基于PET的成像中人工智能方法的性能,用于在淋巴瘤中预测结果:系统性审查和元分析
Mohammad Mehdi Mehrabi Nejad1, Mohammad Reza Ghanbari Boroujeni2, Alireza Hayati3
1Department of Radiology, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
European journal of radiology
|June 4, 2025
概括
人工智能 (AI) 方法,特别是深度学习 (DL),使用PET扫描有效预测淋巴瘤结果. 这些人工智能工具对个性化癌症治疗策略有前途.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 基于正子发射断层扫描 (PET) 的成像在淋巴瘤管理中至关重要.
- 预测患者的结果和治疗反应仍然是一个挑战.
研究的目的:
- 系统地审查和元分析人工智能 (AI) 方法的预测性能,使用治疗前PET成像来确定淋巴瘤的结果.
- 评估不同的AI方法的有效性,包括深度学习 (DL) 和机器学习 (ML),在预测无进展生存 (PFS),总生存 (OS) 和治疗反应方面.
主要方法:
- 在PubMed-MEDLINE,Scopus和Web of Science的系统文献搜索到2024年10月.
- 包括75项专注于使用基于PET成像的淋巴瘤AI预测模型的原始研究.
- 进行元分析,子组分析和元回归,以组合预测性绩效指标 (HR,AUC,C指数,OR).
主要成果:
- 人工智能方法显示PFS (AUC:0.78,C指数:0.79) 和OS (AUC:0.75,C指数:0.79) 的强有力的预测性能.
- 人工智能还显示出对治疗反应的显著预测能力 (OR:5.36,AUC:0.85).
- 深度学习 (DL) 方法与其他人工智能方法相比,在PFS和治疗响应预测方面表现优越.
结论:
- 人工智能方法,特别是DL,基于治疗前的PET成像显示出对淋巴瘤结果的强大预测能力.
- 这些发现支持AI在促进淋巴瘤患者精准医学方面的潜力.
- 建议进行进一步的前性研究,以促进这些AI工具的临床整合.
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