一个个性化的,基于风险的方法来积极监测前列腺癌,从更广泛的瘤实践中汲取教训:混合方法审查
Jeroen J Lodder1, Sebastiaan Remmers1, Roderick C N van den Bergh1
1Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands.
Journal of personalized medicine
|March 26, 2025
概括
对前列腺癌 (PCa) 的个性化主动监测 (AS) 使用基于风险的模型来减少低风险患者的随访负担. 这些模型,比如PRIAS和Canary PASS,表现中等,与标准实践相比,可以简化监测.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 前列腺癌 (PCa) 的积极监测 (AS) 正在向个性化,基于风险的方法发展.
- 当前的AS协议往往缺乏个性化,可能导致不必要的监测或错过的进展.
- 在其他癌症 (甲状腺,乳腺,脏,膀) 中从AS中吸取的经验教训可能会为PCa策略提供信息.
研究的目的:
- 审查PCa个性化,基于风险的AS的现状.
- 识别和评估PCa.的现有基于风险的AS工具.
- 探索AS在其他癌症类型中的可转移教训.
主要方法:
- 混合方法审查结合了系统和叙事方法.
- 在多个数据库 (Medline,Embase等) 中进行系统搜索. 坚持PRISMA指南的使用.
- 对甲状腺癌,乳腺癌,脏癌和膀癌中AS的叙述性综述.
主要成果:
- 九项研究描述了四种基于风险的AS工具:PRIAS,约翰霍普金斯,金丝雀通道和STRATCANS.
- 模型主要使用PSA和活检数据;MRI数据有限.
- 性能中等 (AUC/C指数0.580.85),校准良好;一些模型减少了随访负担.
结论:
- 个性化,基于风险的AS模型可以减少低风险PCa患者的随访负担,同时确保高风险个体的适当监测.
- PCa AS比其他固体瘤更先进,但跨癌症学习可以改进选择和监测.
- 基于风险的AS提供了一种更为定制的方法来管理PCa.
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