在瘤学中整合基因组学,放射学和病理学:一个范围审查和人工智能手术手术的框架
Selma Mtoor1, Niki Rashidian2, Nouredin Messaoudi3
1Department of Biomedical, University Duisburg-Essen, 45141 Essen, Germany.
Bioengineering (Basel, Switzerland)
|January 28, 2026
概括
在瘤学中的多式人工智能,整合了基因组学,放射学和病理学,显示出不均的进展. 虽然放射病理学正在进步,但三模式集成和临床部署需要进一步开发未来的外科手术应用.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 生物信息学是一种生物信息学.
背景情况:
- 多模式人工智能在瘤学中的整合正在迅速推进.
- 证据是异质的,并且在基因组学,放射学和病理学模式中分布不均.
- 当前的研究在数据集成和验证方面面临着挑战.
研究的目的:
- 绘制两种或多种omic模式的综合实证研究的地图.
- 总结人工智能集成和验证方法.
- 为了确定未来外科手术开发的研究差距.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 在主要数据库 (PubMed,Ovid,Wiley,Google Scholar) 搜索了2020年1月至2025年3月的研究.
- 绘制研究特征,模式,融合策略,人工智能模型,验证和性能指标.
主要成果:
- 11项研究符合纳入标准,共有1078名参与者.
- 大多数研究集中在放射学-病理学整合上;基因组学整合和三模融合并不常见.
- 临床任务,终点,预处理和验证的显著差异,可比性有限.
结论:
- 在放射病理学和跨度关联建模方面,方法学活动正在增长.
- 三模式管道和临床部署的多模式工作流程尚未开发.
- 外科手术被提议作为一个概念路线图,而不是当前的临床能力,由已识别的研究差距提供信息.
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