谱来预测存活率,帮助HBV-HCC的个性化治疗策略.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 在瘤学瘤学.
- 流行病学 流行病学
背景情况:
- 乙型肝炎病毒 (HBV) 感染是全球肝细胞癌 (HCC) 的主要驱动因素,特别是在中国.
- 确定预后因素对于管理HBV相关的HCC和改善患者的治疗结果至关重要.
研究的目的:
- 确定影响HBV相关HCC患者整体存活时间的独立风险因素.
- 开发和验证用于估计这些患者的生存概率的预测性诺莫格拉姆模型.
主要方法:
- 一项回顾性队列研究包括了1723名在2009年至2018年期间被诊断患有HBV相关的HCC的患者.
- 使用多变量COX比例危险回归分析来确定独立的风险因素.
- 根据已识别的风险因素构建了一个预后名录图.
主要成果:
- 对HCC存活的显著风险因素包括年龄 (≥60岁),男性性别,低白蛋白 (ALB <35 g/L),高AST (≥80 U/L),特定的AFP水平 (20-400 ng/mL和≥400 ng/mL),晚期肝硬度测量 (LSM 9-22 kPa和≥22 kPa),以及晚期BCLC阶段 (B/C和D).
- 开发的名图表显示出良好的预测准确性,一致性指数为0.842.
- 校准曲线表明可靠的长期生存率预测.
结论:
- 在HBV相关的HCC患者中确定了整体存活期的独立风险因素.
- 一个经过验证的诺莫格拉姆模型为个人生存预测和在HBV-HCC管理中的临床决策提供了有价值的工具.
更多相关视频
12:24A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
5.2K
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
296
相关概念视频
Cancer Survival Analysis
357
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...
357
Comparing the Survival Analysis of Two or More Groups
201
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...
201
Kaplan-Meier Approach
154
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,...
154
