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人工智能和机器学习预测跨动脉化学栓塞结果:系统性审查

Elina En Li Cho1, Michelle Law2, Zhenning Yu2

  • 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

Digestive diseases and sciences
|December 21, 2024
PubMed
概括

在肝细胞癌 (HCC) 中预测跨动脉化学血栓化 (TACE) 反应是具有挑战性的. 人工智能和放射学模型在预测HCC患者的TACE结果方面表现有希望.

关键词:
人工智能的人工智能是人工智能.肝细胞癌是肝细胞癌.中间阶段 中间阶段通过动脉的化学栓塞.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 超动脉化血栓化 (TACE) 是中期肝细胞癌 (HCC) 的标准治疗方法.
  • 在HCC患者中预测TACE治疗反应仍然是一个重大的临床挑战.

研究的目的:

  • 系统地审查放射学和人工智能模型的性能和有效性,以预测HCC的TACE结果.
  • 评估这些预测模型的诊断准确性.

主要方法:

  • 在Medline和Embase数据库进行系统的文献搜索,截至2024年4月7日.
  • 包括开发TACE反应预测模型的研究,并使用AUC,特异性或灵敏度评估性能.
  • 不包括综述,病例系列,儿科和动物研究.

主要成果:

  • 包括64篇涉及13,412名患者的文章.
  • 使用治疗前CT和MRI扫描的AI模型在预测TACE疗效方面具有价值.
  • 基于放射学模型和组合模型 (成像+非成像特征) 显示出卓越的性能.

结论:

  • 结合了整合临床,实验室和放射学特征的综合预测模型,显示了在HCC中准确预测TACE反应的潜力.
  • 人工智能和放射学为提高HCC患者的治疗个性化提供了有希望的工具.