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Updated: Feb 5, 2026

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通过大型语言模型探索生物标志物驱动的临床试验的过去和当前景观
Margaret Guo1, Evan Passalacqua2, Erik Bao3
1Bakar Computational Health Sciences Institute, University of California-San Francisco, San Francisco, CA.
JCO clinical cancer informatics
|February 3, 2026
概括
生物标志物驱动的癌症临床试验至关重要,但由于地理限制,往往无法获得. 一个新的工具,NCI临床试验导航器,旨在改善这些重要研究研究的患者匹配.
科学领域:
- 在瘤学瘤学.
- 临床试验 临床试验
- 基因组学就是基因组学.
- 生物技术是生物技术.
背景情况:
- 生物标志物对于癌症临床试验资格和患者获取至关重要.
- 国家癌症研究所临床试验报告计划 (NCI CTRP) 数据库包含广泛的试验信息.
- 大型语言模型 (LLM) 为分析临床试验数据提供了新的应用.
研究的目的:
- 调查癌症临床试验的情况,重点关注生物标志物资格.
- 确定临床试验中止的地理差异和常见原因.
- 开发一种自动化工具,用于将患者与生物标志物驱动的试验相匹配.
主要方法:
- 从癌症.gov通过NCI CTRP API提取了20894项试验.
- 量化了癌症亚型中的生物标志物率,并分析了试验地点的分布.
- 确定了试验失败的原因,并开发了一个基于LLM的患者与试验匹配应用程序.
主要成果:
- 20,894项试验中的5,044项包括生物标志物资格数据,通常聚集在主要学术中心周围.
- 在36种癌症亚型中确定了630种生物标志物,其中许多适用于多种癌症.
- 试验积累困难和赞助问题是停止的主要原因.
结论:
- 生物标志物试验的地理分布限制了患者的访问,并导致试验失败.
- 许多人拥有突变,使他们有资格参加生物标志物驱动的试验.
- 开发了一个自动化工具,NCI临床试验导航器,以增强患者与试验的匹配.
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