What can be learnt from models of incidence rates?

从发病率模型中可以学到什么?

阅读:1

Abstract

Models of breast cancer incidence have evolved from the observation by Armitage and Doll in the 1950s that the pattern of incidence by age differs for reproductive cancers from those of other major malignancies. Both two-stage and multistage models have been applied to breast cancer incidence. Consistent across modeling approaches, risk accumulation or the rate of increase in breast cancer incidence is most rapid from menarche to first birth. Models that account for the change in risk after menopause and the temporal sequence of reproductive events summarize risk efficiently and give added insights to potentially important mechanistic features. First pregnancy has an adverse impact on progesterone receptor negative tumors, while increasing parity reduces the risk of estrogen/progesterone receptor positive tumors but not estrogen/progesterone receptor negative tumors. Integrated prediction models that incorporate prediction of carrier status for highly penetrant genes and also account for lifestyle factors, mammographic density, and endogenous hormone levels remain to be efficiently implemented. Models that both inform and reflect the emerging understanding of the molecular and cell biology of carcinogenesis are still a long way off.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。