阴茎癌患者的整体存活率与基于人口的对照组之间的差异
Lukas Scheipner1,2, Stefano Tappero1,3,4, Mattia Luca Piccinelli1,5,6
1Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada.
概括
与年龄相匹配的男性相比,阴茎状细胞癌 (SCCP) 患者的5年整体存活率 (OS) 显著降低. 这种生存差距随着癌症的晚期扩大,突显了SCCP对患者结果的关键影响.
科学领域:
- 在瘤学瘤学.
- 流行病学 流行病学
- 生存分析的分析.
背景情况:
- 阴茎状细胞癌 (SCCP) 是一种罕见的恶性瘤.
- 评估SCCP患者的生存结果对于了解疾病影响至关重要.
- 与基于人口的数据进行比较,为生存率提供了必要的背景.
研究的目的:
- 为了确定阴茎癌患者的5年总生存期 (OS) 是否与年龄相匹配的男性人口对照不同.
- 分析SCCP各个阶段的生存差异.
主要方法:
- 使用了监测流行病学和最终结果 (SEER) 数据库 (2004-2018).
- 在2004年至2013年期间诊断的确定的SCCP患者.
- 使用蒙特卡洛模拟与社会保障局生命表进行年龄匹配的控制.
- 根据阶段,比较OS和分析的癌症特异性死亡率 (CSM) 与其他原因死亡率 (OCM).
主要成果:
- 总共有2282名SCCP患者被分析,分期分布:第一阶段 (43%),第二阶段 (36%),第三阶段 (13%),第四阶段 (8%).
- 对SCCP患者的五年生存期明显落后于对照组:第一阶段 (63%vs80%),第二阶段 (50%vs80%),第三阶段 (39%vs84%),第四阶段 (26%vs87%).
- CSM占死亡人数的很大一部分,从第一阶段的12%到五年后的第四阶段的60%.
结论:
- 与一般男性人口相比,SCCP患者在所有疾病阶段的5年总生存率较差.
- SCCP患者和对照人群之间的生存差异随着癌症阶段的增加而增加,从17%到61%不等.
- 癌症特异性死亡率是SCCP患者整体死亡率的重要因素,特别是在晚期.
相关概念视频
01:21Cancer Survival Analysis
356
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...
356
01:20Comparing the Survival Analysis of Two or More Groups
197
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...
197
01:24Kaplan-Meier Approach
152
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,...
152
01:20Actuarial Approach
79
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,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
79
01:15Assumptions of Survival Analysis
134
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
134