数字生物标志物用于精确的肺癌早期检测:将人工智能驱动的多态系统集成到临床途径中
Fan Bu1,2, Zhi-Qiang Ling1,2
1Zhejiang Cancer Institute, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
Cancer medicine
|February 6, 2026
概括
将人工智能 (AI) 与多omics数据集成加快了早期肺癌检测. 这种方法有助于识别用于风险分层和个性化查的生物标志物,尽管临床翻译面临挑战.
科学领域:
- 生物标志物发现发现
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 肺癌是全球癌症死亡的主要原因之一.
- 早期检测对于改善患者的治疗结果至关重要.
- 高危人群需要有效的查和管理策略.
研究的目的:
- 通过多个omics层审查肺癌的早期诊断生物标志物.
- 总结人工智能 (AI) 在生物标志物发现的多omics数据分析中的作用.
- 评估将这些方法整合到患者护理中的临床实用性和挑战.
主要方法:
- 系统地评估多omics数据 (基因组学,表观基因组学,转录组学,蛋白质组学,代谢组学,微生物组学).
- 审查人工智能应用,包括机器学习和深度学习,用于数据集成和分析.
- 评估临床决策支持系统 (CDSS) 和选算法.
主要成果:
- 多omics策略正在识别用于早期肺癌检测的分子签名.
- 人工智能有助于风险分层,诊断精细化和治疗规划.
- 临床实用性得到了强调,但数据异质性和可解释性等障碍仍然存在.
结论:
- 单细胞和空间多组学,与联合学习,为临床实施提供解决方案.
- 人工智能和多omics集成可以推进个性化,风险适应的早期肺癌检测.
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