在丹麦的癌症儿童中复发和复发后的存活率:2001-2021年
Mie Mølgaard Andersen1,2, Marie Christine Lundius Sørensen1,2, Kjeld Schmiegelow1,3
1Department of Paediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark.
Pediatric blood & cancer
|October 22, 2024
概括
随着更好的治疗,儿童癌症的生存率有所改善. 丹麦的血液和固体瘤复发率下降,复发后生存率增加,但中枢神经系统瘤没有变化.
科学领域:
- 儿科瘤学 儿科瘤学
- 癌症流行病学 癌症流行病学
- 临床研究 临床研究
背景情况:
- 儿科癌症治疗和支持性护理的进步提高了生存率.
- 调查复发和复发后生存的时间趋势对于了解治疗疗效至关重要.
研究的目的:
- 分析丹麦癌症儿童复发发病率和复发后生存率的最近变化.
- 为了确定血液癌症,固体瘤和中枢神经系统 (CNS) 瘤的趋势.
主要方法:
- 一项基于登记的研究,对2890名在15岁之前被诊断患有癌症的儿童进行了研究 (2001-2021年).
- 使用阿伦-约翰森和卡普兰-梅尔估计器来评估复发的累积发病率和复发后的生存概率.
- 包括血液癌症,固体瘤和特定时间框架的中枢神经系统瘤.
主要成果:
- 在2001-2010年和2011-2021年期间,血液癌 (14%至11%) 和固体瘤 (21%至18%) 的5年累计复发发病率下降.
- 复发后的5年生存率改善了血液癌症 (44%至61%) 和固体瘤 (38%至46%).
- 恶性中枢神经系统瘤在2010-2015年至2016-2021年期间显示稳定的复发发病率 (49%) 和存活率 (20%).
结论:
- 在丹麦,儿童血液癌症和固体瘤的复发发病率降低和复发后存活率增加的改善观察到.
- 对于复发的儿童来说,仍然存在持续的生存差距,这凸显了加强和向治疗的必要性.
- 对于高风险的儿科癌症患者,特别是中枢神经系统瘤患者,需要进一步的研究和治疗进展.
相关概念视频
Cancer Survival Analysis
328
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...
328
Introduction To Survival Analysis
184
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
184
Comparing the Survival Analysis of Two or More Groups
155
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...
155
Actuarial Approach
63
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,...
63
Treatment Resistant Cancers
3.3K
Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.3K
Assumptions of Survival Analysis
97
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.
97


