乳腺密度和乳腺癌风险:掩盖和检测偏差
Jane M Lange1, Charlotte C Gard2, Ellen S O'Meara3
1Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, United States.
American journal of epidemiology
|August 4, 2024
概括
乳腺密度较高的女性患乳腺癌 (BC) 的风险明显更高,而不仅仅是诊断. 这一发现支持告知乳腺密度较高的女性,她们的BC风险增加.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 流行病学 流行病学
背景情况:
- 乳腺密度是影响乳腺癌 (BC) 诊断和查有效性的已知因素.
- 乳腺密度与实际BC风险之间的关系,除了诊断之外,需要进一步调查,因为乳房镜敏感度的变化.
研究的目的:
- 研究乳腺密度与乳腺癌发病的实际风险之间的关联.
- 为了区分BC诊断的风险和BC发病的风险,与乳腺密度相关.
主要方法:
- 利用了33 542名乳腺癌监测联盟 (2000 - 2018) 妇女的数据.
- 据估计,乳房密 (BI-RADS C/D) 和不密 (BI-RADS A/B) 乳房的乳房显微镜敏感度和查性乳房显微镜率.
- 使用卡普兰-梅尔估计的BC诊断的相对风险 (RRdx) 和BC发病的相对风险 (RRonset) 的自然史模型.
主要成果:
- 密集与非密集的乳房的BC诊断的相对风险 (RRdx) 为1.80.
- 考虑到密度特定的乳房镜敏感度,估计的BC发病 (RRonset) 的相对风险为1.73.
- 敏感性分析证实了乳腺密度较高与BC发病风险增加之间的强有力的关联,与诊断风险相比.
结论:
- 乳腺密度较高与乳腺癌发病风险增加密切相关.
- 这些发现支持强制通知乳腺密度较高的女性,以提高她们的BC风险的政策.
相关概念视频
Bias in Epidemiological Studies
190
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
190
Strategies for Assessing and Addressing Confounding
87
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
87
Bias
4.1K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.1K


