精密瘤学中的人工智能 (AI) 和机器学习 (ML):通过多学科集成来提高可发现性的审查
Lise Wei1, Dipesh Niraula2, Evan D H Gates3
1Department of Radiation Oncology, University of Michigan, Michigan, United States.
The British journal of radiology
|September 3, 2023
概括
人工智能 (AI) 与多组数据相结合,显示出对彻底改变癌症诊断和治疗的前景. 需要进一步的研究和临床试验,以将这些先进的AI和多组学方法整合到患者护理中.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 医疗成像医学成像
背景情况:
- 精密瘤学越来越多地利用多组学数据 (成像放射学,分子生物标志物) 来改善诊断和治疗.
- 人工智能 (AI),包括机器学习 (ML) 和深度学习 (DL),提供了强大的工具来分析复杂的多态数据集.
研究的目的:
- 审查多组数据类别及其在瘤学中的诊断/治疗作用.
- 为了说明基于人工智能的数据融合和建模方法用于多组学分析.
- 展示在瘤学中的多学科研究的应用和挑战.
主要方法:
- 多组学数据类型的分类及其临床相关性.
- 解释人工智能驱动的数据融合技术 (ML/DL) 用于整合各种omics数据.
- 讨论瘤学中的验证方案和现实世界的临床应用.
主要成果:
- 与人工智能结合的多组数据对癌症亚型,风险分层,预后和临床决策具有重大潜力.
- 人工智能驱动的成像放射学和分子生物标志物的整合可以增强诊断和治疗策略.
- 确定的挑战包括数据异质性,可用性和验证,阻碍临床翻译.
结论:
- 人工智能和多经济学的整合为精密瘤学带来了变革的潜力.
- 数据收集的标准化,先进的计算基础设施和强大的验证对于临床实施至关重要.
- 大规模的前性临床试验对于弥合研究成果与实质性临床益处之间的差距至关重要.
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