以往癌症检查的持久性:丹麦全国的队列研究
Jesper Lykkegaard1, Jonas Kanstrup Olsen1, Sonja Wehberg1
1Research Unit for General Practice, Department of Public Health, University of Southern Denmark, Odense, Denmark.
Scandinavian journal of primary health care
|January 22, 2024
概括
负面的癌症查并不能保证之后的安全时期. 一些查,如乳房影像和CT胸部,显示癌症风险增加后检查,挑战了早期癌症检测的"安全窗口"的想法.
科学领域:
- 在瘤学瘤学.
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 先前癌症查结果为负的患者仍然可能患上癌症.
- 一般诊所的接触往往涉及患有癌症的新症状或持续症状的患者.
- 调查一个潜在的潜力.
- 安全时期的安全时期.
- 在负面查后对患者管理至关重要.
研究的目的:
- 探索肺癌,胃肠道癌,结肠直肠癌,膀癌和乳腺癌的负面癌症查后是否存在安全期.
- 在特定诊断程序的负面结果后评估癌症风险.
- 为了向临床实践提供有关癌症查负面结果的解释的信息.
主要方法:
- 在丹麦进行了基于注册的事件时间分析.
- 3,300万年龄在30-85岁之间的公民根据他们上次检查以来的时间进行了分类.
- 根据年龄和性别调整的风险比率是使用1年的随访来计算的,将被检查的与未被检查的个人进行比较.
主要成果:
- 阴性查结果很常见,即使在后来被诊断为癌症的患者中也是如此.
- 阴性结肠镜检查与长达10年的结肠直肠癌风险减半有关.
- 相反,阴性乳房扫描和CT胸部结果与在1-2年内出现相关癌症的风险显著增加有关.
结论:
- 没有确定任何癌症风险足够低的检查后期,以最终排除癌症.
- 这些发现挑战了可靠的可靠的概念.
- 安全窗口安全窗口
- 在对所研究的癌症进行负面查后.
- 临床警是必要的,即使在负面的诊断程序后.
相关概念视频
01:21Cancer Survival Analysis
353
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...
353
02:59Cancer Prevention
6.2K
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Some...
6.2K
01:20Comparing the Survival Analysis of Two or More Groups
195
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...
195