APOLLO11:一种基于生物数据的模型,用于肺癌的临床和转化研究
Arsela Prelaj1, Leonardo Provenzano2,3, Vanja Miskovic2,3
1Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. arsela.prelaj@istitutotumori.mi.it.
NPJ precision oncology
|January 29, 2026
概括
确定肺癌的预测生物标志物至关重要. APOLLO11联盟正在创建一个全国性的数据网络和生物库,以实现人工智能驱动的精确瘤学研究.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 识别预测性和耐药性生物标志物是临床癌症研究中至关重要的未满足需求.
- 人工智能 (AI) 为开发个性化的预测算法提供了一种强大的方法.
- 目前的研究缺乏一个强大的基础设施,用于大规模的,联合的,多组的,在肺癌中收集真实世界的数据.
研究的目的:
- 建立一个全国性的,分散的网络和数据库,用于肺癌研究.
- 创建一个多层次的生物库来存储和分析现实世界的临床和生物数据.
- 通过数据驱动的研究为人工智能驱动的精密瘤学奠定基础.
主要方法:
- 形成APOLLO11联盟,一个分布式的意大利肺癌网络.
- 开发一个分散的,长期的,以人口为基础的,真实世界的数据库.
- 建立一个具有本地存储和中央注释的多层次生物库.
主要成果:
- 创建一个全国性的网络,用于未来的数据收集和分析.
- 建立一个分散的数据存储库和生物银行基础设施.
- 奠定了肺癌研究数据驱动范式的基础.
结论:
- APOLLO11联盟解决了对肺癌数据的标准化基础设施的需求.
- 这一举措将促进开发可靠的AI模型,用于精确瘤学.
- 该项目旨在通过数据驱动的方法推进个性化癌症护理.
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