人工智能模型在低度质瘤的手术规划中:系统审查
Vivek Sanker1, Aparna Venkatesan2, Afia Salma3
1Department of Neurosurgery, Stanford University, Palo Alto, CA, United States.
Frontiers in oncology
|February 2, 2026
概括
人工智能 (AI) 和机器学习 (ML) 模型在低度质瘤 (LGG) 的手术规划方面显示出前景. 需要对更大的数据集进行进一步验证,以提高质瘤切除的预测准确度.
科学领域:
- 神经外科 神经外科
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能技术,包括深度学习 (DL) 和卷积神经网络 (CNN),有助于自动提取临床数据,用于质瘤监测和手术规划.
- 低度质瘤 (LGG) 在手术规划和治疗后监测方面存在独特的挑战.
研究的目的:
- 系统地审查和分析AI/ML模型在LGG手术规划中的作用.
- 确定最有效的AI技术用于LGG瘤细分和生存预测.
主要方法:
- 在主要数据库 (PubMed,Scopus,Web of Science,ArXiV,Embase) 进行了全面的系统文献搜索,截至2025年7月14日.
- 包括的研究重点是ML模型在LGG手术规划中的实用性,包括手术前和手术内应用.
主要成果:
- 审查了8项研究,评估了AI在瘤细分,神经导航,超光谱成像和手术建议中的应用.
- 深度医学CNN在瘤细分方面实现了最高的DICE系数. 在超光谱成像分析中,DL方法在超光谱成像分析中大约超过了经典方法的10%.
- 像BSL,BITES和DeepSurv这样的AI模型在受限制的平均生存时间中显示出显著的差异,这表明了预测价值.
结论:
- 人工智能/多发性肌痛模型在质瘤切除诊断和管理方面显示出有希望的结果.
- 目前的发现是初步的,基于一个小队列 (n=8).
- 与更大的患者群体进行外部验证对于提高这些AI模型的预测能力至关重要.
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