人工智能和多奥米克在乳腺癌中的融合:彻底改变了诊断,预测和精确瘤学
Bitao Jiang1, Yuefei Wu2, Xiao Chen3
1Department of Hematology and Oncology, Beilun Branch of the First Affiliated Hospital, College of Medicine, Zhejiang University, Ningbo, China; Department of Hematology and Oncology, Beilun People's Hospital, Ningbo, China.
Critical reviews in oncology/hematology
|January 30, 2026
概括
使用人工智能 (AI) 进行多主题数据整合,提炼乳腺癌 (BC) 亚型并识别生物标志物. 临床翻译方面的挑战是通过拟议的人工智能驱动的多式联络整合策略来解决的,以改善患者管理.
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:
- 乳腺癌 (BC) 是一种异构的恶性瘤,导致显著的死亡率.
- 多omics分析提升了瘤生物学理解,但面临着整合挑战.
- 将复杂的omics数据转化为临床决策支持仍然很困难.
研究的目的:
- 审查多omics研究如何完善BC亚型和生物标志物发现.
- 综合人工智能和深度学习在BC多式联网数据集成方面的进展.
- 概述障碍,并提出AI-multi-omics临床翻译的解决方案.
主要方法:
- 对乳腺癌的多项omics研究的审查.
- 人工智能和深度学习方法的综合用于多式联网数据集成.
- 对集成框架和深度学习架构进行批判性检查.
主要成果:
- 多omics分析提炼BC亚型,并有助于生物标志物/目标的发现.
- 人工智能,特别是深度学习,将多种omics与成像,病理学和临床数据相结合.
- 多模式人工智能模型显示出比单模式方法更好的患者级预测.
结论:
- 临床翻译AI-multi-omics面临诸如异质性和缺乏验证等障碍.
- 标准化预处理,缺失意识融合和可解释的人工智能至关重要.
- 建议在BC管理中可靠地部署AI-multi-omics的临床路线图.
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